Features: Add tag support, improve error handling, MCP improvements

- Add tag_name parameter to deployment flow
- Improve population retry logic with numbered options
- Add MCP package to requirements
- Update CLAUDE.md with dual method documentation
- File organization: move docs to dev_notes/, create scratch/ folder
- Add .cursorrules for file organization
- Add data adjuster modules for runtime data modification

.cursorrules

@@ -0,0 +1,214 @@
+# Cursor Rules for Demo Wire Project
+
+## CRITICAL PROCESS MANAGEMENT RULE
+
+**NEVER restart the application unless explicitly requested**
+
+- DO NOT run commands like `kill`, `lsof -ti:7862 | xargs kill`, or restart scripts
+- DO NOT execute `launch_chat.py`, `demo_prep.py` or similar startup commands
+- When making code changes, say "Changes saved to [file] - restart when ready" and STOP
+- ONLY restart if user explicitly says: "restart", "rerun the app", "relaunch", "kill and restart"
+- If user says "don't stop the process" or "keep it running" - NEVER restart, no matter what
+
+## Contact & Communication
+
+- User (boone) is a ThoughtSpot Architect with 20+ years experience
+- You can challenge ideas, but respect deep domain knowledge
+- When asked to STOP - stop immediately and wait for further instructions
+- Start discussions with back-and-forth using numbered questions
+- TS = ThoughtSpot (the company where boone works)
+- Ask rather than assume when in doubt
+
+## Debugging and Problem-Solving Protocol
+
+### Confidence and Communication
+
+1. **Don't act over-confident unless you're extremely sure**
+   - Check documentation before claiming you know how something works
+   - Say "I don't know" or "Let me check" instead of guessing
+   - If you're uncertain, say so upfront
+
+2. **NEVER claim something is fixed until it's tested**
+   - ❌ WRONG: "I've fixed the tags issue" (without testing)
+   - ✅ RIGHT: "I've updated the code - let's test if tags work now"
+   - Show the test results, don't just assume it works
+
+3. **When debugging:**
+   - Check documentation FIRST before blaming external systems
+   - State what you're checking and why
+   - Share what you found (even if it proves you wrong)
+
+4. **It's OK to say:**
+   - "I don't know - should I research this?"
+   - "I'm not certain, but here are 2 possibilities..."
+   - "Let me verify this works before saying it's fixed"
+
+## File Organization - CRITICAL
+
+### NEVER create files in root without asking first
+
+**tests/** - Real, reusable test cases only
+- Unit tests for core functions
+- Integration tests that could be automated
+- Tests you'd run in CI/CD
+
+**scratch/** - ALL temporary/experimental/debug files
+- ALL experimental/debug/check/verify/analyze scripts
+- One-off fixes (fix_*.py, adjust_*.py, emergency_*.py)
+- Debug scripts (debug_*.py, check_*.py, verify_*.py)
+- Analysis tools (analyze_*.py, get_*.py, show_*.py)
+- Test files you're experimenting with
+- Backup files (.bak, .bak2)
+- Export/debug .yml/.json files
+- **ANY script that's temporary or one-time use**
+- DO NOT commit without cleanup
+
+**dev_notes/** - All documentation
+- All .md files except README.md and CLAUDE.md
+- Presentation materials (.pptx, .html, .txt)
+- Research documents
+- Architecture notes
+
+**Root directory** - ONLY essential files
+- Main application files
+- Core utilities
+- Configuration (.env, requirements.txt)
+- README.md and CLAUDE.md only
+- DO NOT create random files here
+
+### Simple Decision Tree
+
+Creating a new file? Ask yourself:
+
+1. **Is it a real test that should be automated?** → `tests/`
+2. **Is it documentation/presentation?** → `dev_notes/`
+3. **Is it core application code?** → Root (but ASK first!)
+4. **Everything else?** → `scratch/` (debug, check, verify, analyze, fix, backup, experimental)
+
+### When in doubt: PUT IT IN SCRATCH
+
+## Testing Existing Features
+
+**NEVER create simplified versions of working code**
+
+When testing:
+- ❌ WRONG: Write new simplified code from scratch
+- ✅ RIGHT: Call existing working functions in a test harness
+
+Example:
+- ❌ WRONG: Create new viz configs and call low-level functions
+- ✅ RIGHT: Call `create_liveboard_from_model()` (the actual function used)
+
+## Environment & Setup
+
+- Python virtual environment: `./demo_wire/bin/activate`
+- NOT using conda
+- Supabase IS installed and configured (.env works)
+- ThoughtSpot auth works with demo_builder_user
+
+## Common Mistakes to AVOID
+
+1. DO NOT add unnecessary .env validation - variables are populated
+2. DO NOT try to install supabase/packages - already in venv
+3. DO NOT change defaults to "thoughtspot.com" - that's for customer URLs
+4. DO NOT assume worksheets needed - they're deprecated, use models
+5. ALWAYS use venv when running Python: `source ./demo_wire/bin/activate && python`
+
+## Before Making Changes
+
+1. Check sprint documentation: `dev_notes/sprint2_102025.md`
+2. Read "Known Issues" section
+3. Verify using venv, not system Python
+4. Don't add validation that blocks working code
+5. Check file organization rules before creating files
+
+## Frustration Points (AVOID)
+
+User gets frustrated when you:
+- Don't trust that .env variables are correct
+- Try to reinstall already-installed packages
+- Make changes without understanding context
+- Break working code by "simplifying" it
+- **RESTART THE APPLICATION WITHOUT PERMISSION**
+
+## Working Patterns
+
+- Settings save/load through Supabase works
+- ThoughtSpot TML IS YAML format (yaml.dump() required)
+- Models replaced worksheets
+- Liveboards match "golden demo" style
+
+## Liveboard Creation - DUAL METHOD SYSTEM
+
+**PRIMARY GOAL:** Both MCP and TML methods must work simultaneously with shared codebase
+
+### Method Selection (via environment variable)
+- `USE_MCP_LIVEBOARD=true` → MCP method (default)
+- `USE_MCP_LIVEBOARD=false` → TML method
+- Entry point: `thoughtspot_deployer.py:1548`
+
+### MCP Method (AI-Driven)
+- Uses Model Context Protocol with ThoughtSpot's agent.thoughtspot.app
+- Natural language questions → ThoughtSpot creates visualizations
+- OAuth authentication, requires npx/Node.js
+- **Status:** Working well!
+- **Main function:** `create_liveboard_from_model_mcp()` - line 2006
+
+### TML Method (Template-Based)
+- Builds ThoughtSpot Modeling Language (YAML) structures
+- Direct control over visualization types and layout
+- REST API with token auth
+- **Status:** Needs fixes for KPIs and search queries
+- **Main function:** `create_liveboard_from_model()` - line 1779
+
+### Critical Shared Code (Changes affect BOTH methods)
+- `_generate_smart_questions_with_ai()` - line 1863
+- `_generate_fallback_visualizations()` - line 1442
+- LLM Researcher instance
+- Outlier conversion helpers
+
+### Method-Specific Code (Safe to change independently)
+- **MCP only:**
+  - `_convert_outlier_to_mcp_question()` - line 1809
+  - `_create_kpi_question_from_outlier()` - line 1835
+  - MCP tool calls (getAnswer, createLiveboard)
+- **TML only:**
+  - `LiveboardCreator` class - line 452
+  - `generate_search_query()` - line 539
+  - `create_visualization_tml()` - line 1196
+  - `deploy_liveboard()` - line 1670
+
+### KPI Requirements (BOTH methods need these)
+- **For sparklines and percent change comparisons:**
+  - Must include time dimension (date column)
+  - Must specify granularity (daily, weekly, monthly, quarterly, yearly)
+  - Example: `[Total_revenue] [Order_date].monthly`
+- **MCP:** Natural language includes time context
+- **TML:** Search query must have `[measure] [date_column].granularity`
+
+### Terminology Clarification
+- **Outliers:** Interesting data points in existing data (both methods support)
+- **Data Adjuster:** Modifying values for scenarios (NOT supported by MCP, Snowflake views needed)
+
+### Golden Demo Structure
+- **Location:** `/Users/mike.boone/cursor_demowire/DemoPrep/liveboard_demogold2/🏬 Global Retail Apparel Sales (New).liveboard.tml`
+- Uses GROUPS (tabs) not text tiles for organization
+- KPI structure: `[sales] [date].weekly [date].'last 8 quarters'`
+- Groups organize visualizations by theme
+- Brand colors applied via style_properties
+
+### Testing Strategy
+- Test BOTH methods when changing shared code
+- Use separate test files: `tests_temp/test_mcp_*.py` vs `tests_temp/test_tml_*.py`
+- Same dataset, same company, compare results
+
+## Project Context
+
+- Read primary context: `dev_notes/sprint2_102025.md`
+- Software stored in boone's repo, will be open sourced to TS repo
+- This is a living project - update understanding as you learn
+
+---
+
+*Derived from CLAUDE.md - Last Updated: November 18, 2025*
+

.gitignore

@@ -217,3 +217,4 @@ dev/
 # Development notes and sensitive documentation
 dev_notes/
 *.tml
+scratch/

CHAT_ARCHITECTURE_PLAN.md

@@ -1,1408 +0,0 @@
-# AI-Centric Chat Application Architecture Plan
-## Transformation from Linear Workflow to Conversational Demo Builder
-
-**Date:** 2025-11-12  
-**Purpose:** Transform the current button-based demo builder into a conversational AI agent that guides users through demo creation with approval gates and iterative refinement.
-
----
-
-## Executive Summary
-
-Transform the existing linear workflow application into an **AI-powered conversational demo builder** where:
-- Users chat naturally with an AI agent to create demos
-- The AI interprets intent, executes actions, and asks for approval
-- Each stage requires explicit user approval before proceeding
-- Users can iterate and refine outputs at any stage
-- The system maintains context and state across the conversation
-
----
-
-## Current Architecture Analysis
-
-### Existing Components
-```
-demo_prep.py (3581 lines)
-├── UI Layer: Gradio interface with button-based workflow
-├── Workflow Handler: progressive_workflow_handler()
-├── Stage Execution: Linear progression through stages
-└── State Management: DemoBuilder class
-
-Supporting Modules:
-├── demo_builder_class.py - State management
-├── main_research.py - Company/industry research
-├── schema_utils.py - DDL parsing & validation
-├── cdw_connector.py - Snowflake deployment
-├── thoughtspot_deployer.py - TS object creation
-├── liveboard_creator.py - Visualization generation
-├── model_semantic_updater.py - Model enhancement
-└── demo_personas.py - Business context
-```
-
-### Current Workflow Stages
-1. **Research** → Company analysis + Industry research
-2. **Create DDL** → Generate Snowflake schema
-3. **Create Population Code** → Generate data scripts
-4. **Deploy** → Push to Snowflake + Create TS connection/tables
-5. **Create Model** → Generate semantic model
-6. **Create Liveboard** → Generate visualizations
-
-### Current Limitations
-- ❌ No approval gates - everything auto-advances
-- ❌ Limited iteration - can only "redo" entire stages
-- ❌ No conversational refinement
-- ❌ Button-based interaction (not chat)
-- ❌ No AI interpretation of user intent
-- ❌ Hard to make targeted changes (e.g., "change this one visualization")
-
----
-
-## New Chat-Based Architecture
-
-### Core Concept: AI Controller Pattern
-
-```
-┌─────────────────────────────────────────────────────┐
-│                    USER INPUT                        │
-│              (Natural Language Chat)                 │
-└────────────────────┬────────────────────────────────┘
-                     │
-                     ▼
-┌─────────────────────────────────────────────────────┐
-│              INTENT CLASSIFIER                       │
-│   (AI Router - determines what user wants)          │
-│                                                      │
-│  Categories:                                         │
-│  • stage_advance (ready to move forward)            │
-│  • stage_approval (approve current output)          │
-│  • stage_rejection (redo current output)            │
-│  • refinement_request (modify specific aspect)      │
-│  • information_request (explain/show something)     │
-│  • configuration_change (update parameters)         │
-└────────────────────┬────────────────────────────────┘
-                     │
-                     ▼
-┌─────────────────────────────────────────────────────┐
-│           CONVERSATION CONTROLLER                    │
-│   (Manages workflow state & execution)              │
-│                                                      │
-│  Responsibilities:                                   │
-│  • Maintains conversation context                   │
-│  • Tracks current stage & substage                  │
-│  • Determines when stage is "complete"              │
-│  • Executes appropriate actions                     │
-│  • Manages approval gates                           │
-│  • Handles error recovery                           │
-└────────────────────┬────────────────────────────────┘
-                     │
-                     ▼
-┌─────────────────────────────────────────────────────┐
-│              STAGE EXECUTORS                         │
-│   (Specialized handlers for each workflow stage)    │
-│                                                      │
-│  • ResearchExecutor                                 │
-│  • DDLExecutor                                      │
-│  • PopulationExecutor                               │
-│  • DeploymentExecutor                               │
-│  • ModelExecutor                                    │
-│  • LiveboardExecutor                                │
-│  • VisualizationRefiner (new!)                     │
-│  • SiteCreator (new!)                              │
-│  • BotCreator (new!)                               │
-└────────────────────┬────────────────────────────────┘
-                     │
-                     ▼
-┌─────────────────────────────────────────────────────┐
-│            RESPONSE GENERATOR                        │
-│   (Formats output for user in chat)                 │
-│                                                      │
-│  • Streaming responses                              │
-│  • Rich formatting (code blocks, tables)            │
-│  • Action prompts ("What would you like to do?")   │
-│  • Progress indicators                              │
-└─────────────────────────────────────────────────────┘
-```
-
----
-
-## Detailed Component Design
-
-### 1. Intent Classifier (New Component)
-
-**Purpose:** Interpret user's natural language to determine their intent
-
-**Implementation:**
-```python
-class IntentClassifier:
-    """
-    Uses LLM to classify user intent and extract parameters
-    """
-    
-    def __init__(self, llm_provider="anthropic", model="claude-sonnet-4.5"):
-        self.provider = llm_provider
-        self.model = model
-    
-    def classify_intent(self, user_message: str, conversation_context: ConversationContext) -> Intent:
-        """
-        Returns Intent object with:
-        - intent_type: enum (APPROVE, REJECT, REFINE, ADVANCE, INFO, CONFIGURE)
-        - confidence: float
-        - parameters: dict (extracted entities)
-        - reasoning: str (why this classification)
-        """
-        
-        system_prompt = f"""You are an intent classifier for a demo preparation workflow.
-        
-Current Stage: {conversation_context.current_stage}
-Current Stage Status: {conversation_context.stage_status}
-Available Actions: {conversation_context.get_available_actions()}
-
-Classify the user's intent into one of these categories:
-
-1. APPROVE - User approves current output and wants to proceed
-   Examples: "looks good", "approve", "yes proceed", "let's move on"
-   
-2. REJECT - User wants to redo current stage with changes
-   Examples: "no that's not right", "redo the DDL", "try again with..."
-   
-3. REFINE - User wants to modify specific aspect of current output
-   Examples: "change the customer table", "make this visualization a bar chart"
-   
-4. ADVANCE - User wants to move to next stage (when no approval needed)
-   Examples: "create the population code", "let's do the liveboard"
-   
-5. INFO - User wants information or explanation
-   Examples: "show me the DDL", "what tables did you create", "explain this"
-   
-6. CONFIGURE - User wants to change settings/parameters
-   Examples: "use GPT-4 instead", "increase data volume", "change company to Acme"
-
-Extract relevant parameters and return structured JSON.
-"""
-        
-        # Make LLM call to classify intent
-        response = self.llm_call(system_prompt, user_message)
-        return Intent.from_json(response)
-```
-
-**Key Features:**
-- Context-aware (knows current stage)
-- Extracts parameters (e.g., which table to modify, new company name)
-- Confidence scoring
-- Falls back to clarification questions if ambiguous
-
----
-
-### 2. Conversation Controller (Enhanced DemoBuilder)
-
-**Purpose:** Orchestrates the entire demo creation conversation
-
-**Implementation:**
-```python
-class ConversationController:
-    """
-    Main orchestrator for chat-based demo creation
-    Inherits from DemoBuilder, adds conversational logic
-    """
-    
-    def __init__(self, use_case: str = None, company_url: str = None):
-        # State tracking
-        self.conversation_history: List[Message] = []
-        self.current_stage: Stage = Stage.INITIALIZATION
-        self.stage_status: StageStatus = StageStatus.NOT_STARTED
-        
-        # Approval tracking
-        self.pending_approval: Optional[ApprovalRequest] = None
-        self.stage_outputs: Dict[Stage, Any] = {}
-        
-        # Context for AI decision making
-        self.conversation_context = ConversationContext()
-        
-        # Executors for each stage
-        self.executors = {
-            Stage.RESEARCH: ResearchExecutor(),
-            Stage.CREATE_DDL: DDLExecutor(),
-            Stage.CREATE_POPULATION: PopulationExecutor(),
-            Stage.DEPLOY: DeploymentExecutor(),
-            Stage.CREATE_MODEL: ModelExecutor(),
-            Stage.CREATE_LIVEBOARD: LiveboardExecutor(),
-            Stage.REFINE_VIZS: VisualizationRefiner(),
-            Stage.CREATE_SITE: SiteCreator(),
-            Stage.CREATE_BOT: BotCreator()
-        }
-    
-    async def process_message(self, user_message: str) -> AsyncGenerator[str, None]:
-        """
-        Main entry point for processing user messages
-        Yields streaming responses
-        """
-        # 1. Add to conversation history
-        self.conversation_history.append(Message(role="user", content=user_message))
-        
-        # 2. Classify intent
-        intent = await self.intent_classifier.classify_intent(
-            user_message, 
-            self.conversation_context
-        )
-        
-        # 3. Update context
-        self.conversation_context.update(intent, self.current_stage)
-        
-        # 4. Route to appropriate handler
-        if intent.type == IntentType.APPROVE:
-            async for response in self.handle_approval(intent):
-                yield response
-                
-        elif intent.type == IntentType.REJECT:
-            async for response in self.handle_rejection(intent):
-                yield response
-                
-        elif intent.type == IntentType.REFINE:
-            async for response in self.handle_refinement(intent):
-                yield response
-                
-        elif intent.type == IntentType.ADVANCE:
-            async for response in self.handle_stage_advance(intent):
-                yield response
-                
-        elif intent.type == IntentType.INFO:
-            async for response in self.handle_info_request(intent):
-                yield response
-                
-        elif intent.type == IntentType.CONFIGURE:
-            async for response in self.handle_configuration(intent):
-                yield response
-        
-        # 5. Determine next action and prompt user
-        next_prompt = self.get_next_action_prompt()
-        yield f"\n\n{next_prompt}"
-    
-    def should_request_approval(self, stage: Stage) -> bool:
-        """
-        Determines if a stage requires user approval before proceeding
-        """
-        approval_required_stages = [
-            Stage.RESEARCH,
-            Stage.CREATE_DDL,
-            Stage.CREATE_POPULATION,
-            Stage.CREATE_MODEL,
-            Stage.CREATE_LIVEBOARD
-        ]
-        return stage in approval_required_stages
-    
-    async def handle_approval(self, intent: Intent) -> AsyncGenerator[str, None]:
-        """
-        User approved current stage output
-        """
-        if not self.pending_approval:
-            yield "There's nothing pending approval right now. What would you like to do?"
-            return
-        
-        # Mark stage as approved
-        self.stage_outputs[self.current_stage].status = OutputStatus.APPROVED
-        yield f"✅ Great! {self.current_stage.display_name} approved.\n\n"
-        
-        # Advance to next stage
-        next_stage = self.get_next_stage()
-        if next_stage:
-            yield f"Moving to: **{next_stage.display_name}**\n"
-            self.current_stage = next_stage
-            
-            # Start next stage automatically or ask what to do
-            if self.should_auto_start(next_stage):
-                async for response in self.execute_stage(next_stage):
-                    yield response
-            else:
-                yield f"Ready to start {next_stage.display_name}. Just say 'go' or 'start' when ready!"
-        else:
-            yield "🎉 All stages complete! Your demo is ready."
-    
-    async def handle_rejection(self, intent: Intent) -> AsyncGenerator[str, None]:
-        """
-        User rejected current stage output and wants to redo
-        """
-        yield f"🔄 Got it, let me redo the {self.current_stage.display_name}.\n\n"
-        
-        # Extract what to change from intent parameters
-        changes = intent.parameters.get('requested_changes', '')
-        if changes:
-            yield f"Incorporating your feedback: {changes}\n\n"
-        
-        # Re-execute current stage with modifications
-        self.stage_status = StageStatus.IN_PROGRESS
-        executor = self.executors[self.current_stage]
-        
-        async for response in executor.execute(
-            context=self.get_execution_context(),
-            modifications=intent.parameters
-        ):
-            yield response
-        
-        # Request approval again
-        self.pending_approval = ApprovalRequest(stage=self.current_stage)
-        yield "\n\n" + self.format_approval_request()
-    
-    async def handle_refinement(self, intent: Intent) -> AsyncGenerator[str, None]:
-        """
-        User wants to refine specific aspect of current output
-        """
-        target = intent.parameters.get('target')  # e.g., "customer_table", "viz_3"
-        modification = intent.parameters.get('modification')  # e.g., "add email column"
-        
-        yield f"🎨 Refining {target}...\n\n"
-        
-        # Use specialized refiner based on what's being modified
-        if self.current_stage == Stage.CREATE_DDL:
-            async for response in self.refine_ddl(target, modification):
-                yield response
-                
-        elif self.current_stage == Stage.CREATE_LIVEBOARD:
-            async for response in self.refine_visualization(target, modification):
-                yield response
-        
-        # Show updated output and ask if good now
-        yield "\n\nHere's the updated version. How does this look?"
-    
-    def get_next_action_prompt(self) -> str:
-        """
-        Returns context-appropriate prompt for user's next action
-        """
-        if self.pending_approval:
-            return "👉 **Please review and approve**, or tell me what to change."
-        
-        if self.stage_status == StageStatus.COMPLETE:
-            next_stage = self.get_next_stage()
-            if next_stage:
-                return f"👉 **Ready for {next_stage.display_name}?** Say 'yes' to continue or ask me questions."
-            else:
-                return "🎉 **Demo complete!** What would you like to do next?"
-        
-        if self.stage_status == StageStatus.ERROR:
-            return "❌ **There was an error.** Would you like me to try again?"
-        
-        return "💬 **What would you like to do?**"
-```
-
----
-
-### 3. Stage Executors (Specialized Handlers)
-
-**Purpose:** Each stage has a dedicated executor that knows how to perform that specific task
-
-**Base Executor Interface:**
-```python
-from abc import ABC, abstractmethod
-
-class StageExecutor(ABC):
-    """Base class for all stage executors"""
-    
-    @abstractmethod
-    async def execute(
-        self, 
-        context: ExecutionContext, 
-        modifications: Optional[Dict] = None
-    ) -> AsyncGenerator[str, None]:
-        """
-        Execute this stage
-        Yields streaming responses
-        """
-        pass
-    
-    @abstractmethod
-    def can_refine(self, target: str) -> bool:
-        """
-        Returns True if this executor can refine the specified target
-        """
-        pass
-    
-    @abstractmethod
-    async def refine(
-        self, 
-        target: str, 
-        modification: str,
-        context: ExecutionContext
-    ) -> AsyncGenerator[str, None]:
-        """
-        Refine specific aspect of this stage's output
-        """
-        pass
-```
-
-**Example: DDL Executor**
-```python
-class DDLExecutor(StageExecutor):
-    """
-    Handles DDL generation with intelligent refinement
-    """
-    
-    async def execute(
-        self, 
-        context: ExecutionContext, 
-        modifications: Optional[Dict] = None
-    ) -> AsyncGenerator[str, None]:
-        """
-        Generate DDL from research context
-        """
-        yield "## 🏗️ Creating Database Schema\n\n"
-        
-        # Build prompt with research context
-        prompt = self.build_ddl_prompt(
-            research=context.research_results,
-            use_case=context.use_case,
-            modifications=modifications
-        )
-        
-        # Stream DDL generation
-        yield "```sql\n"
-        
-        ddl_content = ""
-        async for chunk in context.llm.stream(prompt):
-            ddl_content += chunk
-            yield chunk
-        
-        yield "\n```\n\n"
-        
-        # Validate DDL
-        is_valid, validation_msg = validate_ddl_syntax(ddl_content)
-        if is_valid:
-            yield f"✅ DDL validation passed\n\n"
-            
-            # Parse and show table summary
-            tables = parse_ddl_schema(ddl_content)
-            yield f"**Generated {len(tables)} tables:**\n"
-            for table_name, table_info in tables.items():
-                yield f"- `{table_name}` ({len(table_info['columns'])} columns)\n"
-        else:
-            yield f"⚠️ Validation warning: {validation_msg}\n\n"
-        
-        # Store in context
-        context.store_output(Stage.CREATE_DDL, {
-            'ddl': ddl_content,
-            'tables': tables,
-            'validation': validation_msg
-        })
-    
-    def can_refine(self, target: str) -> bool:
-        """Check if target is a table name or column"""
-        tables = self.context.get_output(Stage.CREATE_DDL).get('tables', {})
-        
-        # Check if it's a table name
-        if target.lower() in [t.lower() for t in tables.keys()]:
-            return True
-        
-        # Check if it's a column in format "table.column"
-        if '.' in target:
-            table, column = target.split('.')
-            if table in tables and column in tables[table]['columns']:
-                return True
-        
-        return False
-    
-    async def refine(
-        self, 
-        target: str, 
-        modification: str,
-        context: ExecutionContext
-    ) -> AsyncGenerator[str, None]:
-        """
-        Refine specific table or column
-        """
-        current_ddl = context.get_output(Stage.CREATE_DDL)['ddl']
-        
-        # Use LLM to modify just the target portion
-        prompt = f"""You are modifying a SQL DDL schema.
-
-Current DDL:
-{current_ddl}
-
-User wants to modify: {target}
-Requested change: {modification}
-
-Return the COMPLETE updated DDL with the changes applied.
-Maintain all other tables unchanged.
-Only modify what was requested.
-"""
-        
-        yield f"Updating {target}...\n\n"
-        yield "```sql\n"
-        
-        updated_ddl = ""
-        async for chunk in context.llm.stream(prompt):
-            updated_ddl += chunk
-            yield chunk
-        
-        yield "\n```\n\n"
-        
-        # Update stored output
-        context.store_output(Stage.CREATE_DDL, {'ddl': updated_ddl})
-        
-        yield f"✅ Updated {target}\n"
-```
-
-**Example: Visualization Refiner (New)**
-```python
-class VisualizationRefiner(StageExecutor):
-    """
-    Specialized executor for refining visualizations
-    """
-    
-    async def refine(
-        self, 
-        target: str,  # e.g., "viz_3" or "revenue over time chart"
-        modification: str,  # e.g., "change to bar chart" or "add region filter"
-        context: ExecutionContext
-    ) -> AsyncGenerator[str, None]:
-        """
-        Intelligently refine a specific visualization
-        """
-        liveboard = context.get_output(Stage.CREATE_LIVEBOARD)
-        
-        # Find the target visualization
-        viz_index = self.find_visualization(target, liveboard)
-        if viz_index is None:
-            yield f"❌ Couldn't find visualization: {target}\n"
-            return
-        
-        current_viz = liveboard['visualizations'][viz_index]
-        
-        yield f"🎨 Refining visualization: **{current_viz['title']}**\n\n"
-        
-        # Classify what kind of modification is needed
-        modification_type = await self.classify_modification(modification)
-        
-        if modification_type == 'CHART_TYPE':
-            # Change chart type
-            new_chart_type = extract_chart_type(modification)
-            yield f"Changing from {current_viz['chart_type']} to {new_chart_type}...\n"
-            
-            # Regenerate with new chart type
-            updated_viz = await self.regenerate_viz_with_type(
-                current_viz, 
-                new_chart_type,
-                context
-            )
-        
-        elif modification_type == 'DATA_FILTER':
-            # Add/modify filter
-            yield f"Adding filter: {modification}\n"
-            updated_viz = await self.add_viz_filter(current_viz, modification, context)
-        
-        elif modification_type == 'MEASURE_CHANGE':
-            # Change measure/dimension
-            yield f"Updating data fields...\n"
-            updated_viz = await self.update_viz_fields(current_viz, modification, context)
-        
-        # Update liveboard
-        liveboard['visualizations'][viz_index] = updated_viz
-        context.store_output(Stage.CREATE_LIVEBOARD, liveboard)
-        
-        # Show preview
-        yield "\n**Updated Visualization:**\n"
-        yield self.format_viz_preview(updated_viz)
-        yield "\n"
-```
-
----
-
-### 4. Enhanced State Management
-
-**New Data Models:**
-
-```python
-from enum import Enum
-from dataclasses import dataclass
-from typing import List, Dict, Optional, Any
-from datetime import datetime
-
-class Stage(Enum):
-    """Workflow stages"""
-    INITIALIZATION = "initialization"
-    RESEARCH = "research"
-    CREATE_DDL = "create_ddl"
-    CREATE_POPULATION = "create_population"
-    DEPLOY = "deploy"
-    CREATE_MODEL = "create_model"
-    CREATE_LIVEBOARD = "create_liveboard"
-    REFINE_VIZS = "refine_visualizations"
-    CREATE_SITE = "create_site"
-    CREATE_BOT = "create_bot"
-    COMPLETE = "complete"
-    
-    @property
-    def display_name(self) -> str:
-        names = {
-            Stage.INITIALIZATION: "Setup",
-            Stage.RESEARCH: "Research",
-            Stage.CREATE_DDL: "DDL Creation",
-            Stage.CREATE_POPULATION: "Population Code",
-            Stage.DEPLOY: "Deployment",
-            Stage.CREATE_MODEL: "Model Creation",
-            Stage.CREATE_LIVEBOARD: "Liveboard Creation",
-            Stage.REFINE_VIZS: "Visualization Refinement",
-            Stage.CREATE_SITE: "Site Creation",
-            Stage.CREATE_BOT: "Bot Creation",
-            Stage.COMPLETE: "Complete"
-        }
-        return names.get(self, self.value)
-
-class StageStatus(Enum):
-    """Status of current stage"""
-    NOT_STARTED = "not_started"
-    IN_PROGRESS = "in_progress"
-    AWAITING_APPROVAL = "awaiting_approval"
-    APPROVED = "approved"
-    REJECTED = "rejected"
-    COMPLETE = "complete"
-    ERROR = "error"
-
-class IntentType(Enum):
-    """Types of user intent"""
-    APPROVE = "approve"
-    REJECT = "reject"
-    REFINE = "refine"
-    ADVANCE = "advance"
-    INFO = "info"
-    CONFIGURE = "configure"
-    CLARIFICATION_NEEDED = "clarification_needed"
-
-@dataclass
-class Intent:
-    """User intent classification result"""
-    type: IntentType
-    confidence: float
-    parameters: Dict[str, Any]
-    reasoning: str
-    
-    @classmethod
-    def from_json(cls, json_data: Dict) -> 'Intent':
-        return cls(
-            type=IntentType(json_data['type']),
-            confidence=json_data.get('confidence', 0.0),
-            parameters=json_data.get('parameters', {}),
-            reasoning=json_data.get('reasoning', '')
-        )
-
-@dataclass
-class Message:
-    """Conversation message"""
-    role: str  # "user" or "assistant"
-    content: str
-    timestamp: datetime = None
-    metadata: Dict = None
-    
-    def __post_init__(self):
-        if self.timestamp is None:
-            self.timestamp = datetime.now()
-        if self.metadata is None:
-            self.metadata = {}
-
-@dataclass
-class ApprovalRequest:
-    """Pending approval for stage output"""
-    stage: Stage
-    output: Any
-    timestamp: datetime = None
-    
-    def __post_init__(self):
-        if self.timestamp is None:
-            self.timestamp = datetime.now()
-
-class ConversationContext:
-    """
-    Maintains context for the conversation controller
-    """
-    def __init__(self):
-        self.current_stage: Stage = Stage.INITIALIZATION
-        self.stage_history: List[Stage] = []
-        self.user_preferences: Dict = {}
-        self.last_n_messages: List[Message] = []
-        
-    def update(self, intent: Intent, current_stage: Stage):
-        """Update context based on new intent"""
-        self.last_n_messages.append(Message(
-            role="system",
-            content=f"Intent: {intent.type.value}",
-            metadata={'intent': intent}
-        ))
-        
-        if len(self.last_n_messages) > 20:
-            self.last_n_messages = self.last_n_messages[-20:]
-    
-    def get_available_actions(self) -> List[str]:
-        """Returns list of actions available in current state"""
-        actions = []
-        
-        if self.current_stage == Stage.INITIALIZATION:
-            actions = ["configure", "start_research"]
-        elif self.current_stage == Stage.RESEARCH:
-            actions = ["approve", "reject", "show_details"]
-        # ... etc for each stage
-        
-        return actions
-```
-
----
-
-### 5. Chat UI Design (Gradio)
-
-**New Interface Layout:**
-
-```python
-def create_chat_interface():
-    """
-    Create conversational demo builder interface
-    """
-    
-    with gr.Blocks(title="ThoughtSpot Demo Builder - Chat", theme=gr.themes.Soft()) as interface:
-        
-        # Initialize conversation controller
-        controller = gr.State(ConversationController())
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                # Main chat interface
-                gr.Markdown("# 💬 ThoughtSpot Demo Builder")
-                gr.Markdown("Let's create an amazing demo together! Tell me about your company...")
-                
-                chatbot = gr.Chatbot(
-                    value=[],
-                    height=600,
-                    label="Demo Builder Assistant",
-                    avatar_images=[None, "🤖"]
-                )
-                
-                with gr.Row():
-                    msg = gr.Textbox(
-                        label="Your message",
-                        placeholder="Type your message here... (e.g., 'Create a demo for Acme Corp in retail')",
-                        lines=2,
-                        scale=4
-                    )
-                    submit = gr.Button("Send", scale=1, variant="primary")
-                
-                # Quick action buttons (context-aware)
-                with gr.Row(visible=True) as quick_actions:
-                    approve_btn = gr.Button("✅ Approve", visible=False)
-                    reject_btn = gr.Button("❌ Redo", visible=False)
-                    next_btn = gr.Button("➡️ Next Stage", visible=False)
-            
-            with gr.Column(scale=1):
-                # Progress and context sidebar
-                gr.Markdown("## 📊 Progress")
-                
-                stage_display = gr.Markdown("**Current Stage:** Initialization")
-                
-                # Visual progress tracker
-                progress_html = gr.HTML("""
-                    <div style='padding: 10px;'>
-                        <div class='stage-item'>⚪ Research</div>
-                        <div class='stage-item'>⚪ Create DDL</div>
-                        <div class='stage-item'>⚪ Create Population</div>
-                        <div class='stage-item'>⚪ Deploy</div>
-                        <div class='stage-item'>⚪ Create Model</div>
-                        <div class='stage-item'>⚪ Create Liveboard</div>
-                        <div class='stage-item'>⚪ Refine</div>
-                    </div>
-                """)
-                
-                gr.Markdown("## ⚙️ Current Settings")
-                settings_display = gr.JSON(
-                    value={
-                        "company": "Not set",
-                        "use_case": "Not set",
-                        "llm": "claude-sonnet-4.5"
-                    },
-                    label="Configuration"
-                )
-                
-                gr.Markdown("## 📁 Generated Assets")
-                assets_list = gr.HTML("<i>No assets yet</i>")
-        
-        # Chat message handler
-        def respond(message, chat_history, controller_state):
-            """Process user message and generate response"""
-            
-            # Add user message to chat
-            chat_history.append((message, None))
-            
-            # Stream AI response
-            ai_response = ""
-            async for chunk in controller_state.process_message(message):
-                ai_response += chunk
-                chat_history[-1] = (message, ai_response)
-                yield chat_history, controller_state, update_ui_state(controller_state)
-            
-            return chat_history, controller_state, update_ui_state(controller_state)
-        
-        def update_ui_state(controller_state):
-            """
-            Update all UI elements based on controller state
-            Returns: (stage_display, progress_html, settings, assets, quick_buttons_visibility)
-            """
-            # Update stage display
-            stage_md = f"**Current Stage:** {controller_state.current_stage.display_name}"
-            
-            # Update progress HTML
-            progress = generate_progress_html(controller_state)
-            
-            # Update settings
-            settings = {
-                "company": controller_state.company_url or "Not set",
-                "use_case": controller_state.use_case or "Not set",
-                "current_stage": controller_state.current_stage.value,
-                "llm": controller_state.llm_provider
-            }
-            
-            # Update assets
-            assets = generate_assets_html(controller_state)
-            
-            # Update quick action button visibility
-            show_approve = controller_state.stage_status == StageStatus.AWAITING_APPROVAL
-            show_next = controller_state.stage_status == StageStatus.COMPLETE
-            
-            return (
-                stage_md,
-                progress,
-                settings,
-                assets,
-                gr.update(visible=show_approve),  # approve_btn
-                gr.update(visible=show_approve),  # reject_btn
-                gr.update(visible=show_next)      # next_btn
-            )
-        
-        # Wire up handlers
-        msg.submit(
-            fn=respond,
-            inputs=[msg, chatbot, controller],
-            outputs=[chatbot, controller, stage_display, progress_html, settings_display, 
-                     assets_list, approve_btn, reject_btn, next_btn]
-        )
-        
-        submit.click(
-            fn=respond,
-            inputs=[msg, chatbot, controller],
-            outputs=[chatbot, controller, stage_display, progress_html, settings_display,
-                     assets_list, approve_btn, reject_btn, next_btn]
-        )
-        
-        # Quick action buttons
-        approve_btn.click(
-            fn=lambda ch, ctrl: respond("I approve this", ch, ctrl),
-            inputs=[chatbot, controller],
-            outputs=[chatbot, controller, stage_display, progress_html]
-        )
-        
-        reject_btn.click(
-            fn=lambda ch, ctrl: respond("Please redo this", ch, ctrl),
-            inputs=[chatbot, controller],
-            outputs=[chatbot, controller, stage_display, progress_html]
-        )
-    
-    return interface
-```
-
----
-
-## Example Conversation Flows
-
-### Flow 1: Happy Path (All Approvals)
-
-```
-USER: Create a demo for Amazon.com focused on supply chain analytics
-
-AI: Great! Let me start by researching Amazon and supply chain analytics.
-    [Streaming research results...]
-    
-    ✅ Research complete! I found:
-    - Amazon is a global e-commerce and cloud computing leader
-    - Key supply chain challenges: last-mile delivery, inventory optimization
-    - Focus areas: predictive demand, warehouse efficiency
-    
-    👉 Please review the research. Reply "approve" to proceed or tell me what to adjust.
-
-USER: looks good
-
-AI: ✅ Research approved!
-    
-    Moving to: **DDL Creation**
-    
-    Creating database schema for supply chain analytics...
-    [Streaming SQL DDL...]
-    
-    ✅ Generated 5 tables:
-    - `dim_products` (12 columns)
-    - `dim_warehouses` (8 columns)
-    - `dim_suppliers` (10 columns)
-    - `dim_dates` (7 columns)
-    - `fact_inventory_movements` (15 columns)
-    
-    👉 Please review the DDL. Approve or request changes.
-
-USER: Can you add a "category" column to the products table?
-
-AI: 🎨 Refining dim_products table...
-    
-    ```sql
-    CREATE TABLE dim_products (
-        product_id INT IDENTITY(1,1) PRIMARY KEY,
-        product_name VARCHAR(200) NOT NULL,
-        sku VARCHAR(50) UNIQUE,
-        category VARCHAR(100),  -- ✨ Added
-        subcategory VARCHAR(100),
-        ...
-    );
-    ```
-    
-    ✅ Updated dim_products with category column
-    
-    How does this look?
-
-USER: perfect, approve
-
-AI: ✅ DDL approved!
-    
-    Moving to: **Population Code**
-    
-    Generating realistic supply chain data with strategic outliers...
-    [Streaming Python code...]
-    
-    📊 Population script ready:
-    - 5,000 products
-    - 20 warehouses
-    - 100 suppliers
-    - 50,000+ inventory movements
-    - 5 strategic outliers for demos
-    
-    👉 Approve to proceed with deployment?
-
-USER: yes
-
-AI: ✅ Population code approved!
-    
-    🚀 **Deploying to Snowflake...**
-    - Creating schema DM251112_143052_AMAZO_SUP...
-    - Executing DDL... ✅
-    - Populating data... ✅ (5,000 rows inserted)
-    
-    🎯 **Creating ThoughtSpot objects...**
-    - Connection... ✅
-    - Tables imported... ✅
-    - Model generated... ✅
-    
-    📊 **Creating Liveboard...**
-    Generated 6 visualizations:
-    1. Inventory Levels by Warehouse (KPI)
-    2. Stock Movement Trends (Line Chart)
-    3. Top Products by Turnover (Bar Chart)
-    4. Supplier Performance (Scatter)
-    5. Warehouse Capacity Utilization (Stacked Column)
-    6. Geographic Distribution (Geo Map)
-    
-    🎉 **Demo complete!**
-    
-    What would you like to do next?
-    - Refine visualizations
-    - Create demo site
-    - Create demo bot
-    - Export assets
-```
-
----
-
-### Flow 2: Refinement and Iteration
-
-```
-USER: The bar chart for top products doesn't look right. Can you make it show the top 10 instead of top 5?
-
-AI: 🎨 Refining visualization: **Top Products by Turnover**
-    
-    Updating to show top 10 products...
-    
-    **Updated Visualization:**
-    📊 Top Products by Turnover (Bar Chart)
-    - Shows: Top 10 products
-    - Sorted by: Total turnover (descending)
-    - Time period: Last 90 days
-    
-    Better?
-
-USER: yes but can you also change it to a horizontal bar chart?
-
-AI: 🎨 Refining visualization: **Top Products by Turnover**
-    
-    Changing to horizontal bar chart...
-    
-    **Updated Visualization:**
-    📊 Top Products by Turnover (Horizontal Bar)
-    - Shows: Top 10 products
-    - Orientation: Horizontal (easier to read long product names)
-    - Sorted by: Total turnover (descending)
-    
-    How's this?
-
-USER: perfect!
-
-AI: ✅ Great! Visualization updated.
-    
-    Your liveboard now has 6 visualizations, all looking good.
-    
-    What's next?
-    - Export demo assets
-    - Create a demo site
-    - Create a demo bot
-    - Or make more refinements
-```
-
----
-
-### Flow 3: Error Recovery
-
-```
-USER: approve the DDL
-
-AI: ✅ DDL approved!
-    
-    Moving to: **Population Code**
-    
-    Generating population scripts...
-    [Streaming...]
-    
-    ❌ Syntax validation failed:
-    - Line 42: Invalid Snowflake date format
-    - Line 156: Missing table reference
-    
-    🔄 Let me fix these issues and regenerate...
-    
-    [Streaming corrected code...]
-    
-    ✅ Validation passed!
-    
-    📊 Population script ready (corrected):
-    - Fixed date formatting to Snowflake syntax
-    - Added missing foreign key references
-    
-    👉 Approve to deploy?
-
-USER: yes deploy
-
-AI: 🚀 **Deploying to Snowflake...**
-    - Creating schema... ✅
-    - Executing DDL... ✅
-    - Populating data... ⚠️ 
-    
-    ❌ Error during population:
-    "Foreign key constraint violation on line 1,245"
-    
-    This usually means dimension data wasn't inserted before fact table.
-    
-    🔄 Would you like me to:
-    1. Fix the insert order and retry
-    2. Show you the error details
-    3. Regenerate the population code
-    
-    What would you prefer?
-
-USER: fix and retry
-
-AI: 🔧 Fixing insert order...
-    
-    Problem identified: Fact table inserts attempted before dimension tables completed.
-    
-    Solution: Reordering to insert dimensions first, then facts.
-    
-    ♻️ Retrying deployment...
-    - Dropping existing tables... ✅
-    - Recreating schema... ✅
-    - Inserting dimensions... ✅
-    - Inserting facts... ✅
-    
-    ✅ **Deployment successful!**
-    
-    All 50,000+ rows inserted successfully.
-    
-    Moving to ThoughtSpot object creation...
-```
-
----
-
-## Migration Strategy
-
-### Phase 1: Foundation (Week 1-2)
-**Goal:** Build core chat infrastructure without breaking existing functionality
-
-1. **Create new components** (parallel to existing code):
-   - `intent_classifier.py` - Intent classification
-   - `conversation_controller.py` - Main orchestrator
-   - `stage_executors/` - Directory with executor classes
-   - `conversation_models.py` - New data models
-
-2. **Add chat UI** (new tab in existing Gradio app):
-   - Keep existing button UI
-   - Add new "Chat Mode" tab
-   - Wire up basic chat → existing workflow
-   - No approval gates yet
-
-3. **Test basic flow**:
-   - User can chat to trigger stages
-   - Stages execute using existing code
-   - Responses formatted nicely in chat
-
-### Phase 2: Intent Classification (Week 3)
-**Goal:** AI interprets user intent accurately
-
-1. **Implement IntentClassifier**:
-   - Create prompt templates for classification
-   - Test with various user inputs
-   - Handle ambiguous cases
-
-2. **Add context tracking**:
-   - Track conversation history
-   - Maintain current stage/status
-   - Make intent classification context-aware
-
-3. **Test intent accuracy**:
-   - Unit tests for common intents
-   - Edge cases (ambiguous, multi-intent)
-   - Confidence thresholds
-
-### Phase 3: Approval Gates (Week 4)
-**Goal:** Users must approve before advancing
-
-1. **Add approval workflow**:
-   - After each stage, request approval
-   - Block advancement until approved
-   - Handle rejection → redo
-
-2. **Implement approval UX**:
-   - Clear approval requests in chat
-   - Quick action buttons
-   - Timeout handling (auto-approve after N minutes?)
-
-3. **Test approval flows**:
-   - Happy path (all approvals)
-   - Rejection scenarios
-   - Multiple iterations
-
-### Phase 4: Refinement (Week 5-6)
-**Goal:** Users can refine specific aspects
-
-1. **Implement DDL refinement**:
-   - Target table/column modifications
-   - Schema constraint validation
-   - Partial regeneration
-
-2. **Implement viz refinement**:
-   - Chart type changes
-   - Data field modifications
-   - Filter additions
-
-3. **Implement population refinement**:
-   - Outlier adjustments
-   - Data volume changes
-   - Scenario modifications
-
-### Phase 5: New Stages (Week 7-8)
-**Goal:** Add site and bot creation
-
-1. **Create SiteCreator executor**:
-   - Generate demo website HTML
-   - Use company branding
-   - Embed ThoughtSpot liveboards
-
-2. **Create BotCreator executor**:
-   - Generate chatbot config
-   - Train on demo data
-   - Integrate with ThoughtSpot API
-
-3. **Test end-to-end**:
-   - Full workflow from research to bot
-   - All approval gates working
-   - Refinement at each stage
-
-### Phase 6: Polish & Optimize (Week 9-10)
-**Goal:** Production-ready
-
-1. **Error handling**:
-   - Graceful failures
-   - Automatic retries
-   - User-friendly error messages
-
-2. **Performance**:
-   - Streaming optimization
-   - Caching improvements
-   - Parallel execution where possible
-
-3. **UX enhancements**:
-   - Better progress visualization
-   - Asset preview in chat
-   - Export/download in chat
-
-4. **Documentation**:
-   - User guide
-   - Example conversations
-   - Troubleshooting
-
----
-
-## Technical Considerations
-
-### LLM Provider Strategy
-- **Intent Classification**: Fast model (GPT-4o-mini or Claude Haiku)
-- **Content Generation**: High-quality model (Claude Sonnet 4.5 or GPT-4o)
-- **Refinement**: Mid-tier model (GPT-4o-mini for speed)
-
-### State Persistence
-- Save conversation state to database/file after each message
-- Enable "resume" functionality
-- Handle browser refresh gracefully
-
-### Async/Streaming
-- Use Python async/await throughout
-- Stream all LLM responses
-- Non-blocking stage execution
-
-### Error Recovery
-- Try/except around all stage executions
-- Automatic retry logic (with exponential backoff)
-- User-friendly error explanations
-- Option to rollback to previous stage
-
-### Testing Strategy
-1. **Unit tests** for each executor
-2. **Integration tests** for full workflows
-3. **Intent classification tests** with golden dataset
-4. **End-to-end tests** with real LLM calls
-5. **Performance tests** for streaming
-
----
-
-## Success Metrics
-
-### User Experience
-- ✅ Users can complete a demo without reading docs
-- ✅ Natural language commands work 95%+ of the time
-- ✅ Approval gates prevent bad outputs from advancing
-- ✅ Refinements work without full regeneration
-
-### Technical
-- ✅ Intent classification accuracy > 90%
-- ✅ Average workflow completion time < 15 minutes
-- ✅ Zero data loss from browser refresh
-- ✅ Error recovery success rate > 80%
-
-### Business
-- ✅ Reduction in demo prep time by 50%
-- ✅ Increase in demo quality (measured by win rate)
-- ✅ Users prefer chat mode over button mode (survey)
-
----
-
-## File Structure (New)
-
-```
-DemoPrep/
-├── demo_prep.py (existing - add chat tab)
-├── demo_builder_class.py (existing - enhance)
-│
-├── chat/
-│   ├── __init__.py
-│   ├── intent_classifier.py
-│   ├── conversation_controller.py
-│   ├── conversation_models.py
-│   ├── ui.py
-│   └── prompts/
-│       ├── intent_classification.py
-│       ├── clarification.py
-│       └── approval_requests.py
-│
-├── executors/
-│   ├── __init__.py
-│   ├── base.py
-│   ├── research_executor.py
-│   ├── ddl_executor.py
-│   ├── population_executor.py
-│   ├── deployment_executor.py
-│   ├── model_executor.py
-│   ├── liveboard_executor.py
-│   ├── visualization_refiner.py
-│   ├── site_creator.py
-│   └── bot_creator.py
-│
-├── (existing files unchanged)
-│   ├── main_research.py
-│   ├── schema_utils.py
-│   ├── cdw_connector.py
-│   ├── thoughtspot_deployer.py
-│   ├── liveboard_creator.py
-│   └── ...
-│
-└── tests/
-    ├── test_intent_classifier.py
-    ├── test_conversation_controller.py
-    ├── test_executors.py
-    └── test_refinement.py
-```
-
----
-
-## Risk Mitigation
-
-### Risk 1: Intent Classification Accuracy
-**Mitigation:**
-- Start with clear examples in prompts
-- Build golden dataset for testing
-- Add clarification questions when confidence < 0.7
-- Fallback to button UI if classification fails repeatedly
-
-### Risk 2: User Confusion
-**Mitigation:**
-- Clear prompts about what to do next
-- Quick action buttons as fallback
-- Help command to explain current state
-- Visual progress indicator
-
-### Risk 3: Complex State Management
-**Mitigation:**
-- Use proven state management patterns
-- Comprehensive logging
-- Ability to export/import state
-- Rollback functionality
-
-### Risk 4: LLM Cost
-**Mitigation:**
-- Use cheaper models for classification
-- Cache intent results when possible
-- Optimize prompts for token efficiency
-- Rate limiting and budgets
-
----
-
-## Conclusion
-
-This architecture transforms your linear demo builder into an **intelligent conversational agent** that:
-
-1. ✅ **Understands user intent** through natural language
-2. ✅ **Guides users** through the workflow with clear prompts
-3. ✅ **Requires approval** at key decision points
-4. ✅ **Enables refinement** without full regeneration
-5. ✅ **Handles errors** gracefully with recovery options
-6. ✅ **Maintains context** across the conversation
-7. ✅ **Streams responses** for better UX
-
-The migration can be done **incrementally** without breaking existing functionality, and the modular design allows for **easy extension** to new stages (site, bot creation) in the future.
-
-**Next Steps:**
-1. Review this plan and prioritize features
-2. Create detailed tickets for Phase 1
-3. Set up development branch
-4. Start with intent_classifier.py and basic chat UI
-5. Iterate based on user feedback
-
----
-
-**Questions for Discussion:**
-1. Which stages should require approval vs auto-advance?
-2. Should we use a separate LLM for intent vs content generation?
-3. Do you want to keep the button UI as an option or fully migrate to chat?
-4. What's the priority: refinement capability or new stages (site/bot)?
-5. Any specific visualization refinement features you want?
-

CHAT_INTERFACE_GUIDE.md

@@ -1,321 +0,0 @@
-# Chat Interface Quick Guide
-## New Conversational Demo Builder
-
----
-
-## 🚀 Quick Start
-
-### Run the Chat Interface
-
-```bash
-cd /Users/mike.boone/cursor_demowire/DemoPrep
-source venv/bin/activate
-python launch_chat.py
-```
-
-The interface will open at: **http://localhost:7861**
-
----
-
-## 🎯 Interface Overview
-
-### Layout
-
-```
-┌─────────────────────────────────────────────────────────┐
-│          ThoughtSpot Demo Builder - Chat                │
-├──────────────────────────────────┬──────────────────────┤
-│                                  │  📊 Current Status   │
-│  💬 Chat Conversation            │  ┌─────────────────┐ │
-│  ┌────────────────────────────┐  │  │ Stage: Init     │ │
-│  │ 🤖: Welcome! I'm creating  │  │  │ (read-only)     │ │
-│  │     a perfect demo for...  │  │  └─────────────────┘ │
-│  │                            │  │  ┌─────────────────┐ │
-│  │ You: Start research        │  │  │ Model: claude ▼ │ │
-│  │                            │  │  │ (editable)      │ │
-│  │ 🤖: Starting research...   │  │  └─────────────────┘ │
-│  └────────────────────────────┘  │                      │
-│                                  │  🎯 Demo Settings    │
-│  ┌────────────────────────────┐  │  Company: Amazon    │
-│  │ Type your message...       │  │  Use Case: Supply   │
-│  └────────────────────────────┘  │                      │
-│  [🔍 Start] [⚙️ Config] [💡 Help] │  📈 Progress         │
-│                                  │  ⚪ Research         │
-└──────────────────────────────────┴──────────────────────┘
-```
-
----
-
-## 💬 How to Use
-
-### 1. Starting Message
-
-When you open the interface, you'll see:
-
-```
-👋 Welcome to ThoughtSpot Demo Builder!
-
-I am creating a perfect ThoughtSpot demo for [company] 
-using use case: [use case]
-
-What would you like to do?
-```
-
-### 2. Override Settings with `/over`
-
-Change company or use case on the fly:
-
-**Change Company:**
-```
-/over company: Amazon.com
-```
-
-**Change Use Case:**
-```
-/over usecase: supply chain analytics
-```
-
-**Change Both:**
-```
-/over company: Nike.com usecase: retail analytics
-```
-
-### 3. Natural Conversation
-
-Just type naturally:
-
-```
-You: Start research on this company
-AI: 🔍 Starting research...
-
-You: What stage are we at?
-AI: 📊 Current stage is Research...
-
-You: Help
-AI: 💡 Here's what you can do...
-```
-
----
-
-## 🎮 Quick Action Buttons
-
-Three buttons for common actions:
-
-- **🔍 Start Research** - Begin demo creation
-- **⚙️ Configure** - Adjust settings
-- **💡 Help** - Show available commands
-
----
-
-## 🎨 Key Features
-
-### ✅ Stage Display (Read-Only)
-- Shows current stage in workflow
-- Located in right panel
-- Cannot be edited (controlled by workflow)
-- Updates automatically as you progress
-
-### ✅ Model Selector (Editable)
-- Choose AI model from dropdown
-- Options:
-  - `claude-sonnet-4.5` (recommended)
-  - `gpt-4o`
-  - `gpt-4o-mini`
-  - `gemini-1.5-pro`
-- Changes take effect immediately
-
-### ✅ Settings Display
-- Shows current company
-- Shows current use case
-- Read-only (use `/over` to change)
-
-### ✅ Progress Tracker
-- Visual indicator of all stages
-- Shows completed stages with ✅
-- Current stage with 🔵
-- Upcoming stages with ⚪
-
----
-
-## 📝 Command Reference
-
-### Special Commands
-
-| Command | Description | Example |
-|---------|-------------|---------|
-| `/over company: [name]` | Change company | `/over company: Amazon` |
-| `/over usecase: [case]` | Change use case | `/over usecase: supply chain` |
-| `/over company: [name] usecase: [case]` | Change both | `/over company: Nike usecase: retail` |
-
-### Natural Language
-
-| What to Say | What It Does |
-|-------------|--------------|
-| "Start research" | Begin research phase |
-| "Configure" | Show settings options |
-| "Help" | Show available commands |
-| "What stage?" | Show current progress |
-| "Status" | Show full status |
-
----
-
-## 🆚 Comparison: Chat vs Classic UI
-
-| Feature | Chat Interface | Classic Interface |
-|---------|----------------|-------------------|
-| **Input Method** | Natural language | Buttons & forms |
-| **Settings** | `/over` command | Input fields |
-| **Stage Display** | Visible sidebar | Button text |
-| **Model Selection** | Dropdown (always visible) | Dropdown in form |
-| **Progress** | Visual tracker | Step-by-step |
-| **Quick Actions** | Button shortcuts | N/A |
-| **Learning Curve** | Low (conversational) | Medium (structured) |
-
----
-
-## 🎯 Example Workflow
-
-### Complete Demo Creation
-
-```
-1. Open interface
-   → See welcome message with current settings
-
-2. Override if needed
-   You: /over company: Amazon.com usecase: supply chain
-   AI: ✅ Settings updated!
-
-3. Start research
-   You: Start research
-   AI: 🔍 Starting research phase...
-       [Research results stream here]
-       
-4. Continue with conversation
-   You: That looks good, continue
-   AI: ✅ Research complete! Moving to DDL...
-   
-5. Keep conversing through each stage
-   [Continue naturally through the workflow]
-```
-
----
-
-## 🔧 Configuration
-
-### Default Settings
-
-The interface loads settings from:
-1. **Supabase** (if configured)
-2. **Environment variables** (fallback)
-3. **Hard-coded defaults** (last resort)
-
-### Setting Defaults
-
-Edit your `.env` file:
-
-```env
-USER_EMAIL=your.email@example.com
-DEFAULT_COMPANY=Amazon.com
-DEFAULT_USE_CASE=supply chain analytics
-DEFAULT_AI_MODEL=claude-sonnet-4.5
-```
-
----
-
-## 🚨 Troubleshooting
-
-### Interface Won't Load
-
-```bash
-# Check if port 7861 is in use
-lsof -ti:7861
-
-# Kill existing process
-lsof -ti:7861 | xargs kill -9
-
-# Try again
-python launch_chat.py
-```
-
-### Settings Not Loading
-
-```bash
-# Check .env file exists
-ls -la .env
-
-# Check environment variables
-python -c "from dotenv import load_dotenv; load_dotenv(); import os; print(os.getenv('USER_EMAIL'))"
-```
-
-### Model Not Working
-
-- Check API keys in `.env`
-- Try a different model from dropdown
-- Check console for error messages
-
----
-
-## 💡 Tips & Tricks
-
-### 1. Use Quick Buttons
-Don't remember the command? Use the quick action buttons!
-
-### 2. Watch the Stage
-Keep an eye on the stage indicator to know where you are
-
-### 3. Change Model Anytime
-Dropdown is always accessible - change mid-workflow if needed
-
-### 4. Natural Language Works
-Don't worry about exact commands - just chat naturally
-
-### 5. Override is Powerful
-Use `/over` anytime to pivot to a different company or use case
-
----
-
-## 🎓 Next Steps
-
-### Phase 1 (Current)
-- ✅ Chat interface
-- ✅ `/over` command
-- ✅ Stage display
-- ✅ Model selector
-- ⬜ Connect to actual workflow execution
-
-### Phase 2 (Coming Soon)
-- ⬜ Approval gates
-- ⬜ Real-time workflow execution
-- ⬜ Streaming research results
-- ⬜ Progress updates
-
-### Phase 3 (Future)
-- ⬜ Refinement capability
-- ⬜ Undo/redo
-- ⬜ Save conversation
-- ⬜ Export demo
-
----
-
-## 📞 Support
-
-**Issues?** Check the logs in the terminal where you ran `launch_chat.py`
-
-**Questions?** Refer to the main documentation:
-- `START_HERE.md` - Overview
-- `IMPLEMENTATION_ROADMAP.md` - Development guide
-- `CONVERSATION_PATTERNS.md` - UX patterns
-
----
-
-## 🎉 That's It!
-
-You now have a clean, conversational interface for creating ThoughtSpot demos!
-
-**Try it out:**
-```bash
-python launch_chat.py
-```
-
-**Happy demo building! 🚀**
-

CHAT_TRANSFORMATION_README.md

@@ -1,558 +0,0 @@
-# Chat Transformation Documentation
-## Complete Guide to AI-Centric Demo Builder
-
-**Last Updated:** November 12, 2025
-
----
-
-## 🗺️ Documentation Map
-
-We've created a comprehensive transformation plan across 4 documents. **Start here to navigate:**
-
-### 📘 For Executives & Product Managers
-
-**Start with:**
-1. **[TRANSFORMATION_SUMMARY.md](./TRANSFORMATION_SUMMARY.md)** ⭐ START HERE
-   - Big picture overview
-   - Goals and benefits
-   - Timeline and phases
-   - Success metrics
-
-**Then review:**
-2. **[CONVERSATION_PATTERNS.md](./CONVERSATION_PATTERNS.md)**
-   - User experience examples
-   - Conversation flows
-   - UX patterns
-
-**Time investment:** 20 minutes
-
----
-
-### 👨‍💻 For Developers & Engineers
-
-**Start with:**
-1. **[IMPLEMENTATION_ROADMAP.md](./IMPLEMENTATION_ROADMAP.md)** ⭐ START HERE
-   - Hands-on implementation guide
-   - Code examples
-   - Phase-by-phase tasks
-   - Testing strategies
-
-**Then review:**
-2. **[CHAT_ARCHITECTURE_PLAN.md](./CHAT_ARCHITECTURE_PLAN.md)**
-   - Detailed technical design
-   - Component specifications
-   - Data models
-   - Migration strategy
-
-**Reference as needed:**
-3. **[CONVERSATION_PATTERNS.md](./CONVERSATION_PATTERNS.md)**
-   - Intent classification examples
-   - Response templates
-
-**Time investment:** 1-2 hours for thorough understanding
-
----
-
-### 🎨 For UX Designers
-
-**Start with:**
-1. **[CONVERSATION_PATTERNS.md](./CONVERSATION_PATTERNS.md)** ⭐ START HERE
-   - All user interaction patterns
-   - Conversation examples
-   - Response formatting
-
-**Then review:**
-2. **[TRANSFORMATION_SUMMARY.md](./TRANSFORMATION_SUMMARY.md)**
-   - User experience transformation section
-   - Before/after comparisons
-
-**Time investment:** 30 minutes
-
----
-
-## 📚 Document Descriptions
-
-### 1. TRANSFORMATION_SUMMARY.md
-**Purpose:** High-level overview and navigation hub  
-**Length:** ~15 pages  
-**Best for:** Understanding the big picture  
-
-**Key Sections:**
-- Executive summary
-- Architecture overview
-- UX transformation
-- Implementation phases
-- Success metrics
-- Open questions
-
-**Read this if:** You need to understand what we're building and why
-
----
-
-### 2. IMPLEMENTATION_ROADMAP.md
-**Purpose:** Practical development guide  
-**Length:** ~20 pages  
-**Best for:** Actually building the system  
-
-**Key Sections:**
-- Quick win (Phase 1) with code
-- Phase-by-phase implementation
-- Code snippets library
-- Testing strategies
-- Troubleshooting
-- Common pitfalls
-
-**Read this if:** You're coding this transformation
-
----
-
-### 3. CHAT_ARCHITECTURE_PLAN.md
-**Purpose:** Comprehensive technical specification  
-**Length:** ~40 pages  
-**Best for:** Understanding system design  
-
-**Key Sections:**
-- Current architecture analysis
-- New chat-based architecture
-- Component design (6 major components)
-- Data models
-- Migration strategy (10 phases)
-- Risk mitigation
-- File structure
-
-**Read this if:** You need technical depth or are making architectural decisions
-
----
-
-### 4. CONVERSATION_PATTERNS.md
-**Purpose:** User interaction catalog  
-**Length:** ~25 pages  
-**Best for:** Designing conversations and UX  
-
-**Key Sections:**
-- 7 intent categories with examples
-- Approval/rejection patterns
-- Refinement patterns
-- Navigation patterns
-- Response templates
-- Quality checklist
-
-**Read this if:** You're designing the conversation flow or training the AI
-
----
-
-## 🚀 Quick Start Guide
-
-### I want to understand the vision
-→ Read **TRANSFORMATION_SUMMARY.md** (15 min)
-
-### I want to start coding
-→ Read **IMPLEMENTATION_ROADMAP.md** Phase 1 (30 min)  
-→ Create `chat/` directory structure  
-→ Implement `intent_classifier.py`  
-→ Test basic chat flow
-
-### I want to understand the architecture
-→ Read **CHAT_ARCHITECTURE_PLAN.md** (1 hour)  
-→ Review component diagrams  
-→ Study data models
-
-### I want to design conversations
-→ Read **CONVERSATION_PATTERNS.md** (30 min)  
-→ Try example conversations  
-→ Design new patterns
-
----
-
-## 🎯 Key Concepts (Glossary)
-
-**Chat Mode** - New conversational interface (vs. existing button mode)
-
-**Intent Classification** - AI determining what user wants from their message
-
-**Approval Gate** - Required checkpoint before advancing to next stage
-
-**Refinement** - Targeted modification without full regeneration
-
-**Stage Executor** - Specialized handler for each workflow stage (Research, DDL, etc.)
-
-**Conversation Controller** - Orchestrator managing workflow and state
-
-**Streaming** - Sending partial results as generated (not waiting for completion)
-
----
-
-## 📊 At a Glance
-
-### Current System
-```
-Button → Research → Auto-advance → Button → DDL → Auto-advance → ...
-```
-- 4 stages (Research, DDL, Population, Deploy)
-- Button-driven linear flow
-- No approval gates
-- Full regeneration only
-
-### Future System
-```
-User: "Create demo for Amazon"
-AI: [Research...] "Approve?"
-User: "Yes"
-AI: [DDL...] "Approve?"
-User: "Add email to customers"
-AI: [Refined...] "Better?"
-User: "Perfect"
-AI: [Population...] "Approve?"
-...
-```
-- 9 stages (+ Viz Refinement, Site Creation, Bot Creation)
-- Chat-driven conversational flow
-- Approval gates at major stages
-- Granular refinement capability
-
----
-
-## 🏗️ Architecture in 30 Seconds
-
-```
-User Input → Intent Classifier → Conversation Controller → Stage Executor → Response
-```
-
-**Intent Classifier** - "What does user want?"  
-**Conversation Controller** - "How do I orchestrate this?"  
-**Stage Executor** - "How do I execute this stage?"  
-**Response Formatter** - "How do I present this?"
-
----
-
-## 📅 Timeline
-
-| Phase | Duration | Goal | Complexity |
-|-------|----------|------|------------|
-| **Phase 1** | 2 weeks | Basic chat foundation | 🟢 Low |
-| **Phase 2** | 2 weeks | Approval gates | 🟡 Medium |
-| **Phase 3** | 2 weeks | Refinement | 🟡 Medium |
-| **Phase 4** | 1 week | Viz refinement | 🟡 Medium |
-| **Phase 5** | 3 weeks | New stages (site/bot) | 🔴 High |
-
-**Total:** ~10 weeks to full implementation
-
-**Quick Win:** Phase 1 in 2 weeks shows working chat interface!
-
----
-
-## 🎓 Learning Path
-
-### Week 1: Foundation
-- [ ] Read TRANSFORMATION_SUMMARY.md
-- [ ] Review existing codebase (demo_prep.py, demo_builder_class.py)
-- [ ] Understand current workflow
-
-### Week 2: Design
-- [ ] Read CHAT_ARCHITECTURE_PLAN.md
-- [ ] Review component designs
-- [ ] Sketch conversation flows
-
-### Weeks 3-4: Phase 1 Implementation
-- [ ] Read IMPLEMENTATION_ROADMAP.md Phase 1
-- [ ] Create chat directory structure
-- [ ] Implement intent classifier (simple rules)
-- [ ] Create chat UI tab
-- [ ] Test basic flow
-
-### Weeks 5-6: Phase 2 Implementation
-- [ ] Read IMPLEMENTATION_ROADMAP.md Phase 2
-- [ ] Implement approval gates
-- [ ] Add approval UI elements
-- [ ] Test approval/rejection flows
-
-### Weeks 7-8: Phase 3 Implementation
-- [ ] Read IMPLEMENTATION_ROADMAP.md Phase 3
-- [ ] Implement DDL refinement
-- [ ] Implement population refinement
-- [ ] Test targeted modifications
-
-### Weeks 9-12: Phases 4-5
-- [ ] Continue with remaining phases
-- [ ] Add new capabilities
-- [ ] Polish UX
-- [ ] Deploy
-
----
-
-## 🧪 Testing Strategy
-
-### Unit Tests
-Test individual components in isolation:
-- Intent classifier accuracy
-- Stage executor functionality
-- Refinement logic
-
-### Integration Tests
-Test components working together:
-- Chat → Intent → Executor flow
-- State management across stages
-- Approval gate enforcement
-
-### End-to-End Tests
-Test complete user workflows:
-- Full demo creation flow
-- Refinement iterations
-- Error recovery
-
-### User Acceptance Testing
-Real users testing:
-- Natural language understanding
-- Conversation quality
-- Time to complete demo
-
-**See IMPLEMENTATION_ROADMAP.md for detailed testing scripts**
-
----
-
-## 🚨 Common Pitfalls (Read This!)
-
-### ❌ Don't rewrite existing code
-✅ **DO:** Wrap existing functions  
-❌ **DON'T:** Duplicate functionality
-
-### ❌ Don't over-engineer Phase 1
-✅ **DO:** Use simple rules for intent classification  
-❌ **DON'T:** Build complex LLM system day 1
-
-### ❌ Don't forget streaming
-✅ **DO:** Use `yield` for all responses  
-❌ **DON'T:** Use `return` and make users wait
-
-### ❌ Don't ignore errors
-✅ **DO:** Try/except everywhere with user-friendly messages  
-❌ **DON'T:** Let exceptions crash the chat
-
-### ❌ Don't break existing functionality
-✅ **DO:** Keep chat in separate modules  
-❌ **DON'T:** Modify shared code that button UI uses
-
-**Full list in IMPLEMENTATION_ROADMAP.md**
-
----
-
-## 📞 Getting Help
-
-### Where to Look
-
-**"How do I implement X?"**  
-→ IMPLEMENTATION_ROADMAP.md
-
-**"What should the architecture look like?"**  
-→ CHAT_ARCHITECTURE_PLAN.md
-
-**"How should the user experience work?"**  
-→ CONVERSATION_PATTERNS.md
-
-**"What's the big picture?"**  
-→ TRANSFORMATION_SUMMARY.md
-
-**"What file should I read?"**  
-→ This document (CHAT_TRANSFORMATION_README.md)
-
----
-
-## 🎯 Success Criteria
-
-You'll know the transformation is successful when:
-
-### Phase 1 (Foundation)
-- [ ] Chat UI loads without errors
-- [ ] User can type natural language
-- [ ] System identifies basic intents
-- [ ] Existing workflow stages execute
-- [ ] Output formatted nicely in chat
-
-### Phase 2 (Approval Gates)
-- [ ] User must approve before advancing
-- [ ] Reject/redo works
-- [ ] Approve/advance works
-- [ ] No auto-advancement
-
-### Phase 3 (Refinement)
-- [ ] DDL changes without full regen
-- [ ] Population changes without full regen
-- [ ] Schema integrity maintained
-- [ ] Multiple refinements possible
-
-### Phases 4-5 (Advanced)
-- [ ] Viz refinement works
-- [ ] Site generation works
-- [ ] Bot creation works
-- [ ] End-to-end flow completes
-
----
-
-## 📊 Metrics to Track
-
-**Technical:**
-- Intent classification accuracy
-- Response time (first token)
-- Error rate
-- System uptime
-
-**User Experience:**
-- Demo completion time
-- Refinement iterations
-- Approval rate
-- User satisfaction score
-
-**Business:**
-- Adoption rate (chat vs button)
-- Time savings per demo
-- Demo quality (win rate)
-- Support ticket reduction
-
-**See TRANSFORMATION_SUMMARY.md for detailed metrics**
-
----
-
-## 🔄 Document Update Process
-
-These documents are living and should be updated as we learn:
-
-1. **After each phase:** Update with lessons learned
-2. **When patterns emerge:** Add to CONVERSATION_PATTERNS.md
-3. **When architecture changes:** Update CHAT_ARCHITECTURE_PLAN.md
-4. **When timelines shift:** Update IMPLEMENTATION_ROADMAP.md
-
-**Document Owner:** [Assign owner here]  
-**Last Review:** 2025-11-12  
-**Next Review:** [Schedule first review]
-
----
-
-## 📖 Appendix: File Structure Preview
-
-```
-DemoPrep/
-├── demo_prep.py                    # Main app (add chat tab here)
-├── demo_builder_class.py           # Existing state management
-│
-├── chat/                           # NEW: Chat components
-│   ├── __init__.py
-│   ├── intent_classifier.py       # Determine user intent
-│   ├── conversation_controller.py # Orchestrate workflow
-│   ├── ui.py                      # Chat UI
-│   └── prompts/                   # Prompt templates
-│
-├── executors/                      # NEW: Stage executors
-│   ├── __init__.py
-│   ├── base.py                    # Base executor class
-│   ├── research_executor.py
-│   ├── ddl_executor.py
-│   ├── population_executor.py
-│   ├── deployment_executor.py
-│   ├── model_executor.py
-│   ├── liveboard_executor.py
-│   ├── visualization_refiner.py   # NEW capability
-│   ├── site_creator.py            # NEW capability
-│   └── bot_creator.py             # NEW capability
-│
-├── tests/
-│   ├── chat/                      # NEW: Chat tests
-│   └── executors/                 # NEW: Executor tests
-│
-├── TRANSFORMATION_SUMMARY.md       # Overview (you are here)
-├── IMPLEMENTATION_ROADMAP.md       # Dev guide
-├── CHAT_ARCHITECTURE_PLAN.md       # Technical spec
-├── CONVERSATION_PATTERNS.md        # UX patterns
-└── CHAT_TRANSFORMATION_README.md   # This navigation guide
-```
-
----
-
-## 🎬 Quick Command Reference
-
-```bash
-# Navigate to project
-cd /Users/mike.boone/cursor_demowire/DemoPrep
-
-# Activate venv
-source venv/bin/activate
-
-# Create new directories
-mkdir -p chat executors tests/chat tests/executors
-
-# Create Phase 1 files
-touch chat/__init__.py chat/intent_classifier.py 
-touch chat/conversation_controller.py chat/ui.py
-
-# Run with chat mode
-python demo_prep.py
-
-# Run tests
-pytest tests/chat/
-
-# Check code quality
-python -m pylint chat/
-```
-
----
-
-## ✅ Checklist: Before You Start Coding
-
-- [ ] Read TRANSFORMATION_SUMMARY.md (understand the vision)
-- [ ] Read IMPLEMENTATION_ROADMAP.md Phase 1 (know what to build)
-- [ ] Review existing demo_prep.py code (understand current system)
-- [ ] Set up development environment (venv, dependencies)
-- [ ] Create chat/ directory structure
-- [ ] Read relevant sections of CHAT_ARCHITECTURE_PLAN.md
-- [ ] Review CONVERSATION_PATTERNS.md examples
-- [ ] Understand approval gate concept
-- [ ] Understand refinement concept
-- [ ] Ready to code! 🚀
-
----
-
-## 🌟 Vision Reminder
-
-We're building a system where:
-
-> **"A sales engineer can create a perfect ThoughtSpot demo by simply chatting with an AI assistant, receiving guidance at every step, approving outputs before they advance, and refining specific aspects without waiting for full regeneration."**
-
-**This transformation makes demos:**
-- ✅ Faster to create (50% time savings)
-- ✅ Higher quality (approval gates)
-- ✅ More iterative (granular refinement)
-- ✅ Easier to use (natural language)
-- ✅ More consistent (AI guidance)
-
----
-
-## 📝 Quick Reference Card
-
-| I want to... | Read this... | Section... |
-|--------------|--------------|------------|
-| Understand the vision | TRANSFORMATION_SUMMARY.md | Executive Summary |
-| Start coding Phase 1 | IMPLEMENTATION_ROADMAP.md | Phase 1 Quick Win |
-| Design a conversation | CONVERSATION_PATTERNS.md | Any pattern section |
-| Understand architecture | CHAT_ARCHITECTURE_PLAN.md | Component Design |
-| See example conversation | CONVERSATION_PATTERNS.md | Example flows |
-| Get code snippets | IMPLEMENTATION_ROADMAP.md | Code Snippets Library |
-| Learn about approval gates | TRANSFORMATION_SUMMARY.md | Approval & Iteration |
-| Understand refinement | IMPLEMENTATION_ROADMAP.md | Phase 3 |
-| See success metrics | TRANSFORMATION_SUMMARY.md | Success Metrics |
-| Troubleshoot | IMPLEMENTATION_ROADMAP.md | Getting Unstuck |
-
----
-
-**Ready to transform the demo builder? Start with TRANSFORMATION_SUMMARY.md!**
-
-**Ready to code? Jump to IMPLEMENTATION_ROADMAP.md Phase 1!**
-
-**Questions? Refer back to this navigation guide!**
-
----
-
-*Let's build the future of demo creation! 🚀*
-
-**Last Updated:** November 12, 2025  
-**Version:** 1.0  
-**Status:** Ready for Implementation
-

CLAUDE.md

@@ -46,10 +46,44 @@ The sprint document has 5 key sections you need to understand:
 
 ### Working Patterns
 - Settings save/load through Supabase - this WORKS when using venv
-- ThoughtSpot deployment uses TML format (not YAML)
+- ThoughtSpot TML IS YAML format (use yaml.dump() not json.dumps())
 - Models have replaced worksheets in modern ThoughtSpot
 - Liveboards should match "golden demo" style (see sprint doc)
 
+### Liveboard Creation - Dual Method System
+
+**PRIMARY GOAL: Both MCP and TML methods must work simultaneously**
+
+Two methods for creating liveboards:
+1. **MCP (Model Context Protocol)** - AI-driven, natural language approach
+   - Default method (USE_MCP_LIVEBOARD=true)
+   - Working well! Fast and easy.
+   - Function: `create_liveboard_from_model_mcp()` line 2006
+
+2. **TML (ThoughtSpot Modeling Language)** - Template-based approach  
+   - Backup method (USE_MCP_LIVEBOARD=false)
+   - Needs KPI fixes
+   - Function: `create_liveboard_from_model()` line 1779
+
+**CRITICAL:** When changing shared code (like AI prompts), test BOTH methods!
+
+See `.cursorrules` file for detailed dual-method documentation.
+
+### Terminology (Important!)
+- **Outliers** = Interesting data points in existing data (works with both methods)
+- **Data Adjuster** = Modifying data values (NOT possible with MCP, needs Snowflake views)
+
+### KPI Requirements for Sparklines
+Both methods need:
+- Time dimension (date column)  
+- Granularity (daily, weekly, monthly, etc.)
+- Example: `[Total_revenue] [Order_date].monthly`
+
+### Golden Demo Structure
+- Uses GROUPS (like tabs) NOT text tiles
+- Groups organize visualizations by theme
+- Brand colors via style_properties
+
 ## Quick Commands
 
 ```bash
@@ -67,37 +101,66 @@ lsof -i :7860
 
 ### Where to Put Files
 
-**tests/** - Real test cases that verify functionality
+**tests/** - Real, reusable test cases only
 - Unit tests for core functions
-- Integration tests for workflows
-- Tests that run as part of CI/CD
-- Example: `test_connection.py`, `test_model_creation.py`
-
-**tests_temp/** - Temporary AI-generated test files
-- Experimental test scripts
-- One-off verification scripts
-- Files that might be deleted after testing
-- DO NOT commit these to git without asking
-
-**dev_notes/** - Documentation and analysis
-- All .md files go here (except README.md and and CLAUDE.md)
+- Integration tests that could be automated
+- Tests you'd run as part of CI/CD
+- Example: `test_connection.py`, `test_deployment_flow.py`
+
+**scratch/** - ALL temporary/experimental/debug files
+- ALL experimental/debug/check/verify/analyze scripts
+- One-off fixes (fix_*.py, adjust_*.py, emergency_*.py)
+- Debug scripts (debug_*.py, check_*.py, verify_*.py)
+- Analysis tools (analyze_*.py, get_*.py, show_*.py)
+- Test files you're experimenting with
+- Backup files (.bak, .bak2)
+- Export/debug .yml/.json files
+- **ANY script that's temporary or one-time use**
+- Think of this as your "junk drawer" for work-in-progress
+- DO NOT commit without cleanup/review
+
+**dev_notes/** - All documentation and presentations
+- All .md files (except README.md and CLAUDE.md in root)
+- Presentation materials (.pptx, .html, .txt slides)
 - Research documents
 - Architecture notes
 - Sprint planning documents
 
-**Root directory** - ONLY essential project files
-- Main application files (demo_prep.py, etc.)
-- Configuration files (.env, requirements.txt)
-- README.md and CLAUDE.md (documentation exceptions - must stay in root)
-- DO NOT create random .py, .yml, .md files in root
+**Root directory** - ONLY essential core application files
+- Main entry points (demo_prep.py, launch_chat.py, thoughtspot_deployer.py)
+- Core interfaces (chat_interface.py, liveboard_creator.py)
+- Utilities (supabase_client.py, schema_utils.py, demo_logger.py, etc.)
+- Configuration (.env, requirements.txt)
+- README.md and CLAUDE.md only
+- DO NOT create random files here without asking
+
+### Simple Decision Tree for New Files
+
+Creating a new file? Ask yourself:
+
+1. **Is it a real test that should be automated?** → `tests/`
+2. **Is it documentation or presentation material?** → `dev_notes/`
+3. **Is it core application code?** → Root (but **ASK FIRST!**)
+4. **Everything else?** → `scratch/`
+   - Debug scripts (check_*, verify_*, analyze_*)
+   - Temporary tests
+   - One-off fixes
+   - Get/show info scripts
+   - Backups
+   - Experiments
+
+### Golden Rule: **When in doubt, PUT IT IN SCRATCH**
+
+It's easy to move a file from scratch → tests or scratch → root later.
+It's annoying to clean up root when it's cluttered.
 
 ### Rules for Creating Files
 
 1. **NEVER create files in root directory without asking**
-2. **Test files ALWAYS go in tests_temp/** unless they're real test cases
+2. **Experimental/debug scripts ALWAYS go in scratch/**
 3. **Documentation ALWAYS goes in dev_notes/**
-4. **Export/debug .yml/.json files go in tests_temp/** (and should be gitignored)
-5. **Ask before creating ANY new file** if unsure where it belongs
+4. **If you're not sure it belongs in tests/, put it in scratch/**
+5. **Real tests only in tests/** - automated, reusable, part of CI/CD
 
 ### When Testing Existing Features
 
@@ -149,5 +212,5 @@ This software is curretnly stored in my repo but will be open sourced and move t
 
 ---
 
-*Last Updated: October 23, 2025*
+*Last Updated: December 10, 2025*
 *This is a living document - update as you learn more about the project*

CONVERSATION_PATTERNS.md

@@ -1,829 +0,0 @@
-# Conversation Patterns & Examples
-## User Intent Recognition Guide
-
-This document catalogs common conversation patterns and how the system should respond.
-
----
-
-## 🎯 Pattern Categories
-
-1. **Initialization** - Starting a new demo
-2. **Approval** - Approving stage outputs
-3. **Rejection** - Requesting redo
-4. **Refinement** - Modifying specific aspects
-5. **Navigation** - Moving between stages
-6. **Information** - Asking questions
-7. **Configuration** - Changing settings
-
----
-
-## 1. Initialization Patterns
-
-### Starting a New Demo
-
-**User Says:**
-- "Create a demo for [company] in [industry/use case]"
-- "I want to build a demo for [company]"
-- "Let's create a [use case] demo for [company]"
-- "Build me a supply chain demo for Amazon"
-- "Demo for Walmart retail analytics"
-
-**System Response:**
-```
-Great! Let me start researching [company] for [use case].
-
-🔍 Analyzing company website...
-📊 Researching [industry] best practices...
-
-[Streaming research results...]
-
-✅ Research complete! I found:
-- [Key findings]
-- [Industry insights]
-- [Relevant data points]
-
-👉 Please review the research. Reply "approve" to proceed or tell me what to adjust.
-```
-
-**State Transition:** `initial` → `researching` → `awaiting_approval[research]`
-
----
-
-### Providing Just Company
-
-**User Says:**
-- "Amazon"
-- "Create demo for Nike.com"
-- "https://walmart.com"
-
-**System Response:**
-```
-I'll create a demo for [company]! What use case should I focus on?
-
-Common options:
-- Supply Chain Analytics
-- Sales Performance
-- Customer Analytics
-- Inventory Management
-- Financial Analytics
-
-Or tell me a custom use case!
-```
-
-**State Transition:** `initial` → `awaiting_use_case`
-
----
-
-### Providing Just Use Case
-
-**User Says:**
-- "Supply chain analytics"
-- "Create a sales demo"
-- "I need customer analytics"
-
-**System Response:**
-```
-Great! [Use case] it is. What company should I create this demo for?
-
-Examples:
-- "Amazon.com"
-- "Nike"
-- "A fictional retail company"
-
-You can provide a URL or company name.
-```
-
-**State Transition:** `initial` → `awaiting_company`
-
----
-
-## 2. Approval Patterns
-
-### Explicit Approval
-
-**User Says:**
-- "Approve"
-- "Approved"
-- "Yes, approve"
-- "This looks good, approve"
-- "👍"
-- "LGTM" (Looks Good To Me)
-- "Perfect, proceed"
-
-**System Response:**
-```
-✅ [Stage name] approved!
-
-Moving to: **[Next Stage]**
-
-[Start next stage or ask if ready to proceed]
-```
-
-**State Transition:** `awaiting_approval[stage]` → `ready_for_next_stage` or `executing_next_stage`
-
----
-
-### Implicit Approval
-
-**User Says:**
-- "Looks good"
-- "Great!"
-- "Perfect"
-- "Yes"
-- "Okay"
-- "Good to go"
-- "Let's move on"
-- "Continue"
-
-**System Response:**
-```
-✅ Great! Considering that an approval.
-
-Moving to: **[Next Stage]**
-
-[Start next stage]
-```
-
-**State Transition:** Same as explicit approval
-
----
-
-### Conditional Approval
-
-**User Says:**
-- "Approve, but can we come back to this later?"
-- "Good enough for now, proceed"
-- "Approve with the understanding that we'll refine later"
-
-**System Response:**
-```
-✅ [Stage name] approved (marked for potential revision)!
-
-Don't worry, you can always ask me to go back and refine this later.
-
-Moving to: **[Next Stage]**
-```
-
-**State Transition:** `awaiting_approval[stage]` → `next_stage` (+ mark stage as revisable)
-
----
-
-## 3. Rejection Patterns
-
-### Simple Rejection
-
-**User Says:**
-- "No"
-- "Reject"
-- "Redo"
-- "Try again"
-- "Not good"
-- "This isn't right"
-- "👎"
-
-**System Response:**
-```
-🔄 Got it, let me redo the [stage name].
-
-To make it better, can you tell me:
-- What specifically didn't you like?
-- What would you like to see instead?
-
-Or just say "redo" and I'll try a different approach.
-```
-
-**State Transition:** `awaiting_approval[stage]` → `awaiting_rejection_details`
-
----
-
-### Rejection with Reason
-
-**User Says:**
-- "No, this doesn't match our use case"
-- "Redo it with more focus on real-time data"
-- "Try again with simpler schema"
-- "This is too complex, simplify it"
-
-**System Response:**
-```
-🔄 Understood. Let me redo [stage name] with:
-- [Extracted requirement 1]
-- [Extracted requirement 2]
-
-[Start regenerating with modifications...]
-```
-
-**State Transition:** `awaiting_approval[stage]` → `executing[stage]` (with modifications)
-
----
-
-## 4. Refinement Patterns
-
-### DDL Refinement
-
-**User Says:**
-- "Add a column [column_name] to [table_name]"
-- "Remove the [table_name] table"
-- "Change [column_name] data type to VARCHAR"
-- "Add an email field to customers"
-- "Make product_id the primary key"
-
-**System Response:**
-```
-🎨 Refining DDL...
-
-Updating: [table_name]
-Change: [description of change]
-
-```sql
-[Updated table DDL]
-```
-
-✅ Updated [table_name]
-
-The full DDL has been updated. Approve?
-```
-
-**State Transition:** `awaiting_approval[ddl]` → `refining[ddl]` → `awaiting_approval[ddl]`
-
----
-
-### Visualization Refinement
-
-**User Says:**
-- "Change visualization 3 to a bar chart"
-- "Make the revenue chart show top 10 instead of top 5"
-- "Add a region filter to the sales viz"
-- "Change the KPI to show year-over-year growth"
-- "Make that chart horizontal"
-
-**System Response:**
-```
-🎨 Refining visualization: **[Viz Name]**
-
-Applying change: [description]
-
-**Updated Visualization:**
-📊 [Viz Name] ([Chart Type])
-- [Key changes]
-
-Better? You can:
-- Approve to keep this change
-- Request more refinements
-- Revert to original
-```
-
-**State Transition:** `awaiting_approval[liveboard]` → `refining[viz]` → `awaiting_approval[liveboard]`
-
----
-
-### Population Code Refinement
-
-**User Says:**
-- "Increase data volume to 10,000 rows"
-- "Add more outliers for churn scenarios"
-- "Make the data more realistic"
-- "Change customer names to be more diverse"
-
-**System Response:**
-```
-🎨 Refining population code...
-
-Updating:
-- [Change 1]
-- [Change 2]
-
-```python
-[Updated relevant section]
-```
-
-✅ Population code updated
-
-This will generate [new data characteristics]. Approve?
-```
-
-**State Transition:** `awaiting_approval[population]` → `refining[population]` → `awaiting_approval[population]`
-
----
-
-## 5. Navigation Patterns
-
-### Skip Ahead
-
-**User Says:**
-- "Skip to deployment"
-- "I already have a DDL, let's deploy"
-- "Can we jump to visualization creation?"
-
-**System Response:**
-```
-⚠️ Jumping to [stage] requires completing:
-- [Missing stage 1]
-- [Missing stage 2]
-
-Would you like me to:
-1. Auto-complete these stages with defaults
-2. Go through each stage quickly
-3. Import existing artifacts (if you have them)
-
-Which would you prefer?
-```
-
-**State Transition:** `current_stage` → `awaiting_skip_confirmation`
-
----
-
-### Go Back
-
-**User Says:**
-- "Go back to DDL"
-- "Let's revisit the research"
-- "Can we redo the schema?"
-- "I want to change the company"
-
-**System Response:**
-```
-📍 Going back to [stage]...
-
-⚠️ Note: This will reset later stages:
-- [Stage that will be reset 1]
-- [Stage that will be reset 2]
-
-Continue? (yes/no)
-```
-
-**State Transition:** `current_stage` → `confirming_rollback` → `previous_stage`
-
----
-
-### Show Progress
-
-**User Says:**
-- "Where are we?"
-- "What's the status?"
-- "What have we completed?"
-- "Show progress"
-
-**System Response:**
-```
-📊 **Demo Progress for [Company] - [Use Case]**
-
-✅ Completed:
-- Research (approved)
-- DDL Creation (approved)
-
-🔵 Current Stage:
-- Population Code (awaiting approval)
-
-⚪ Upcoming:
-- Deployment
-- Model Creation
-- Liveboard Creation
-- Refinement
-
-**Next Action:** Please approve the population code or request changes.
-```
-
-**State Transition:** No change (informational)
-
----
-
-## 6. Information Patterns
-
-### View Current Output
-
-**User Says:**
-- "Show me the DDL"
-- "What did you create?"
-- "Let me see the schema"
-- "Display the current output"
-- "Show tables"
-
-**System Response:**
-```
-Here's the current [artifact]:
-
-```sql
-[DDL or other content]
-```
-
-**Summary:**
-- [Key points]
-- [Statistics]
-
-Want to make changes or approve?
-```
-
-**State Transition:** No change (informational)
-
----
-
-### Explain Something
-
-**User Says:**
-- "Why did you create this table?"
-- "Explain the schema design"
-- "What are the outliers you created?"
-- "How does this work?"
-
-**System Response:**
-```
-Let me explain [topic]:
-
-[Clear explanation with context]
-
-**Key Points:**
-- [Point 1]
-- [Point 2]
-
-**Rationale:**
-[Why this approach was chosen]
-
-Any other questions?
-```
-
-**State Transition:** No change (informational)
-
----
-
-### Ask for Help
-
-**User Says:**
-- "Help"
-- "What can I do?"
-- "What are my options?"
-- "Commands"
-
-**System Response:**
-```
-## 💡 Available Actions
-
-**Current Stage: [Stage Name]**
-
-You can:
-1. ✅ **Approve** - Move to next stage
-2. ❌ **Reject/Redo** - Regenerate this stage
-3. 🎨 **Refine** - Make specific changes (e.g., "add email column")
-4. 📊 **View** - Show current output
-5. ❓ **Explain** - Ask me to explain something
-6. ⚙️ **Configure** - Change settings
-
-**Example Commands:**
-- "Approve"
-- "Add category column to products table"
-- "Show me the DDL"
-- "Why did you create 5 tables?"
-- "Use GPT-4 instead"
-
-What would you like to do?
-```
-
-**State Transition:** No change (informational)
-
----
-
-## 7. Configuration Patterns
-
-### Change LLM
-
-**User Says:**
-- "Use GPT-4"
-- "Switch to Claude"
-- "Change AI model to [model name]"
-- "Use a faster model"
-
-**System Response:**
-```
-⚙️ Switching to [new model]...
-
-✅ Now using: **[Model Name]**
-
-Note: This will apply to future stages. Current stage won't be affected unless you redo it.
-
-Continue with current stage?
-```
-
-**State Transition:** No change (update settings)
-
----
-
-### Change Company/Use Case
-
-**User Says:**
-- "Actually, let's use Nike instead"
-- "Change use case to financial analytics"
-- "Switch company to Adidas"
-
-**System Response:**
-```
-⚠️ Changing [company/use case] will reset the demo.
-
-Current progress will be lost:
-- [Stage 1] ❌
-- [Stage 2] ❌
-
-Are you sure? (yes/no)
-```
-
-**State Transition:** `current_stage` → `confirming_restart` → `initial`
-
----
-
-### Change Data Volume
-
-**User Says:**
-- "Increase data to 50,000 rows"
-- "Make the demo data smaller"
-- "Use more realistic data volumes"
-
-**System Response:**
-```
-⚙️ Updating data volume setting...
-
-**New Configuration:**
-- Data volume: [new amount]
-- Expected rows: ~[estimate]
-
-This will apply when we generate/regenerate the population code.
-
-Current stage: [stage]. Want to apply this now?
-```
-
-**State Transition:** No change (update settings for future use)
-
----
-
-## 🎯 Intent Classification Rules
-
-### Priority Order (when ambiguous)
-
-1. **Approval/Rejection** (if pending approval) - highest priority
-2. **Refinement** (if specific target mentioned)
-3. **Navigation** (if moving to different stage)
-4. **Information** (if question words present)
-5. **Configuration** (if settings mentioned)
-6. **Clarification** (if unclear) - lowest priority
-
-### Confidence Thresholds
-
-- **High (> 0.9)**: Execute immediately
-- **Medium (0.7 - 0.9)**: Execute with confirmation
-- **Low (< 0.7)**: Ask for clarification
-
-### Clarification Examples
-
-**When Intent Unclear:**
-```
-🤔 I'm not sure I understand. Did you want to:
-
-1. Approve the current DDL
-2. Make a specific change to the DDL
-3. Redo the entire DDL
-4. Move to a different stage
-
-Please clarify!
-```
-
-**When Multiple Intents Detected:**
-```
-I think you want to do multiple things:
-
-1. [Intent 1]
-2. [Intent 2]
-
-Which should I do first?
-```
-
----
-
-## 🔀 Complex Conversation Flows
-
-### Multi-Step Refinement
-
-```
-User: "Add email to customers"
-AI: ✅ Added email column. [Shows DDL]
-
-User: "Also add phone number"
-AI: ✅ Added phone number too. [Shows updated DDL]
-
-User: "Actually make phone number required"
-AI: ✅ Updated phone number to NOT NULL. [Shows DDL]
-
-User: "Perfect, approve"
-AI: ✅ DDL approved! Moving to Population Code...
-```
-
-### Refinement After Deployment
-
-```
-[After deployment complete]
-
-User: "Wait, can we change visualization 2 to a line chart?"
-AI: 🎨 Sure! Updating viz 2...
-     ⚠️ This requires redeploying to ThoughtSpot. Continue? (yes/no)
-
-User: "yes"
-AI: 🔄 Redeploying liveboard...
-     ✅ Updated! Visualization 2 is now a line chart.
-```
-
-### Iterative Approval
-
-```
-User: "Create demo for Amazon"
-AI: [Starts research...] "Use case?"
-
-User: "Supply chain"
-AI: [Completes research...] "Approve?"
-
-User: "Actually, can you focus more on last-mile delivery?"
-AI: 🔄 Redoing research with focus on last-mile...
-     [Updated research...] "Better?"
-
-User: "Yes, much better"
-AI: ✅ Approved! Moving to DDL...
-```
-
----
-
-## 📝 Response Templates
-
-### Generic Success
-```
-✅ [Action] completed successfully!
-
-[Details or summary]
-
-👉 [Next action prompt]
-```
-
-### Generic Error
-```
-❌ [Action] failed: [error message]
-
-**Possible causes:**
-- [Cause 1]
-- [Cause 2]
-
-**What to try:**
-1. [Solution 1]
-2. [Solution 2]
-
-Would you like me to try again?
-```
-
-### Generic Clarification
-```
-🤔 I'm not sure I understood correctly.
-
-Did you mean:
-- [Option A]
-- [Option B]
-- Something else (please clarify)
-```
-
-### Generic Confirmation
-```
-⚠️ This action will [consequence].
-
-Are you sure? (yes/no)
-```
-
----
-
-## 🎨 Formatting Guide
-
-### Code Blocks
-- SQL: \`\`\`sql ... \`\`\`
-- Python: \`\`\`python ... \`\`\`
-- JSON: \`\`\`json ... \`\`\`
-
-### Emphasis
-- **Bold** for important actions or names
-- *Italic* for notes or asides
-- `Code font` for technical terms
-
-### Emojis (consistent usage)
-- ✅ Success/approved
-- ❌ Error/rejected
-- ⚠️ Warning
-- 🔄 Redoing/retrying
-- 🎨 Refining
-- 📊 Data/statistics
-- 🔍 Research/analyzing
-- 🏗️ Creating/building
-- 🚀 Deploying
-- 💬 Information/help
-- ⚙️ Configuration
-- 🤔 Clarification needed
-- 👉 Next action prompt
-- 📁 Assets/files
-
----
-
-## 🧪 Testing Conversation Patterns
-
-### Test Script
-
-```python
-def test_conversation_patterns():
-    """Test various conversation patterns"""
-    
-    controller = ConversationController()
-    
-    # Test initialization
-    assert_response_contains(
-        controller.process_message("Create demo for Amazon in supply chain"),
-        ["research", "Amazon", "supply chain"]
-    )
-    
-    # Test approval
-    assert_response_contains(
-        controller.process_message("approve"),
-        ["approved", "DDL"]
-    )
-    
-    # Test refinement
-    assert_response_contains(
-        controller.process_message("add email column to customers"),
-        ["refining", "email", "customers"]
-    )
-    
-    # Test rejection
-    assert_response_contains(
-        controller.process_message("no, redo this"),
-        ["redo", "again"]
-    )
-```
-
----
-
-## 🎯 Conversation Quality Checklist
-
-For each system response, ensure:
-
-- [ ] **Clear**: User knows what happened
-- [ ] **Actionable**: User knows what to do next
-- [ ] **Concise**: Not too verbose
-- [ ] **Formatted**: Uses proper markdown/code blocks
-- [ ] **Contextual**: References previous conversation
-- [ ] **Helpful**: Offers guidance if user seems stuck
-- [ ] **Consistent**: Uses same emojis/format throughout
-
----
-
-## 📊 Analytics & Metrics
-
-Track these conversation metrics:
-
-1. **Intent Classification Accuracy**
-   - % of messages correctly classified
-   - % requiring clarification
-
-2. **User Satisfaction Indicators**
-   - Approval rate (approvals / total stages)
-   - Refinement frequency (refinements / total stages)
-   - Rejection rate (rejections / total stages)
-
-3. **Efficiency Metrics**
-   - Time to first approval
-   - Number of messages per stage
-   - Stages completed per session
-
-4. **Common Patterns**
-   - Most frequent refinement requests
-   - Most common points of confusion
-   - Most popular conversation flows
-
----
-
-## 🔮 Future Conversation Patterns
-
-### Voice Commands (Future)
-```
-User: [Voice] "Add email to customers"
-AI: 🎤 Voice command recognized: "Add email column to customers table"
-    Proceeding...
-```
-
-### Multi-Modal (Future)
-```
-User: [Uploads CSV] "Use this data structure"
-AI: 📁 Analyzing uploaded file...
-    Detected: 5 tables, 43 columns
-    Generating DDL from your schema...
-```
-
-### Proactive Suggestions (Future)
-```
-AI: 💡 I notice you're creating a sales demo. Would you like me to:
-    - Add common sales KPIs automatically?
-    - Include typical sales outliers?
-    - Use industry-standard table names?
-```
-
----
-
-**This is a living document. Update as we discover new patterns!**
-

DEVELOPMENT_NOTES.md

@@ -1,73 +0,0 @@
-# Development Notes & Future Ideas
-## Chat-Based Demo Builder
-
-**Last Updated:** November 12, 2025
-
----
-
-## 🔮 Future Enhancements
-
-### Workflow Order: DDL-First Approach
-
-**Date:** 2025-11-12  
-**Status:** Under Consideration
-
-**Idea:**
-Instead of Research → DDL → Population, consider:
-DDL → Research → Population
-
-**Rationale:**
-- Start with DDL schema definition
-- Once we know the tables/structure, we can do more targeted research
-- Research can then focus on finding data patterns that fit the schema
-- May lead to better alignment between research and actual demo structure
-
-**Questions to Explore:**
-- Does this work for all use cases?
-- How do we guide DDL creation without research context?
-- Could we do: Basic Research → DDL → Detailed Research → Population?
-
-**Next Steps:**
-- Prototype this workflow
-- Test with a few demos
-- Compare quality of output vs current approach
-
----
-
-## 📋 Backlog
-
-### High Priority
-- [ ] Connect chat interface to actual workflow execution
-- [ ] Implement approval gates
-- [ ] Add streaming for real-time feedback
-
-### Medium Priority
-- [ ] Settings page with modern UI
-- [ ] Save/load conversation state
-- [ ] Export demo artifacts
-
-### Low Priority
-- [ ] Voice commands
-- [ ] Multi-modal inputs (upload CSV for schema)
-- [ ] Proactive AI suggestions
-
----
-
-## 🐛 Known Issues
-
-- Supabase integration optional (shows warning)
-- Settings currently load from .env as fallback
-
----
-
-## 💡 Design Decisions Log
-
-### 2025-11-12: Chat Interface Foundation
-- Used Gradio for consistency with existing app
-- Port 7862 to avoid conflicts
-- Kept settings loading simple for MVP
-
----
-
-**Add your notes here as we discover new ideas!**
-

IMPLEMENTATION_ROADMAP.md

@@ -1,773 +0,0 @@
-# Implementation Roadmap: Chat-Based Demo Builder
-## Quick Start Guide for Development
-
----
-
-## 🎯 Vision Summary
-
-Transform this:
-```
-[Button: Start Research] → Auto → [Button: Create DDL] → Auto → [Button: Deploy]
-```
-
-Into this:
-```
-User: "Create a demo for Amazon in supply chain"
-AI: [Research...] "Here's what I found. Approve?"
-User: "Looks good"
-AI: [DDL...] "Created 5 tables. Approve?"
-User: "Add a category column to products"
-AI: "Done! Approve now?"
-User: "Yes"
-AI: [Deploy...] "Demo live! Create visualizations?"
-```
-
----
-
-## 🚀 Quick Win: Phase 1 (Week 1-2)
-
-### Goal
-Basic chat interface that triggers existing workflow (no approval gates yet)
-
-### What to Build
-
-1. **chat/intent_classifier.py** (~200 lines)
-```python
-"""Simple intent classifier to route user messages"""
-
-class SimpleIntentClassifier:
-    """
-    V1: Rule-based classifier (no LLM needed for MVP)
-    """
-    
-    def classify(self, message: str, context: dict) -> str:
-        """
-        Returns one of: 'start_research', 'create_ddl', 'create_population', 
-                       'deploy', 'show_status', 'help'
-        """
-        message_lower = message.lower()
-        
-        # Simple keyword matching for MVP
-        if any(word in message_lower for word in ['research', 'analyze', 'study']):
-            return 'start_research'
-        
-        if any(word in message_lower for word in ['ddl', 'schema', 'tables']):
-            return 'create_ddl'
-        
-        if any(word in message_lower for word in ['populate', 'data', 'generate']):
-            return 'create_population'
-        
-        if any(word in message_lower for word in ['deploy', 'push', 'create']):
-            return 'deploy'
-        
-        if any(word in message_lower for word in ['status', 'progress', 'where']):
-            return 'show_status'
-        
-        return 'help'
-```
-
-2. **chat/conversation_controller.py** (~300 lines)
-```python
-"""Simple controller that bridges chat to existing workflow"""
-
-class ConversationControllerV1:
-    """
-    V1: Simple wrapper around existing DemoBuilder
-    Just adds chat formatting, no approval gates
-    """
-    
-    def __init__(self):
-        self.demo_builder = None
-        self.classifier = SimpleIntentClassifier()
-        self.state = 'initial'  # 'initial', 'researching', 'creating_ddl', etc.
-    
-    async def process_message(self, message: str):
-        """
-        Process user message and yield chat responses
-        """
-        intent = self.classifier.classify(message, {'state': self.state})
-        
-        if intent == 'start_research':
-            # Extract company URL from message
-            url = extract_url(message)
-            use_case = extract_use_case(message) or "analytics"
-            
-            yield "🔍 Starting research...\n\n"
-            
-            # Call existing research code
-            self.demo_builder = DemoBuilder(use_case, url)
-            async for chunk in self.run_research_stage():
-                yield chunk
-            
-            yield "\n\n✅ Research complete! Type 'create ddl' to continue."
-        
-        elif intent == 'create_ddl':
-            yield "🏗️ Creating DDL...\n\n"
-            async for chunk in self.run_ddl_stage():
-                yield chunk
-            yield "\n\n✅ DDL created! Type 'create population' to continue."
-        
-        # ... etc for other intents
-    
-    async def run_research_stage(self):
-        """
-        Wrap existing research code in streaming format
-        """
-        # Call existing progressive_workflow_handler for research stage
-        # Format output for chat
-        pass
-```
-
-3. **chat/ui.py** (~150 lines)
-```python
-"""Gradio chat interface"""
-
-def create_chat_tab():
-    """
-    Add chat tab to existing Gradio app
-    """
-    with gr.Tab("💬 Chat Mode"):
-        gr.Markdown("## AI-Powered Demo Builder")
-        gr.Markdown("Just tell me what you want to create!")
-        
-        chatbot = gr.Chatbot(height=600)
-        msg = gr.Textbox(
-            placeholder="E.g., 'Create a supply chain demo for Amazon.com'",
-            lines=2
-        )
-        
-        controller = gr.State(ConversationControllerV1())
-        
-        async def respond(message, history, ctrl):
-            history.append((message, ""))
-            
-            response = ""
-            async for chunk in ctrl.process_message(message):
-                response += chunk
-                history[-1] = (message, response)
-                yield history, ctrl
-        
-        msg.submit(respond, [msg, chatbot, controller], [chatbot, controller])
-    
-    return chatbot, msg, controller
-```
-
-4. **Update demo_prep.py** (10 lines added)
-```python
-# Add at the end of create_demo_prep_interface()
-
-from chat.ui import create_chat_tab
-
-with gr.Tabs():
-    with gr.Tab("🎛️ Classic Mode"):
-        # ... existing button interface ...
-    
-    # NEW: Add chat tab
-    chat_interface = create_chat_tab()
-
-return interface
-```
-
-### Testing Phase 1
-
-```bash
-# Test basic chat interaction
-python demo_prep.py
-
-# In browser, go to "Chat Mode" tab
-# Type: "Create a demo for Amazon.com in supply chain"
-# Should trigger research stage
-# Should see streaming output in chat
-# Should prompt for next action
-```
-
-**Success Criteria:**
-- ✅ Chat UI appears in new tab
-- ✅ User can type natural language
-- ✅ System identifies intent (research, ddl, etc.)
-- ✅ Existing workflow stages execute
-- ✅ Output formatted nicely in chat
-
----
-
-## 🎯 Phase 2: Approval Gates (Week 3-4)
-
-### What to Build
-
-1. **Add approval state to controller**
-```python
-class ConversationControllerV2:
-    def __init__(self):
-        self.pending_approval = None  # What's waiting for approval
-        self.state = 'initial'
-    
-    async def process_message(self, message: str):
-        # Check if message is approval/rejection
-        if self.pending_approval:
-            if is_approval(message):
-                await self.handle_approval()
-            elif is_rejection(message):
-                await self.handle_rejection()
-            else:
-                yield "⏸️ Please approve or reject the current output first."
-                return
-        
-        # Otherwise, process as intent
-        # ...
-```
-
-2. **Add approval UI elements**
-```python
-def create_chat_tab():
-    # ... existing code ...
-    
-    with gr.Row():
-        approve_btn = gr.Button("✅ Approve", visible=False)
-        reject_btn = gr.Button("❌ Redo", visible=False)
-    
-    # Update visibility based on controller state
-    def update_buttons(ctrl):
-        has_pending = ctrl.pending_approval is not None
-        return gr.update(visible=has_pending), gr.update(visible=has_pending)
-```
-
-3. **Request approval after each stage**
-```python
-async def run_ddl_stage(self):
-    # ... generate DDL ...
-    
-    # Store for approval
-    self.pending_approval = {
-        'stage': 'ddl',
-        'output': ddl_results,
-        'timestamp': datetime.now()
-    }
-    
-    yield "\n\n---\n"
-    yield "👉 **Please review the DDL above.**\n"
-    yield "Reply 'approve' to continue or 'redo' to regenerate.\n"
-```
-
-**Test:** User must approve before moving to next stage
-
----
-
-## 🎨 Phase 3: Refinement (Week 5-6)
-
-### What to Build
-
-1. **DDL Refinement**
-```python
-class DDLRefiner:
-    """Handles targeted DDL modifications"""
-    
-    async def refine(self, current_ddl: str, instruction: str):
-        """
-        E.g., instruction = "add email column to customers table"
-        """
-        # Use LLM to modify specific part
-        prompt = f"""Modify this DDL:
-        {current_ddl}
-        
-        Change requested: {instruction}
-        
-        Return the complete updated DDL.
-        """
-        
-        # Stream modified DDL
-```
-
-2. **Detect refinement intent**
-```python
-def classify(self, message: str, context: dict) -> str:
-    # ... existing code ...
-    
-    # Check for refinement keywords
-    if context.get('pending_approval'):
-        if 'add' in message or 'change' in message or 'modify' in message:
-            return 'refine'
-    
-    # ...
-```
-
-3. **Handle refinement in controller**
-```python
-async def process_message(self, message: str):
-    # ... existing code ...
-    
-    if intent == 'refine' and self.pending_approval:
-        stage = self.pending_approval['stage']
-        
-        if stage == 'ddl':
-            yield "🎨 Refining DDL...\n\n"
-            refined = await self.ddl_refiner.refine(
-                self.pending_approval['output'],
-                message
-            )
-            self.pending_approval['output'] = refined
-            yield "\n\nUpdated! Approve now?"
-```
-
-**Test:** User can say "add category column" and get targeted update
-
----
-
-## 📊 Phase 4: Visualization Refinement (Week 7)
-
-### What to Build
-
-```python
-class VisualizationRefiner:
-    """Refine specific visualizations without regenerating all"""
-    
-    async def refine_viz(self, liveboard: dict, viz_id: int, instruction: str):
-        """
-        E.g., instruction = "change to bar chart" or "add region filter"
-        """
-        current_viz = liveboard['visualizations'][viz_id]
-        
-        # Classify refinement type
-        if 'chart' in instruction or 'type' in instruction:
-            return await self.change_chart_type(current_viz, instruction)
-        elif 'filter' in instruction:
-            return await self.add_filter(current_viz, instruction)
-        elif 'measure' in instruction or 'metric' in instruction:
-            return await self.change_measures(current_viz, instruction)
-```
-
-**Test:** User can say "change viz 3 to bar chart" after liveboard created
-
----
-
-## 🌐 Phase 5: New Stages (Week 8-10)
-
-### Site Creator
-
-```python
-class SiteCreator:
-    """Generate demo website with embedded ThoughtSpot"""
-    
-    async def create_site(self, demo_context: dict):
-        """
-        Generates HTML site with:
-        - Company branding
-        - Embedded liveboard
-        - Demo narrative
-        """
-        html = f"""
-        <!DOCTYPE html>
-        <html>
-        <head>
-            <title>{demo_context['company']} Demo</title>
-            <style>{self.generate_css(demo_context['brand_colors'])}</style>
-        </head>
-        <body>
-            <h1>{demo_context['use_case']} Analytics</h1>
-            
-            <!-- Embedded ThoughtSpot -->
-            <div id="thoughtspot-embed"></div>
-            
-            <script>
-                // ThoughtSpot embed code
-            </script>
-        </body>
-        </html>
-        """
-        return html
-```
-
-### Bot Creator
-
-```python
-class BotCreator:
-    """Generate chatbot config for demo"""
-    
-    async def create_bot(self, demo_context: dict):
-        """
-        Creates bot that can:
-        - Answer questions about the data
-        - Generate ThoughtSpot searches
-        - Explain visualizations
-        """
-        bot_config = {
-            'name': f"{demo_context['company']} Demo Bot",
-            'knowledge_base': self.build_knowledge_base(demo_context),
-            'thoughtspot_connection': demo_context['ts_connection'],
-            'sample_questions': self.generate_sample_questions(demo_context)
-        }
-        return bot_config
-```
-
----
-
-## 📦 Deliverables by Phase
-
-### Phase 1 (Weeks 1-2): Foundation
-- [ ] `chat/intent_classifier.py`
-- [ ] `chat/conversation_controller.py`
-- [ ] `chat/ui.py`
-- [ ] Update `demo_prep.py` with chat tab
-- [ ] Basic intent recognition (rule-based)
-- [ ] Chat triggers existing workflow
-- [ ] Formatted output in chat
-
-### Phase 2 (Weeks 3-4): Approval Gates
-- [ ] Approval state management
-- [ ] Approve/reject buttons in UI
-- [ ] Block advancement without approval
-- [ ] Redo functionality
-- [ ] Tests for approval flow
-
-### Phase 3 (Weeks 5-6): Refinement
-- [ ] DDL refinement
-- [ ] Population code refinement
-- [ ] Intent detection for refinements
-- [ ] Partial regeneration
-- [ ] Tests for refinement accuracy
-
-### Phase 4 (Week 7): Viz Refinement
-- [ ] Visualization refiner class
-- [ ] Chart type changes
-- [ ] Filter modifications
-- [ ] Measure/dimension updates
-- [ ] Tests for viz refinement
-
-### Phase 5 (Weeks 8-10): New Stages
-- [ ] Site creator executor
-- [ ] Bot creator executor
-- [ ] HTML generation
-- [ ] Bot config generation
-- [ ] End-to-end tests
-
----
-
-## 🧪 Testing Strategy
-
-### Unit Tests
-```python
-# test_intent_classifier.py
-def test_research_intent():
-    classifier = SimpleIntentClassifier()
-    assert classifier.classify("research Amazon", {}) == 'start_research'
-    assert classifier.classify("analyze this company", {}) == 'start_research'
-
-def test_approval_intent():
-    classifier = SimpleIntentClassifier()
-    context = {'pending_approval': True}
-    assert classifier.classify("looks good", context) == 'approve'
-    assert classifier.classify("no redo it", context) == 'reject'
-```
-
-### Integration Tests
-```python
-# test_conversation_flow.py
-async def test_full_workflow():
-    controller = ConversationControllerV2()
-    
-    # Start research
-    responses = []
-    async for chunk in controller.process_message("Create demo for Amazon in supply chain"):
-        responses.append(chunk)
-    
-    assert "research" in "".join(responses).lower()
-    assert controller.state == 'awaiting_approval'
-    
-    # Approve
-    async for chunk in controller.process_message("approve"):
-        pass
-    
-    assert controller.state == 'ready_for_ddl'
-```
-
-### Manual Test Script
-```markdown
-1. Open app in browser
-2. Go to Chat Mode tab
-3. Type: "Create a demo for Amazon.com in supply chain"
-4. ✅ Verify research starts
-5. ✅ Verify streaming output appears
-6. ✅ Verify approval prompt appears
-7. Type: "approve"
-8. ✅ Verify advances to DDL
-9. Type: "add category column to products"
-10. ✅ Verify DDL is refined (not fully regenerated)
-11. ✅ Verify updated DDL shown
-12. Type: "approve"
-13. ✅ Verify advances to population
-... continue through all stages
-```
-
----
-
-## 🔧 Development Tips
-
-### 1. Use Existing Code
-Don't rewrite what works! Wrap existing functions:
-
-```python
-# Good: Reuse existing
-async def run_research_stage(self):
-    for result in progressive_workflow_handler(...):
-        yield self.format_for_chat(result)
-
-# Bad: Rewrite from scratch
-async def run_research_stage(self):
-    # ... 200 lines of duplicated code ...
-```
-
-### 2. Start Simple
-Phase 1 doesn't need LLM for intent classification:
-
-```python
-# V1: Simple rules
-if 'research' in message:
-    return 'start_research'
-
-# V2 (later): LLM-based
-intent = await llm.classify(message, context)
-```
-
-### 3. Stream Everything
-Users want to see progress:
-
-```python
-# Good
-async def generate_ddl(self):
-    yield "Creating schema...\n"
-    for chunk in llm.stream(...):
-        yield chunk
-    yield "\n✅ Done!\n"
-
-# Bad
-async def generate_ddl(self):
-    result = llm.complete(...)  # User waits...
-    return result
-```
-
-### 4. Test in Isolation
-Each component should work standalone:
-
-```python
-# Test classifier alone
-classifier = SimpleIntentClassifier()
-assert classifier.classify("research Amazon", {}) == 'start_research'
-
-# Test controller alone (mock LLM)
-controller = ConversationControllerV1()
-controller.llm = MockLLM()
-```
-
----
-
-## 🚨 Common Pitfalls
-
-### Pitfall 1: Over-engineering Phase 1
-**Problem:** Trying to build perfect intent classification from day 1  
-**Solution:** Start with simple rules, add LLM later
-
-### Pitfall 2: Breaking Existing Functionality
-**Problem:** Modifying shared code breaks button UI  
-**Solution:** Keep chat in separate modules, wrap existing code
-
-### Pitfall 3: No Streaming
-**Problem:** User waits 30 seconds with no feedback  
-**Solution:** Yield partial results frequently
-
-### Pitfall 4: Complex State Management
-**Problem:** State gets out of sync between UI and controller  
-**Solution:** Single source of truth (controller), UI just displays
-
-### Pitfall 5: Ignoring Errors
-**Problem:** LLM fails, app crashes  
-**Solution:** Try/except everywhere, graceful error messages
-
----
-
-## 📝 Code Snippets Library
-
-### Extract Company URL from Message
-```python
-import re
-
-def extract_url(message: str) -> Optional[str]:
-    """Extract URL from user message"""
-    # Match various URL formats
-    url_pattern = r'https?://(?:www\.)?[\w\-\.]+\.[\w]{2,}'
-    match = re.search(url_pattern, message)
-    if match:
-        return match.group(0)
-    
-    # Match domain names
-    domain_pattern = r'(?:www\.)?[\w\-]+\.com|\.io|\.net|\.org'
-    match = re.search(domain_pattern, message)
-    if match:
-        return f"https://{match.group(0)}"
-    
-    return None
-```
-
-### Format Output for Chat
-```python
-def format_for_chat(workflow_output: str) -> str:
-    """Convert workflow output to chat-friendly format"""
-    
-    # Add emoji for status
-    output = workflow_output
-    output = output.replace("✅", "✅")  # Already good
-    output = output.replace("ERROR:", "❌ ERROR:")
-    output = output.replace("WARNING:", "⚠️")
-    
-    # Format code blocks
-    if "CREATE TABLE" in output:
-        output = f"```sql\n{output}\n```"
-    elif "import " in output or "def " in output:
-        output = f"```python\n{output}\n```"
-    
-    return output
-```
-
-### Check if Message is Approval
-```python
-def is_approval(message: str) -> bool:
-    """Check if user is approving"""
-    approval_words = [
-        'approve', 'approved', 'yes', 'looks good', 'perfect', 
-        'great', 'proceed', 'continue', 'go ahead', 'lgtm',
-        'ok', 'okay', 'good', '👍', 'yes'
-    ]
-    message_lower = message.lower().strip()
-    return any(word in message_lower for word in approval_words)
-
-def is_rejection(message: str) -> bool:
-    """Check if user is rejecting"""
-    rejection_words = [
-        'no', 'reject', 'redo', 'retry', 'again', 'wrong',
-        'incorrect', 'bad', 'not good', "don't like", '👎'
-    ]
-    message_lower = message.lower().strip()
-    return any(word in message_lower for word in rejection_words)
-```
-
----
-
-## 🎯 Success Metrics
-
-### Phase 1
-- [ ] Chat UI loads without errors
-- [ ] User can complete research via chat
-- [ ] Output formatted readably
-- [ ] No regression in button UI
-
-### Phase 2
-- [ ] User must approve before advancing
-- [ ] Reject → redo works
-- [ ] Approve → advance works
-- [ ] State persists across reloads
-
-### Phase 3
-- [ ] DDL refinement doesn't break schema
-- [ ] Changes are targeted (not full regen)
-- [ ] User can iterate multiple times
-- [ ] Refinement intent detected accurately
-
-### Phase 4
-- [ ] Viz changes reflected in ThoughtSpot
-- [ ] Multiple vizs can be refined independently
-- [ ] Chart type changes work
-- [ ] Filter additions work
-
-### Phase 5
-- [ ] Site HTML generated and valid
-- [ ] Bot config valid and usable
-- [ ] Branding applied correctly
-- [ ] End-to-end flow completes
-
----
-
-## 🆘 Help & Resources
-
-### When Things Break
-
-1. **Check logs**
-```python
-import logging
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-logger.debug(f"Intent classified as: {intent}")
-logger.debug(f"Controller state: {self.state}")
-```
-
-2. **Test in isolation**
-```python
-# Don't test full flow, isolate the issue
-classifier = SimpleIntentClassifier()
-result = classifier.classify("your problematic message", {})
-print(result)  # What did it return?
-```
-
-3. **Use print statements liberally**
-```python
-async def process_message(self, message: str):
-    print(f"[DEBUG] Received: {message}")
-    intent = self.classifier.classify(message, self.context)
-    print(f"[DEBUG] Intent: {intent}")
-    print(f"[DEBUG] State: {self.state}")
-```
-
-### Getting Unstuck
-
-**Problem:** Intent classification not working  
-**Solution:** Add debug output to see what's being classified
-
-**Problem:** Approval state not updating  
-**Solution:** Check if `is_approval()` function detecting your test phrase
-
-**Problem:** Streaming not showing in UI  
-**Solution:** Verify `yield` is used (not `return`)
-
-**Problem:** Error on LLM call  
-**Solution:** Check API key, rate limits, try with mock first
-
----
-
-## 🎬 Quick Start Commands
-
-```bash
-# Set up dev environment
-cd /Users/mike.boone/cursor_demowire/DemoPrep
-source venv/bin/activate
-
-# Create new directory structure
-mkdir -p chat executors tests/chat tests/executors
-
-# Create Phase 1 files
-touch chat/__init__.py
-touch chat/intent_classifier.py
-touch chat/conversation_controller.py
-touch chat/ui.py
-
-# Run with chat mode
-python demo_prep.py
-
-# Run tests
-pytest tests/chat/
-
-# Check for errors
-python -m pylint chat/
-```
-
----
-
-## 📞 Next Steps
-
-1. ✅ Read this roadmap
-2. ✅ Review CHAT_ARCHITECTURE_PLAN.md for detailed design
-3. ⬜ Create `chat/` directory structure
-4. ⬜ Implement Phase 1 (intent classifier + basic chat UI)
-5. ⬜ Test Phase 1 manually
-6. ⬜ Get feedback on UX
-7. ⬜ Move to Phase 2 (approval gates)
-
-**Let's build this! 🚀**
-

MCP_VS_TML_ANALYSIS.md

@@ -1,501 +0,0 @@
-# MCP vs TML Liveboard Creation: Deep Dive Analysis
-
-## Executive Summary
-
-**Goal:** Make MCP liveboard creation produce results as compelling as TML-based creation for realistic demos.
-
-**Current State:**
-- ✅ MCP works functionally (creates liveboards)
-- ❌ MCP creates "ugly" basic visualizations
-- ✅ TML creates beautiful, well-designed visualizations
-- ❌ The two methods are completely disconnected
-
-**Key Finding:** The TML method has YEARS of intelligence built in (outlier parsing, AI visualization generation, chart type inference, smart layouts, color palettes) while MCP just asks generic questions. We need to bring all that intelligence INTO the MCP workflow.
-
----
-
-## Detailed Comparison
-
-### TML Method (The Good One)
-
-#### **Workflow:**
-```
-1. Parse outliers from population script (structured comments)
-2. Use outliers to create targeted visualizations
-   - Extract semantic types (measure_type, dimension_type)
-   - Map to specific chart types (VIZ_TYPE)
-   - Generate ThoughtSpot search queries
-   - Create companion KPIs
-3. If more viz needed, use AI (GPT-4) to generate additional ones
-4. Add styled text tiles for context
-5. Create smart layout based on chart types
-6. Apply color palettes and formatting
-7. Deploy via TML import
-```
-
-#### **Key Intelligence:**
-
-1. **Outlier-Driven Viz** (lines 686-780):
-   - Reads structured comments from population script:
-     ```python
-     # DEMO_OUTLIER: High-Value Customers at Risk
-     # INSIGHT: Top 5 customers (>$50K LTV) showing declining satisfaction
-     # VIZ_TYPE: COLUMN
-     # VIZ_MEASURE_TYPE: customer_lifetime_value, satisfaction_score
-     # VIZ_DIMENSION_TYPES: customer_name, customer_segment
-     # SHOW_ME: Show customers where lifetime_value > 50000 and satisfaction < 3
-     # KPI_METRIC: total_at_risk_revenue
-     # IMPACT: $250K annual revenue at risk
-     # TALKING_POINT: Notice how ThoughtSpot surfaces...
-     ```
-   - Maps semantic types to actual model columns
-   - Generates precise ThoughtSpot search queries
-   - Creates companion KPIs automatically
-   - Infers chart types intelligently (geo detection, etc.)
-
-2. **AI-Driven Generation** (lines 1175-1265):
-   - Uses GPT-4 with context about:
-     - Company name and use case
-     - Available measures, dimensions, dates from model
-   - Generates diverse chart types (KPI, LINE, COLUMN, etc.)
-   - Creates business-friendly titles
-   - Applies time filters appropriately
-   - Ensures chart type diversity
-
-3. **Smart Layout** (lines 782-861):
-   - 12-column grid system
-   - Chart-type-specific sizing:
-     - KPIs: 3 cols × 3 height (compact)
-     - Maps: 12 cols × 7 height (full width)
-     - Scatter: 6 cols × 7 height
-     - Tables: 8 cols × 5 height
-   - Auto-wrapping rows
-   - Text tiles for context
-
-4. **Professional Styling** (lines 886-1003):
-   - Curated color palettes (teal, purple, pink, blue, etc.)
-   - KPI-specific: sparklines, comparisons, anomalies
-   - Geo maps: gradient heat maps
-   - Stacked charts: diverse region colors
-   - Chart-specific configurations
-
-5. **Text Tiles** (lines 1363-1383):
-   - Adds context tiles with markdown
-   - Colored backgrounds (#2E3D4D, #85016b)
-   - Dashboard overview, key insights
-
----
-
-### MCP Method (The Current One)
-
-#### **Workflow:**
-```
-1. Call getRelevantQuestions with use_case (e.g., "sales analytics")
-2. Get back 5 generic questions
-3. Call getAnswer for each question
-4. Send all answers to createLiveboard
-5. Done (no control over viz types, layout, styling)
-```
-
-#### **What It Does:**
-- Lines 1696-1710: Calls `getRelevantQuestions` with simple query
-- Lines 1726-1753: Gets answers for questions
-- Lines 1775-1798: Creates basic HTML note tile
-- Lines 1815-1819: Calls `createLiveboard` with answers
-
-#### **What It DOESN'T Do:**
-- ❌ No outlier awareness
-- ❌ No chart type control
-- ❌ No layout control
-- ❌ No color/styling control
-- ❌ No AI-driven question generation
-- ❌ No companion KPIs
-- ❌ No text tiles/context
-- ❌ Generic questions not tied to actual data patterns
-
----
-
-## The Problem
-
-**MCP delegates everything to ThoughtSpot's AI**, which:
-- Generates generic questions ("What is total sales?")
-- Creates default visualizations
-- Uses default layout
-- Applies basic styling
-
-**Result:** Functional but not demo-worthy.
-
----
-
-## The Solution: Hybrid Intelligent MCP
-
-### Strategy: Bring TML Intelligence into MCP
-
-Instead of asking MCP generic questions, we:
-1. ✅ **Use outliers** to generate targeted questions
-2. ✅ **Use AI (GPT-4)** to generate additional smart questions
-3. ✅ **Specify chart types** in questions (if MCP supports it)
-4. ✅ **Create text tiles** separately or in note tile
-5. ⚠️ **Layout** - may be limited by MCP API
-
-### Implementation Plan
-
-#### **Phase 1: Outlier Integration** (HIGHEST IMPACT)
-
-**Goal:** Feed outlier-driven questions to MCP instead of generic ones.
-
-**Changes to `create_liveboard_from_model_mcp()`:**
-
-```python
-def create_liveboard_from_model_mcp(
-    ts_client,
-    model_id: str,
-    model_name: str,
-    company_data: Dict,
-    use_case: str,
-    num_visualizations: int = 6,
-    liveboard_name: str = None,
-    outliers: Optional[List[Dict]] = None  # ← ADD THIS
-) -> Dict:
-```
-
-**New Logic:**
-```python
-# BEFORE calling getRelevantQuestions:
-
-questions_to_ask = []
-
-# 1. If we have outliers, use them first
-if outliers:
-    for outlier in outliers:
-        # Convert outlier to MCP-friendly question
-        question = _convert_outlier_to_question(outlier)
-        questions_to_ask.append(question)
-        
-        # Add companion KPI if specified
-        if outlier.get('kpi_companion'):
-            kpi_question = _create_kpi_question(outlier)
-            questions_to_ask.append(kpi_question)
-
-# 2. If we need more questions, use AI to generate them
-remaining = num_visualizations - len(questions_to_ask)
-if remaining > 0:
-    ai_questions = _generate_smart_questions_with_ai(
-        company_data, use_case, model_columns, remaining
-    )
-    questions_to_ask.extend(ai_questions)
-
-# 3. Use MCP getAnswer directly with our smart questions
-answers = []
-for question in questions_to_ask:
-    answer = await session.call_tool("getAnswer", {
-        "question": question,
-        "datasourceId": model_id
-    })
-    answers.append(answer)
-
-# 4. Create liveboard (MCP controls viz types, but at least questions are smart)
-```
-
-**Helper Functions Needed:**
-```python
-def _convert_outlier_to_question(outlier: Dict) -> str:
-    """
-    Convert outlier metadata to natural language question for MCP.
-    
-    Input: {
-        'title': 'High-Value Customers at Risk',
-        'show_me_query': 'Show customers where lifetime_value > 50000 and satisfaction < 3',
-        'viz_type': 'COLUMN',
-        'viz_measure_types': 'customer_lifetime_value, satisfaction_score',
-        'viz_dimension_types': 'customer_name'
-    }
-    
-    Output: "Show me customers with lifetime value greater than 50000 and satisfaction less than 3"
-    """
-    # Parse SHOW_ME query into natural language
-    # Remove quotes, standardize
-    return outlier['show_me_query'].replace('"', '').replace("'", '')
-
-def _create_kpi_question(outlier: Dict) -> str:
-    """Create companion KPI question"""
-    # Example: "What is the total revenue for high-value at-risk customers?"
-    kpi_metric = outlier.get('kpi_metric', '')
-    return f"What is the {kpi_metric}?"
-
-def _generate_smart_questions_with_AI(
-    company_data: Dict,
-    use_case: str,
-    model_columns: List[Dict],
-    num_questions: int
-) -> List[str]:
-    """
-    Use GPT-4 to generate smart, targeted questions
-    (Similar to generate_visualizations_from_research but for questions)
-    """
-    # Extract measures, dimensions from model_columns
-    # Prompt GPT-4: "Generate X business questions for [company] [use_case]"
-    # Return natural language questions
-```
-
----
-
-#### **Phase 2: Enhanced Note Tile** (QUICK WIN)
-
-**Current:** Basic HTML gradient box with generic text.
-
-**Better:** Rich dashboard header with:
-- Company logo (if available)
-- Use case-specific insights
-- Key metrics summary
-- Outlier highlights
-
-```python
-def _create_rich_note_tile(
-    company_data: Dict,
-    use_case: str,
-    outliers: List[Dict],
-    num_viz: int
-) -> str:
-    """Create compelling dashboard header"""
-    
-    outlier_highlights = ""
-    if outliers:
-        outlier_highlights = "<h3>🎯 Strategic Insights</h3><ul>"
-        for outlier in outliers[:3]:  # Top 3
-            outlier_highlights += f"<li><strong>{outlier['title']}</strong>: {outlier['insight']}</li>"
-        outlier_highlights += "</ul>"
-    
-    return f"""
-    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
-                padding: 40px; border-radius: 20px; color: white;">
-        <h1 style="margin: 0 0 20px 0; font-size: 40px;">
-            {company_data.get('name', 'Company')} {use_case} Analytics
-        </h1>
-        <p style="font-size: 18px; opacity: 0.9;">
-            {company_data.get('description', 'AI-powered insights and analytics')}
-        </p>
-        {outlier_highlights}
-        <div style="margin-top: 30px; padding: 20px; background: rgba(255,255,255,0.1); 
-                    border-radius: 12px;">
-            <p style="margin: 0; font-size: 14px;">
-                📊 {num_viz} AI-generated visualizations | 
-                🎯 {len(outliers)} strategic outliers | 
-                ⚡ Created with MCP
-            </p>
-        </div>
-    </div>
-    """
-```
-
----
-
-#### **Phase 3: Question Quality Enhancement** (MEDIUM EFFORT)
-
-**Problem:** Generic questions like "What is total sales?" aren't compelling.
-
-**Solution:** Generate questions that:
-- Reference specific time periods ("last quarter", "this year vs last year")
-- Include filters and segments ("top 10 products", "high-value customers")
-- Ask about trends and patterns ("how has X changed over time?")
-- Use business terminology from the use case
-
-**Example Transformation:**
-```
-❌ Generic: "What is total sales?"
-✅ Better: "What are the top 10 products by sales in the last quarter?"
-
-❌ Generic: "Show sales by region"
-✅ Better: "How do sales compare across regions this year versus last year?"
-
-❌ Generic: "What is customer count?"
-✅ Better: "Which customers have lifetime value over $50K but declining satisfaction?"
-```
-
-**Implementation:**
-```python
-def _enhance_question_quality(question: str, use_case: str) -> str:
-    """Add business context and time filters to questions"""
-    
-    # Use GPT-4 to enhance:
-    prompt = f"""
-    Enhance this analytics question for a {use_case} demo:
-    "{question}"
-    
-    Make it more specific by:
-    - Adding time filters (last quarter, this year vs last year, etc.)
-    - Adding top N limits where appropriate
-    - Using business terminology
-    - Making it sound like a real business question
-    
-    Return only the enhanced question, no explanation.
-    """
-    
-    response = openai.chat.completions.create(
-        model="gpt-4o",
-        messages=[{"role": "user", "content": prompt}]
-    )
-    
-    return response.choices[0].message.content.strip()
-```
-
----
-
-#### **Phase 4: Model Column Introspection** (IMPORTANT)
-
-**Problem:** MCP questions should reference actual columns in the model.
-
-**Solution:** Fetch model schema first, use it to generate questions.
-
-```python
-async def _create_mcp_liveboard():
-    # Step 0: Fetch model columns (like TML method does)
-    model_columns = _fetch_model_columns_for_mcp(model_id, ts_client)
-    
-    measures = [col for col in model_columns if col['type'] == 'MEASURE']
-    dimensions = [col for col in model_columns if col['type'] == 'ATTRIBUTE']
-    date_columns = [col for col in model_columns if col['type'] == 'DATE']
-    
-    # Step 1: Generate questions using actual column names
-    questions = _generate_questions_from_outliers_and_columns(
-        outliers, measures, dimensions, date_columns, use_case
-    )
-    
-    # Rest of MCP workflow...
-```
-
----
-
-## Comparison Table
-
-| Feature | TML Method | MCP Method (Current) | MCP Method (Proposed) |
-|---------|-----------|---------------------|---------------------|
-| **Outlier Integration** | ✅ Full support | ❌ None | ✅ Full support |
-| **Question Quality** | ✅ AI-generated, precise | ❌ Generic | ✅ AI-enhanced |
-| **Chart Type Control** | ✅ Full control | ❌ MCP decides | ⚠️ Via questions? |
-| **Layout Control** | ✅ Smart grid | ❌ MCP decides | ⚠️ Limited |
-| **Color/Styling** | ✅ Professional | ❌ Default | ⚠️ Limited |
-| **Text Tiles** | ✅ Multiple styled | ⚠️ One note tile | ✅ Rich note tile |
-| **Companion KPIs** | ✅ Auto-created | ❌ None | ✅ From outliers |
-| **Model Introspection** | ✅ Full schema | ❌ None | ✅ Add it |
-| **Demo Readiness** | ✅ Beautiful | ❌ Basic | ✅ Much better |
-
----
-
-## Recommendations
-
-### **MUST DO (Phase 1 & 2):**
-1. ✅ Add `outliers` parameter to `create_liveboard_from_model_mcp()`
-2. ✅ Convert outliers to targeted questions
-3. ✅ Add AI question generation for additional viz
-4. ✅ Enhance note tile with outlier highlights
-5. ✅ Fetch model schema to reference actual columns
-
-**Estimated Effort:** 4-6 hours
-**Impact:** 🔥🔥🔥 Massive improvement in demo quality
-
-### **SHOULD DO (Phase 3):**
-6. ✅ Add question quality enhancement
-7. ✅ Create helper functions for outlier→question conversion
-8. ✅ Add companion KPI generation
-
-**Estimated Effort:** 2-3 hours
-**Impact:** 🔥🔥 Significant improvement
-
-### **NICE TO HAVE (Phase 4):**
-9. ⚠️ Investigate if MCP supports chart type hints
-10. ⚠️ See if MCP allows layout customization
-11. ⚠️ Test color palette specifications
-
-**Estimated Effort:** 2-4 hours research + implementation
-**Impact:** 🔥 Moderate (depends on MCP API capabilities)
-
----
-
-## Implementation Priority
-
-### **Immediate (Today):**
-```python
-# 1. Update function signature
-def create_liveboard_from_model_mcp(..., outliers=None):
-
-# 2. Add outlier→question conversion
-if outliers:
-    questions = [_outlier_to_question(o) for o in outliers]
-else:
-    # Fall back to generic questions
-    questions = await getRelevantQuestions(use_case)
-
-# 3. Use questions with MCP getAnswer
-```
-
-### **Next (This Week):**
-- Add AI question generation for remaining viz
-- Enhance note tile with outlier highlights
-- Fetch model schema first
-
-### **Future (Nice to Have):**
-- Research MCP API capabilities for styling/layout
-- Add question quality enhancement pass
-- Create comprehensive outlier→viz mapping
-
----
-
-## Key Insight
-
-**The TML method is smart because of the DATA (outliers) and AI (GPT-4), not because of TML itself.**
-
-We can bring the same intelligence to MCP by:
-1. Feeding it outlier-driven questions (not generic ones)
-2. Using AI to generate additional smart questions
-3. Referencing actual model columns
-4. Creating a rich note tile
-
-**MCP can be just as good as TML if we give it the same quality inputs!**
-
----
-
-## Next Steps
-
-1. **Review this document** with stakeholder
-2. **Decide on priority** (recommend: Phase 1 + 2)
-3. **Implement changes** to `create_liveboard_from_model_mcp()`
-4. **Test with real demo** to compare quality
-5. **Iterate** based on results
-
----
-
-## Questions to Explore
-
-1. Does MCP `getAnswer` support chart type hints?
-   - Can we say: "Show X as a bar chart"?
-   
-2. Does MCP `createLiveboard` allow layout specification?
-   - Or does it always auto-layout?
-   
-3. Can we pass multiple note tiles?
-   - Or just one `noteTile` parameter?
-   
-4. Does MCP respect viz ordering?
-   - If we pass answers in a specific order, does it layout in that order?
-
-5. What's the model ID compatibility issue?
-   - Why do newer models not work with MCP?
-   - Is there a model configuration we need to set?
-
----
-
-## Conclusion
-
-**Current State:** MCP creates functional but basic liveboards.
-
-**Root Cause:** We're asking MCP generic questions instead of leveraging the outlier intelligence and AI that makes TML liveboards compelling.
-
-**Solution:** Hybrid approach - use TML's intelligence (outliers, AI, schema introspection) to generate smart questions for MCP.
-
-**Expected Outcome:** MCP liveboards that are just as demo-ready as TML liveboards, with the added benefit of MCP's AI-driven answer generation.
-
-**Time Investment:** ~6-10 hours total for Phase 1-3.
-
-**ROI:** 🎯 Transform "ugly" basic liveboards into compelling demo assets.
-

MCP_liveboard_creation.md

@@ -1,530 +0,0 @@
-# ThoughtSpot MCP Implementation Guide
-
-## Overview
-This document provides a comprehensive guide for implementing ThoughtSpot's Model Context Protocol (MCP) to create automated, AI-driven analytics liveboards.
-
----
-
-## Table of Contents
-1. [What is MCP](#what-is-mcp)
-2. [Architecture](#architecture)
-3. [Prerequisites](#prerequisites)
-4. [Available MCP Tools](#available-mcp-tools)
-5. [Implementation Workflow](#implementation-workflow)
-6. [Code Examples](#code-examples)
-7. [Best Practices](#best-practices)
-8. [Troubleshooting](#troubleshooting)
-
----
-
-## What is MCP
-
-**Model Context Protocol (MCP)** is a standardized protocol that enables AI agents and applications to interact with ThoughtSpot's analytics capabilities programmatically.
-
-### Key Benefits
-- 🤖 **AI-Native**: Designed for AI agents like Claude, ChatGPT, etc.
-- 🔄 **Standardized**: Uses JSON-RPC over stdio (stdin/stdout)
-- 🎯 **Intent-Based**: Converts natural language queries into precise data questions
-- 📊 **End-to-End**: From question generation to liveboard creation
-
-### Communication Method
-- **NOT HTTP/REST** - MCP uses stdio (subprocess communication)
-- Uses `mcp-remote` proxy for OAuth authentication
-- Spawns MCP server as subprocess, communicates via stdin/stdout
-
----
-
-## Architecture
-
-```
-┌─────────────────┐
-│  Your Python    │
-│  Application    │
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│   MCP Python    │
-│      SDK        │
-└────────┬────────┘
-         │ stdio
-         ▼
-┌─────────────────┐
-│   mcp-remote    │
-│  (OAuth Proxy)  │
-└────────┬────────┘
-         │ HTTPS
-         ▼
-┌─────────────────┐
-│  ThoughtSpot    │
-│   MCP Server    │
-└─────────────────┘
-```
-
-### Components
-1. **Your Application**: Python code using MCP SDK
-2. **MCP Python SDK**: Handles stdio client communication
-3. **mcp-remote**: npx package that handles OAuth and proxies requests
-4. **ThoughtSpot MCP Server**: `https://agent.thoughtspot.app/mcp`
-
----
-
-## Prerequisites
-
-### Required Software
-- **Python**: 3.8 or higher
-- **Node.js/NPX**: For running `mcp-remote`
-- **MCP Python SDK**: `pip install mcp`
-
-### Required Credentials
-- ThoughtSpot instance URL (e.g., `se-thoughtspot-cloud.thoughtspot.cloud`)
-- ThoughtSpot username and password (for OAuth)
-- Datasource/Model GUIDs from your ThoughtSpot instance
-
-### Environment Setup
-```bash
-# Install MCP SDK
-pip install mcp
-
-# Verify npx is available
-npx --version
-```
-
----
-
-## Available MCP Tools
-
-ThoughtSpot MCP provides 4 core tools:
-
-### 1. ping
-**Purpose**: Health check to verify connection
-
-**Parameters**: None
-
-**Returns**: "Pong"
-
-**Example**:
-```python
-result = await session.call_tool("ping", {})
-# Returns: "Pong"
-```
-
----
-
-### 2. getRelevantQuestions
-**Purpose**: Convert vague queries into precise, answerable questions based on datasource schema
-
-**Parameters**:
-- `query` (string, **required**): High-level question or task (e.g., "sales performance", "top products")
-- `datasourceIds` (array, **required**): Array of datasource/model GUIDs
-- `additionalContext` (string, optional): Extra context to improve question generation
-
-**Returns**: JSON array of suggested questions
-```json
-{
-  "questions": [
-    {
-      "question": "What is the product with the highest total sales amount?",
-      "datasourceId": "eb600ad2-ad91-4640-819a-f953602bd4c1"
-    }
-  ]
-}
-```
-
-**Use Case**: Turn user's natural language into specific data queries
-
----
-
-### 3. getAnswer
-**Purpose**: Execute a question against ThoughtSpot and retrieve data/visualization
-
-**Parameters**:
-- `question` (string, **required**): The specific question to answer (typically from `getRelevantQuestions`)
-- `datasourceId` (string, **required**): Single datasource/model GUID
-
-**Returns**: JSON with data, metadata, and viewing URL
-```json
-{
-  "data": "CSV formatted data...",
-  "question": "What is the product with the highest total sales amount?",
-  "session_identifier": "uuid",
-  "generation_number": 2,
-  "frame_url": "https://instance.thoughtspot.cloud/#/embed/..."
-}
-```
-
-**Use Case**: Get actual data and visualizations for specific questions
-
----
-
-### 4. createLiveboard
-**Purpose**: Create a ThoughtSpot liveboard (dashboard) with multiple visualizations
-
-**Parameters**:
-- `name` (string, **required**): Liveboard title
-- `answers` (array, **required**): Array of answer objects from `getAnswer` calls
-- `noteTile` (string, **required**): HTML content for summary/note tile
-
-**Returns**: Success message with liveboard URL
-```json
-{
-  "message": "Liveboard created successfully",
-  "url": "https://instance.thoughtspot.cloud/#/pinboard/[GUID]"
-}
-```
-
-**Use Case**: Build comprehensive dashboards from multiple analyses
-
----
-
-## Implementation Workflow
-
-### Standard 4-Step Process
-
-```
-1. ping                      → Verify connection
-2. getRelevantQuestions     → Generate data questions
-3. getAnswer (multiple)     → Get data for each question
-4. createLiveboard          → Build dashboard
-```
-
-### Detailed Flow
-
-```python
-# Step 1: Connect and verify
-session = ClientSession(...)
-await session.call_tool("ping", {})
-
-# Step 2: Generate questions
-questions = await session.call_tool("getRelevantQuestions", {
-    "query": "sales performance",
-    "datasourceIds": ["datasource-guid"]
-})
-
-# Step 3: Get answers for each question
-answers = []
-for q in questions:
-    answer = await session.call_tool("getAnswer", {
-        "question": q['question'],
-        "datasourceId": q['datasourceId']
-    })
-    answers.append(answer)
-
-# Step 4: Create liveboard
-liveboard = await session.call_tool("createLiveboard", {
-    "name": "Sales Performance Dashboard",
-    "answers": answers,
-    "noteTile": "<html>...</html>"
-})
-```
-
----
-
-## Code Examples
-
-### Minimal Working Example
-
-```python
-import asyncio
-from mcp import ClientSession, StdioServerParameters
-from mcp.client.stdio import stdio_client
-
-async def create_liveboard():
-    # Configure MCP connection
-    server_params = StdioServerParameters(
-        command="npx",
-        args=["mcp-remote@latest", "https://agent.thoughtspot.app/mcp"]
-    )
-    
-    async with stdio_client(server_params) as (read, write):
-        async with ClientSession(read, write) as session:
-            await session.initialize()
-            
-            # Your datasource GUID
-            datasource_id = "your-datasource-guid-here"
-            
-            # Get relevant questions
-            result = await session.call_tool("getRelevantQuestions", {
-                "query": "top products",
-                "datasourceIds": [datasource_id]
-            })
-            
-            # Parse questions
-            import json
-            data = json.loads(result.content[0].text)
-            questions = data['questions']
-            
-            # Get answer for first question
-            answer_result = await session.call_tool("getAnswer", {
-                "question": questions[0]['question'],
-                "datasourceId": datasource_id
-            })
-            
-            answer_data = json.loads(answer_result.content[0].text)
-            
-            # Create liveboard
-            liveboard_result = await session.call_tool("createLiveboard", {
-                "name": "Product Analysis",
-                "answers": [answer_data],
-                "noteTile": "<h2>Product Analysis</h2><p>Top products by sales</p>"
-            })
-            
-            print(liveboard_result.content[0].text)
-
-asyncio.run(create_liveboard())
-```
-
-### Comprehensive Multi-Visualization Example
-
-```python
-async def create_comprehensive_analysis():
-    server_params = StdioServerParameters(
-        command="npx",
-        args=["mcp-remote@latest", "https://agent.thoughtspot.app/mcp"]
-    )
-    
-    async with stdio_client(server_params) as (read, write):
-        async with ClientSession(read, write) as session:
-            await session.initialize()
-            
-            datasource_id = "your-datasource-guid"
-            
-            # Multiple query perspectives
-            queries = [
-                "top selling products",
-                "sales trends over time",
-                "product performance comparison"
-            ]
-            
-            all_questions = []
-            all_answers = []
-            
-            # Generate questions from multiple angles
-            for query in queries:
-                result = await session.call_tool("getRelevantQuestions", {
-                    "query": query,
-                    "datasourceIds": [datasource_id]
-                })
-                
-                data = json.loads(result.content[0].text)
-                all_questions.extend(data['questions'][:3])  # Top 3 from each
-            
-            # Get answers for all questions
-            for q in all_questions[:10]:  # Limit to 10 visualizations
-                try:
-                    answer = await session.call_tool("getAnswer", {
-                        "question": q['question'],
-                        "datasourceId": datasource_id
-                    })
-                    answer_data = json.loads(answer.content[0].text)
-                    all_answers.append(answer_data)
-                except Exception as e:
-                    print(f"Failed to get answer: {e}")
-            
-            # Create rich liveboard
-            note_tile = """
-            <div style="background: linear-gradient(135deg, #1e3a8a 0%, #3b82f6 100%); 
-                        padding: 40px; border-radius: 20px; color: white;">
-                <h1>📊 Comprehensive Sales Analysis</h1>
-                <div style="background: rgba(255,255,255,0.15); padding: 25px; 
-                            border-radius: 15px; margin: 20px 0;">
-                    <h2>🎯 Executive Summary</h2>
-                    <p>Analysis of product performance across multiple dimensions</p>
-                </div>
-                <div style="margin-top: 20px;">
-                    <h3>🔍 Key Findings</h3>
-                    <ul>
-                        <li>Top product performance metrics</li>
-                        <li>Sales trends and patterns</li>
-                        <li>Comparative analysis across products</li>
-                    </ul>
-                </div>
-            </div>
-            """
-            
-            liveboard = await session.call_tool("createLiveboard", {
-                "name": "📊 Comprehensive Product Analysis",
-                "answers": all_answers,
-                "noteTile": note_tile
-            })
-            
-            return liveboard.content[0].text
-
-asyncio.run(create_comprehensive_analysis())
-```
-
----
-
-## Best Practices
-
-### 1. Query Design
-- ✅ Use broad, natural language queries: "sales performance", "customer trends"
-- ❌ Avoid overly specific SQL-like queries
-- ✅ Let ThoughtSpot's AI interpret the schema
-- ✅ Use multiple query angles for comprehensive analysis
-
-### 2. Error Handling
-```python
-try:
-    answer = await session.call_tool("getAnswer", {...})
-except Exception as e:
-    print(f"Question failed: {str(e)}")
-    # Continue with other questions
-```
-
-### 3. Datasource Selection
-- Use models (joined tables) instead of single tables when possible
-- Models provide richer context for question generation
-- Verify datasource has data before using
-
-### 4. Liveboard Design
-- Include rich HTML note tiles with:
-  - Executive summary
-  - Key findings
-  - Visual styling (gradients, colors, emojis)
-  - Methodology explanation
-- Aim for 7-10 visualizations for comprehensive analysis
-- Group related visualizations together
-
-### 5. Authentication
-- OAuth is handled automatically by `mcp-remote`
-- Browser will open for first-time authentication
-- Subsequent calls reuse the session
-- OAuth server runs on `localhost:9414`
-
----
-
-## Troubleshooting
-
-### Common Issues
-
-#### 1. "No answer found for your query"
-**Cause**: Datasource is empty or question doesn't match schema
-
-**Solution**:
-- Verify datasource has data
-- Use system tables (TS: Search, TS: Database) for testing
-- Try simpler questions first
-
-#### 2. "Expected object, received string" (createLiveboard)
-**Cause**: Passing string instead of parsed JSON object
-
-**Solution**:
-```python
-# ❌ Wrong
-answers = [result.content[0].text]
-
-# ✅ Correct
-import json
-answer_data = json.loads(result.content[0].text)
-answers = [answer_data]
-```
-
-#### 3. Connection timeouts
-**Cause**: Network issues or MCP server unavailable
-
-**Solution**:
-- Test with `ping` first
-- Verify npx is installed: `npx --version`
-- Check ThoughtSpot instance is accessible
-
-#### 4. Authentication loop
-**Cause**: OAuth token expired or not saved
-
-**Solution**:
-- Close browser and restart
-- Clear OAuth cache at `~/.mcp-remote/`
-- Ensure OAuth callback server on 9414 is not blocked
-
----
-
-## Getting Datasource GUIDs
-
-### Method 1: ThoughtSpot UI
-1. Log into ThoughtSpot instance
-2. Navigate to **Data** → **Connections** or **Models**
-3. Click on datasource/model
-4. Copy GUID from URL or details page
-
-### Method 2: REST API
-```python
-import requests
-
-# Authenticate
-auth_url = f"https://{ts_instance}/api/rest/2.0/auth/token/full"
-response = requests.post(auth_url, json={
-    "username": "your_username",
-    "password": "your_password"
-})
-token = response.json()['token']
-
-# List datasources
-search_url = f"https://{ts_instance}/api/rest/2.0/metadata/search"
-response = requests.post(search_url, 
-    headers={"Authorization": f"Bearer {token}"},
-    json={"metadata": [{"type": "LOGICAL_TABLE"}]}
-)
-
-for item in response.json():
-    print(f"{item['metadata_name']}: {item['metadata_id']}")
-```
-
----
-
-## File Structure
-
-Recommended project structure:
-
-```
-project/
-├── mcp/
-│   ├── mcp_working_example.py       # Basic example
-│   ├── test_get_questions.py        # Comprehensive example
-│   ├── list_mcp_tools.py            # Tool documentation
-│   └── get_datasources.py           # Helper to get GUIDs
-├── .env                             # ThoughtSpot credentials
-└── requirements.txt                 # mcp, python-dotenv
-```
-
----
-
-## Environment Variables
-
-```properties
-# .env file
-THOUGHTSPOT_URL=your-instance.thoughtspot.cloud
-THOUGHTSPOT_USERNAME=your_username
-THOUGHTSPOT_PASSWORD=your_password
-```
-
----
-
-## Complete Reference Implementation
-
-See `test_get_questions.py` in this repository for a complete, production-ready implementation with:
-- Multiple query generation
-- Error handling
-- Rich HTML formatting
-- 7+ visualizations
-- Professional liveboard styling
-
----
-
-## Support & Resources
-
-- **ThoughtSpot MCP Server**: https://agent.thoughtspot.app/mcp
-- **MCP Python SDK**: https://github.com/modelcontextprotocol/python-sdk
-- **ThoughtSpot REST API Docs**: https://developers.thoughtspot.com
-
----
-
-## Version History
-
-- **v1.0** (November 2025): Initial implementation guide
-- MCP SDK version: 1.21.1
-- mcp-remote version: 0.1.30
-
----
-
-*Document created: November 14, 2025*  
-*Last updated: November 14, 2025*

POPULATION_FIX_SUMMARY.md

@@ -1,160 +0,0 @@
-# Population Code Generation Fix - Summary
-
-## Problem
-The population code was failing with "unexpected indent" errors on line 75, despite template generating clean code.
-
-## Root Causes Identified
-
-### 1. **Code Modification After Generation**
-- `execute_population_script()` was applying dangerous string replacements to clean template code
-- These replacements (lines 352-381 in demo_prep.py) were breaking indentation
-
-### 2. **Template Logic Bug**
-- Table names were being added to the list BEFORE validating columns
-- This caused function calls to non-existent functions
-- Result: incomplete try/except/finally blocks
-
-### 3. **No Distinction Between Template vs LLM Code**
-- All code was treated the same way
-- Template code doesn't need the safety fixes that LLM code needs
-
-## Solutions Implemented
-
-### Solution 1: Flag System for Code Source ✅
-**Files:** `demo_prep.py`, `chat_interface.py`
-
-- Added `skip_modifications` parameter to `execute_population_script()`
-- Template code now bypasses all dangerous string replacements
-- Only does safe schema name replacement
-- LLM code still gets safety fixes
-
-**Usage:**
-```python
-execute_population_script(code, schema_name, skip_modifications=True)  # For template code
-execute_population_script(code, schema_name, skip_modifications=False) # For LLM code
-```
-
-### Solution 2: Comprehensive Diagnostics ✅
-**Files:** `demo_prep.py`
-
-Saves code at each step to `/tmp/demowire_debug/`:
-- `1_original_code.py` - Code before any modifications
-- `2_after_modifications.py` - After string replacements
-- `3_validated_code.py` - Final validated code
-
-**Benefits:**
-- Easy to see exactly what code is being executed
-- Can debug indentation issues visually
-- Compare before/after modifications
-
-### Solution 3: Bulletproof Template Generator ✅
-**Files:** `chat_interface.py`
-
-Improvements:
-1. **Column Validation Before Table Addition**
-   - Only adds table names after validating it has insertable columns
-   - Prevents orphaned function calls
-
-2. **Better Type Handling**
-   - Handles VARCHAR(n) length specifications
-   - Supports BIGINT, DOUBLE, NUMERIC, BOOLEAN
-   - Auto-detects IDENTITY/AUTOINCREMENT columns
-   - More robust column name filtering
-
-3. **Safety Check**
-   - Raises clear error if no valid tables found
-   - Prevents generation of empty main() functions
-
-### Solution 4: Source Tracking ✅
-**Files:** `chat_interface.py`
-
-- Added `demo_builder.population_code_source` attribute
-- Tracks whether code came from "template" or "llm"
-- All execution paths now check this flag
-
-## Testing
-
-### Debug Scripts Created:
-1. `debug_template_generation.py` - Test template with sample DDL
-2. `debug_execution_modifications.py` - Trace code modifications
-
-### Test Results:
-- Template generates clean, valid Python (59-72 lines)
-- Code compiles successfully before modifications  
-- Modified code only fails when replacements break indentation
-
-## Next Steps
-
-### Completed ✅:
-1. ✅ Fix template approach - make bulletproof
-2. ✅ Stop execute_population_script from modifying template code  
-3. ✅ Add comprehensive diagnostics
-
-### Remaining:
-1. Add hybrid LLM approach as fallback (if template fails)
-2. Test with actual user DDL
-
-## How to Use
-
-### For Template Code:
-```python
-# Generation
-code = interface.get_fallback_population_code(schema_info)
-interface.demo_builder.population_code_source = "template"
-
-# Execution
-success, msg = execute_population_script(
-    code, 
-    schema_name, 
-    skip_modifications=True
-)
-```
-
-### For LLM Code:
-```python
-# Generation (via LLM)
-code = generate_from_llm(...)
-interface.demo_builder.population_code_source = "llm"
-
-# Execution (with safety fixes)
-success, msg = execute_population_script(
-    code, 
-    schema_name,
-    skip_modifications=False
-)
-```
-
-## Debugging
-
-If errors still occur:
-1. Check `/tmp/demowire_debug/` for saved code files
-2. Compare the 3 versions to see what changed
-3. Look for console output showing which path was taken:
-   - "🎯 Template-generated code detected"
-   - "⚠️ LLM-generated code - applying safety fixes"
-
-## Key Files Modified
-
-1. **demo_prep.py**
-   - Lines 302-309: Added `skip_modifications` parameter
-   - Lines 346-355: Added debug file saving
-   - Lines 356-382: Added conditional modification logic
-   - Lines 473-476: Added validated code saving
-
-2. **chat_interface.py**
-   - Line 1251: Added `population_code_source` tracking
-   - Lines 1040-1106: Improved template column/type handling
-   - Lines 1315-1359: Added source checking before execution
-   - Multiple locations: Updated all execute_population_script calls
-
-## Summary
-
-The fix ensures that:
-- ✅ Template code stays clean (no modifications)
-- ✅ LLM code gets safety fixes
-- ✅ All code is saved for debugging
-- ✅ Template handles edge cases better
-- ✅ Clear distinction between code sources
-
-The template approach is now production-ready!
-

START_HERE.md

@@ -1,523 +0,0 @@
-# 🚀 START HERE: Chat Transformation Guide
-## Your 5-Minute Orientation to the AI-Centric Demo Builder
-
----
-
-## 🎯 What Are We Building?
-
-Transforming your ThoughtSpot demo builder from **button-driven** to **conversation-driven**.
-
-### Before ❌
-```
-┌──────────────────────────────────────────┐
-│  [Input: Company URL]                    │
-│  [Input: Use Case]                       │
-│  [Button: Start Research] ────────►      │
-│         ↓ (auto-advance)                 │
-│  [Button: Create DDL] ────────►          │
-│         ↓ (auto-advance)                 │
-│  [Button: Create Population] ────────►   │
-│         ↓ (auto-advance)                 │
-│  [Button: Deploy] ────────►              │
-│         ↓                                │
-│  Done ✓                                  │
-└──────────────────────────────────────────┘
-
-Problems:
-- No control over outputs
-- Can't make small changes
-- No approval before advancing
-- Errors propagate
-```
-
-### After ✅
-```
-┌──────────────────────────────────────────┐
-│  💬 Chat: "Create supply chain demo      │
-│           for Amazon.com"                │
-│         ↓                                │
-│  🤖 AI: [Researches...]                  │
-│         "Here's what I found. Approve?"  │
-│         ↓                                │
-│  💬 You: "Looks good but focus more on   │
-│           last-mile delivery"            │
-│         ↓                                │
-│  🤖 AI: [Refines...] "Better?"           │
-│         ↓                                │
-│  💬 You: "Perfect, approve"              │
-│         ↓                                │
-│  🤖 AI: [Creates DDL...] "5 tables       │
-│         created. Approve?"               │
-│         ↓                                │
-│  💬 You: "Add email to customers"        │
-│         ↓                                │
-│  🤖 AI: [Updates...] "Done! Approve?"    │
-│         ↓                                │
-│  💬 You: "Yes"                           │
-│         ↓                                │
-│  [Continue conversationally...]          │
-└──────────────────────────────────────────┘
-
-Benefits:
-- Full control with approval gates ✅
-- Targeted refinements ✅
-- Natural language interface ✅
-- Guided workflow ✅
-```
-
----
-
-## 📚 Documentation (5 Documents)
-
-### 1️⃣ **START_HERE.md** ← You are here!
-**Purpose:** 5-minute overview  
-**Read this:** Right now
-
-### 2️⃣ **CHAT_TRANSFORMATION_README.md**
-**Purpose:** Navigation hub for all docs  
-**Read this:** Next (5 min)
-
-### 3️⃣ **TRANSFORMATION_SUMMARY.md**
-**Purpose:** Strategic overview  
-**Read this:** If you're a PM/executive (15 min)
-
-### 4️⃣ **IMPLEMENTATION_ROADMAP.md** ⭐ DEVELOPERS START HERE
-**Purpose:** Hands-on coding guide  
-**Read this:** If you're building this (1 hour)
-
-### 5️⃣ **CHAT_ARCHITECTURE_PLAN.md**
-**Purpose:** Deep technical specification  
-**Read this:** For architectural decisions (2 hours)
-
-### 6️⃣ **CONVERSATION_PATTERNS.md**
-**Purpose:** UX patterns and examples  
-**Read this:** For UX design (30 min)
-
----
-
-## 🎯 Your Role? Start Here:
-
-### 👔 I'm a Product Manager / Executive
-1. Read **TRANSFORMATION_SUMMARY.md** (15 min)
-   - Understand goals, timeline, ROI
-2. Review **CONVERSATION_PATTERNS.md** for UX (15 min)
-3. Decide on priorities and timeline
-4. Review open questions in TRANSFORMATION_SUMMARY.md
-
-**Total time:** 30 minutes
-
----
-
-### 👨‍💻 I'm a Developer / Engineer
-1. Read **IMPLEMENTATION_ROADMAP.md** (1 hour)
-   - Focus on Phase 1: Foundation
-   - Review code examples
-   - Understand testing strategy
-2. Skim **CHAT_ARCHITECTURE_PLAN.md** for architecture (30 min)
-3. Start coding Phase 1!
-
-**Total time:** 1.5 hours to start coding
-
----
-
-### 🎨 I'm a UX Designer
-1. Read **CONVERSATION_PATTERNS.md** (30 min)
-   - Study all user interaction patterns
-   - Review conversation examples
-2. Read **TRANSFORMATION_SUMMARY.md** UX section (10 min)
-3. Design conversation flows
-
-**Total time:** 40 minutes
-
----
-
-## 🏗️ Architecture (30 Second Version)
-
-```
-USER MESSAGE
-     ↓
-"Create demo for Amazon"
-     ↓
-INTENT CLASSIFIER ──────► What does user want?
-     ↓                    (approve, reject, refine, advance, info, config)
-CONVERSATION CONTROLLER ► Orchestrate workflow
-     ↓                    Manage state & approvals
-STAGE EXECUTOR ───���─────► Execute specific stage
-     ↓                    (Research, DDL, Population, etc.)
-RESPONSE FORMATTER ─────► Format for chat
-     ↓
-AI RESPONSE
-```
-
-**Key Insight:** Each component has a single responsibility, making the system modular and testable.
-
----
-
-## 🚀 Implementation Timeline
-
-```
-┌─────────────┬──────────────┬─────────────────┬──────────┐
-│ Phase 1     │ Phase 2      │ Phase 3         │ Phase 4-5│
-│ Foundation  │ Approval     │ Refinement      │ New      │
-│ (2 weeks)   │ Gates        │ (2 weeks)       │ Stages   │
-│             │ (2 weeks)    │                 │ (4 weeks)│
-├─────────────┼──────────────┼─────────────────┼──────────┤
-│ • Chat UI   │ • Approval   │ • DDL refine    │ • Viz    │
-│ • Intent    │   state      │ • Population    │   refine │
-│   classify  │ • Approve/   │   refine        │ • Site   │
-│ • Bridge to │   reject     │ • Targeted      │   creator│
-│   existing  │   buttons    │   changes       │ • Bot    │
-│   workflow  │ • Block      │                 │   creator│
-│             │   auto-      │                 │          │
-│             │   advance    │                 │          │
-└─────────────┴──────────────┴─────────────────┴──────────┘
-
-Total: ~10 weeks
-Quick Win: Phase 1 in 2 weeks!
-```
-
----
-
-## ✨ Key Features
-
-### 1. **Natural Language Interface**
-```
-❌ Before: Click buttons, fill forms
-✅ After:  Just chat naturally
-```
-
-### 2. **Approval Gates**
-```
-❌ Before: Auto-advances (can't stop)
-✅ After:  Must approve before advancing
-```
-
-### 3. **Granular Refinement**
-```
-❌ Before: Redo entire DDL
-✅ After:  "Add email column to customers"
-```
-
-### 4. **AI Guidance**
-```
-❌ Before: Must know what to click
-✅ After:  AI guides you through
-```
-
-### 5. **Error Recovery**
-```
-❌ Before: Manual troubleshooting
-✅ After:  AI suggests fixes
-```
-
----
-
-## 🎯 Success Metrics
-
-**User Experience:**
-- 50% faster demo creation ⚡
-- 80% first-time success 🎯
-- 4.5/5 user satisfaction ⭐
-
-**Technical:**
-- 90%+ intent accuracy 🎯
-- <500ms response time ⚡
-- 85%+ test coverage ✅
-
-**Business:**
-- 70% adoption rate 📈
-- 40% fewer support tickets 📉
-- Higher win rates 💰
-
----
-
-## 💡 Core Concepts (Learn These)
-
-### Intent Classification
-AI determines what user wants from their message.
-
-**Example:**
-- "Looks good" → APPROVE intent
-- "Add email column" → REFINE intent
-- "Show me the DDL" → INFO intent
-
-### Approval Gate
-Required checkpoint before advancing.
-
-**Example:**
-```
-AI: "DDL created. Approve?"
-[Blocks here until user responds]
-User: "Approve"
-AI: "Moving to Population..."
-```
-
-### Refinement
-Targeted modification without full regeneration.
-
-**Example:**
-```
-User: "Add email to customers"
-AI: [Modifies just that table, not all DDL]
-```
-
-### Stage Executor
-Specialized handler for each workflow stage.
-
-**Example:**
-- ResearchExecutor - Handles research
-- DDLExecutor - Handles DDL creation
-- PopulationExecutor - Handles data population
-
----
-
-## 🚦 Getting Started Checklist
-
-### Before You Begin
-- [ ] Read this document (5 min) ← You're doing it!
-- [ ] Read CHAT_TRANSFORMATION_README.md (5 min)
-- [ ] Identify your role (PM, Dev, UX)
-- [ ] Read role-specific documentation
-
-### For Developers
-- [ ] Read IMPLEMENTATION_ROADMAP.md Phase 1
-- [ ] Review existing codebase
-- [ ] Set up development environment
-- [ ] Create chat/ directory
-- [ ] Implement Phase 1
-
-### For Product/UX
-- [ ] Read TRANSFORMATION_SUMMARY.md
-- [ ] Review conversation examples
-- [ ] Define success criteria
-- [ ] Plan user testing
-
----
-
-## 🎬 Quick Start (Developers)
-
-```bash
-# 1. Navigate to project
-cd /Users/mike.boone/cursor_demowire/DemoPrep
-
-# 2. Activate virtual environment
-source venv/bin/activate
-
-# 3. Create directory structure
-mkdir -p chat executors tests/chat tests/executors
-
-# 4. Create Phase 1 files
-touch chat/__init__.py
-touch chat/intent_classifier.py
-touch chat/conversation_controller.py
-touch chat/ui.py
-
-# 5. Open IMPLEMENTATION_ROADMAP.md
-# Follow Phase 1 code examples
-
-# 6. Run app
-python demo_prep.py
-
-# 7. Test chat interface
-# Go to browser → Chat Mode tab
-```
-
----
-
-## 🔥 What Makes This Special?
-
-### 1. **Approval Gates = Quality Control**
-Bad outputs can't advance. User stays in control.
-
-### 2. **Refinement = Speed**
-Don't regenerate everything. Just fix what's wrong.
-
-### 3. **Chat = Natural**
-No training needed. Just talk to the AI.
-
-### 4. **Modular = Extensible**
-Easy to add new stages (site, bot, etc.)
-
-### 5. **Streaming = Responsive**
-See progress in real-time, not after it's done.
-
----
-
-## 🎓 5-Minute Tutorial
-
-### Conversation Example
-
-**Step 1: Initialize**
-```
-You:  "Create a supply chain demo for Amazon"
-AI:   🔍 Starting research...
-      [streams findings]
-      ✅ Research complete!
-      👉 Approve or request changes?
-```
-
-**Step 2: Approve**
-```
-You:  "Looks good"
-AI:   ✅ Research approved!
-      🏗️ Creating database schema...
-      [streams DDL]
-      Generated 5 tables. Approve?
-```
-
-**Step 3: Refine**
-```
-You:  "Add category column to products"
-AI:   🎨 Adding category to products...
-      ✅ Updated! Approve?
-```
-
-**Step 4: Continue**
-```
-You:  "Yes"
-AI:   ✅ DDL approved!
-      Moving to population code...
-      [continues...]
-```
-
-**That's it!** Natural conversation, full control.
-
----
-
-## 🎯 Next Steps
-
-### Right Now (5 min)
-1. ✅ Finish reading this document
-2. ⬜ Open **CHAT_TRANSFORMATION_README.md**
-3. ⬜ Find your role-specific path
-
-### Today (30-60 min)
-1. ⬜ Read role-appropriate documentation
-2. ⬜ Understand the architecture
-3. ⬜ Review conversation examples
-
-### This Week
-1. ⬜ Set up development environment
-2. ⬜ Review open questions
-3. ⬜ Make go/no-go decision
-4. ⬜ If go: Start Phase 1 implementation
-
----
-
-## 🤔 Common Questions
-
-**Q: Do we keep the button UI?**  
-A: Yes initially (for safety). Can deprecate later based on adoption.
-
-**Q: How long to see results?**  
-A: Phase 1 (basic chat) works in 2 weeks!
-
-**Q: What if intent classification fails?**  
-A: Fallback to clarification questions. See CONVERSATION_PATTERNS.md.
-
-**Q: Does this replace all existing code?**  
-A: No! We wrap existing functions. Low risk.
-
-**Q: What about LLM costs?**  
-A: Use cheap models for classification, premium for generation.
-
-**Q: Can users still use buttons?**  
-A: Yes! Button UI stays during transition.
-
----
-
-## 🆘 Help & Resources
-
-**Question: "Where do I start coding?"**  
-Answer: IMPLEMENTATION_ROADMAP.md → Phase 1
-
-**Question: "How should conversations work?"**  
-Answer: CONVERSATION_PATTERNS.md → Examples
-
-**Question: "What's the architecture?"**  
-Answer: CHAT_ARCHITECTURE_PLAN.md → Component Design
-
-**Question: "What's the big picture?"**  
-Answer: TRANSFORMATION_SUMMARY.md → Executive Summary
-
-**Question: "I'm confused, what should I read?"**  
-Answer: CHAT_TRANSFORMATION_README.md → Your role path
-
----
-
-## 🎯 TL;DR (Too Long; Didn't Read)
-
-**What:** Transform demo builder to chat interface  
-**Why:** Faster, easier, higher quality demos  
-**How:** 5 phases over 10 weeks  
-**Quick Win:** Basic chat in 2 weeks  
-
-**Key Features:**
-- ✅ Natural language chat
-- ✅ Approval gates for quality
-- ✅ Granular refinement for speed
-- ✅ AI guidance throughout
-
-**Next:** Read CHAT_TRANSFORMATION_README.md (5 min)
-
----
-
-## 📊 Visual Summary
-
-```
-┌─────────────────────────────────────────────────────┐
-│                 TRANSFORMATION                      │
-│                                                     │
-│  FROM: Linear Button Workflow                      │
-│        ❌ No control                               │
-│        ❌ Full regeneration only                   │
-│        ❌ Auto-advances                            │
-│                                                     │
-│  TO:   Conversational AI Workflow                  │
-│        ✅ Approval gates                           │
-│        ✅ Granular refinement                      │
-│        ✅ Natural language                         │
-│        ✅ Error recovery                           │
-│                                                     │
-│  RESULT: 50% faster, higher quality demos          │
-└─────────────────────────────────────────────────────┘
-```
-
----
-
-## 🏁 Ready?
-
-**Your next step depends on your role:**
-
-👔 **Product/Executive?**  
-→ Open **TRANSFORMATION_SUMMARY.md**
-
-👨‍💻 **Developer?**  
-→ Open **IMPLEMENTATION_ROADMAP.md**
-
-🎨 **UX Designer?**  
-→ Open **CONVERSATION_PATTERNS.md**
-
-❓ **Not sure?**  
-→ Open **CHAT_TRANSFORMATION_README.md**
-
----
-
-**Welcome to the future of demo creation! 🚀**
-
-*This will change how demos are made. Let's build it together.*
-
----
-
-**Remember:** Start small (Phase 1), iterate quickly, ship incrementally.
-
-**You've got this!** 💪
-
----
-
-Created: November 12, 2025  
-Version: 1.0  
-Status: Ready for Implementation
-
-**Next → CHAT_TRANSFORMATION_README.md**
-

TRANSFORMATION_SUMMARY.md

@@ -1,574 +0,0 @@
-# Chat-Based Demo Builder: Transformation Summary
-## From Linear Workflow to AI-Centric Conversational System
-
-**Created:** November 12, 2025  
-**Status:** Ready for Implementation
-
----
-
-## 📋 Executive Summary
-
-We're transforming your ThoughtSpot demo preparation tool from a button-driven linear workflow into an **AI-powered conversational assistant** that guides users through demo creation via natural language chat.
-
-### Current State ❌
-- Button-based UI with fixed progression
-- No approval gates - auto-advances through stages
-- Limited iteration capability (full stage redo only)
-- Difficult to make targeted changes
-- Linear: Research → DDL → Population → Deploy
-
-### Future State ✅
-- Natural language chat interface
-- AI interprets user intent and takes appropriate action
-- Approval required at each major stage
-- Granular refinement without regeneration
-- Iterative: Create, review, refine, approve, advance
-- New capabilities: Visualization refinement, site creation, bot creation
-
----
-
-## 🎯 Core Transformation Goals
-
-| Goal | Current | Target | Impact |
-|------|---------|--------|--------|
-| **User Experience** | Button clicks | Natural conversation | ⭐⭐⭐⭐⭐ |
-| **Approval Gates** | None (auto-advance) | Required at each stage | ⭐⭐⭐⭐⭐ |
-| **Refinement** | Full regeneration only | Targeted modifications | ⭐⭐⭐⭐ |
-| **Error Recovery** | Manual | AI-assisted | ⭐⭐⭐ |
-| **Iteration Speed** | Slow (full regen) | Fast (incremental) | ⭐⭐⭐⭐ |
-| **Learning Curve** | Requires training | Self-explanatory | ⭐⭐⭐⭐ |
-
----
-
-## 🏗️ Architecture Overview
-
-### High-Level Flow
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                        USER                                  │
-│           "Create a supply chain demo for Amazon"           │
-└───────────────────────────┬─────────────────────────────────┘
-                           │
-                           ▼
-┌─────────────────────────────────────────────────────────────┐
-│                  INTENT CLASSIFIER                           │
-│     What does the user want? (start, approve, refine...)   │
-└───────────────────────────┬─────────────────────────────────┘
-                           │
-                           ▼
-┌─────────────────────────────────────────────────────────────┐
-│              CONVERSATION CONTROLLER                         │
-│   Maintains state, orchestrates workflow, manages approvals │
-└───────────────────────────┬─────────────────────────────────┘
-                           │
-                           ▼
-┌─────────────────────────────────────────────────────────────┐
-│                  STAGE EXECUTORS                             │
-│  ResearchExecutor │ DDLExecutor │ PopulationExecutor │ ...  │
-└───────────────────────────┬─────────────────────────────────┘
-                           │
-                           ▼
-┌─────────────────────────────────────────────────────────────┐
-│              RESPONSE FORMATTER                              │
-│        Streams formatted chat responses to user              │
-└─────────────────────────────────────────────────────────────┘
-```
-
-### Key Components
-
-1. **Intent Classifier** (`chat/intent_classifier.py`)
-   - Determines what user wants from their message
-   - Types: APPROVE, REJECT, REFINE, ADVANCE, INFO, CONFIGURE
-   - Phase 1: Simple rule-based
-   - Phase 2: LLM-powered with context
-
-2. **Conversation Controller** (`chat/conversation_controller.py`)
-   - Orchestrates entire workflow
-   - Manages stage progression
-   - Enforces approval gates
-   - Tracks conversation history
-   - Routes to appropriate executor
-
-3. **Stage Executors** (`executors/`)
-   - Specialized handlers for each workflow stage
-   - Research, DDL, Population, Deploy, Model, Liveboard, etc.
-   - Each knows how to execute and refine its stage
-   - Reuses existing code where possible
-
-4. **Chat UI** (`chat/ui.py`)
-   - Gradio-based conversational interface
-   - Streaming responses
-   - Quick action buttons (Approve/Reject)
-   - Progress visualization
-   - Asset display
-
----
-
-## 🎨 User Experience Transformation
-
-### Example: Creating a Demo
-
-**BEFORE (Button UI):**
-```
-[Input company URL]
-[Input use case]
-[Click: Start Research]
-... wait ...
-[Auto-advances to DDL]
-[Click: Create DDL]
-... wait ...
-[Auto-advances to Population]
-[If error: manual troubleshooting]
-```
-
-**AFTER (Chat UI):**
-```
-User: Create a supply chain demo for Amazon.com
-
-AI: 🔍 Starting research on Amazon and supply chain analytics...
-    [Streams findings in real-time]
-    ✅ Research complete! 
-    👉 Please review and approve, or tell me what to adjust.
-
-User: Looks good but focus more on last-mile delivery
-
-AI: 🔄 Refining research with focus on last-mile delivery...
-    [Streams updated research]
-    Better?
-
-User: Perfect, approve
-
-AI: ✅ Research approved!
-    🏗️ Creating database schema...
-    [Streams DDL]
-    Generated 5 tables. Approve?
-
-User: Add an email column to the customers table
-
-AI: 🎨 Adding email column to customers...
-    [Shows updated table]
-    ✅ Updated! Approve now?
-
-User: Yes
-
-AI: ✅ DDL approved!
-    [Continues to next stage...]
-```
-
----
-
-## 📊 New Workflow Stages
-
-Expanding beyond the current 4-stage workflow:
-
-| Stage | Current | New Capability |
-|-------|---------|----------------|
-| **Research** | ✅ Exists | ✅ + Approval gate + Refinement |
-| **DDL Creation** | ✅ Exists | ✅ + Approval gate + Targeted edits |
-| **Population Code** | ✅ Exists | ✅ + Approval gate + Parameter tuning |
-| **Deployment** | ✅ Exists | ✅ + Error recovery + Rollback |
-| **Model Creation** | ✅ Exists | ✅ + Approval gate |
-| **Liveboard Creation** | ✅ Exists | ✅ + Approval gate |
-| **Visualization Refinement** | ❌ New | 🆕 Chart type changes, filters, measures |
-| **Site Creation** | ❌ New | 🆕 Generate demo website with branding |
-| **Bot Creation** | ❌ New | 🆕 Create demo chatbot |
-
----
-
-## 🔄 Approval & Iteration Flow
-
-### Approval Gate Pattern
-
-```
-┌──────────────────────────┐
-│    Stage Executes         │
-│  (streams results)        │
-└──────────┬───────────────┘
-           │
-           ▼
-┌──────────────────────────┐
-│  Present Results          │
-│  Request Approval         │
-└──────────┬───────────────┘
-           │
-           ▼
-     ┌─────┴─────┐
-     │           │
-     ▼           ▼
-┌─────────┐  ┌──────────┐
-│ APPROVE │  │ REJECT/  │
-│         │  │ REFINE   │
-└────┬────┘  └────┬─────┘
-     │            │
-     │            ▼
-     │       ┌──────────┐
-     │       │ Re-exec  │
-     │       │ with mods│
-     │       └────┬─────┘
-     │            │
-     │            ▼
-     │       ┌──────────┐
-     │       │ Present  │
-     │       │ Updated  │
-     │       └────┬─────┘
-     │            │
-     └────────────┘
-           │
-           ▼
-    ┌─────────────┐
-    │ Next Stage  │
-    └─────────────┘
-```
-
-### Refinement Pattern
-
-```
-User: "Add email column to customers"
-  ↓
-Classify: REFINE intent
-  ↓
-Extract: target="customers", modification="add email column"
-  ↓
-Route to: DDLExecutor.refine()
-  ↓
-Execute: Targeted modification (not full regen)
-  ↓
-Validate: Check schema integrity
-  ↓
-Present: Updated DDL
-  ↓
-Request: Approval (again)
-```
-
----
-
-## 📁 Documentation Structure
-
-We've created 4 comprehensive documents:
-
-### 1. **CHAT_ARCHITECTURE_PLAN.md** (Main Technical Design)
-- Detailed architecture diagrams
-- Component specifications
-- Data models and state management
-- Migration strategy (10 phases)
-- Risk mitigation
-- Success metrics
-
-**Use this for:** Understanding the full system design and technical decisions
-
-### 2. **IMPLEMENTATION_ROADMAP.md** (Practical Guide)
-- Phase-by-phase implementation steps
-- Code snippets and examples
-- Testing strategies
-- Quick wins (Phase 1 in 2 weeks)
-- Common pitfalls to avoid
-- Help & troubleshooting
-
-**Use this for:** Actually building the system, day-to-day development
-
-### 3. **CONVERSATION_PATTERNS.md** (UX Patterns)
-- User intent categories
-- Example conversations
-- Response templates
-- Clarification strategies
-- Edge case handling
-- Quality checklist
-
-**Use this for:** Designing conversation flows, training intent classifier
-
-### 4. **TRANSFORMATION_SUMMARY.md** (This Document)
-- High-level overview
-- Strategic goals
-- Document roadmap
-- Quick reference
-
-**Use this for:** Understanding the big picture, presenting to stakeholders
-
----
-
-## 🚀 Implementation Phases
-
-### Phase 1: Foundation (Weeks 1-2) ⭐ QUICK WIN
-**Goal:** Basic chat that triggers existing workflow
-
-**Deliverables:**
-- Chat UI in new Gradio tab
-- Simple rule-based intent classification
-- Bridge to existing workflow functions
-- Formatted streaming output
-
-**Complexity:** 🟢 Low  
-**Risk:** 🟢 Low (no changes to existing code)  
-**Value:** 🟡 Medium (proves concept)
-
----
-
-### Phase 2: Approval Gates (Weeks 3-4) ⭐ HIGH VALUE
-**Goal:** Users must approve before advancing
-
-**Deliverables:**
-- Approval state management
-- Approve/reject buttons
-- Block auto-advancement
-- Redo functionality
-
-**Complexity:** 🟡 Medium  
-**Risk:** 🟡 Medium (state management)  
-**Value:** 🟢 High (major UX improvement)
-
----
-
-### Phase 3: Refinement (Weeks 5-6) ⭐ HIGH VALUE
-**Goal:** Targeted modifications without full regeneration
-
-**Deliverables:**
-- DDL refinement (table/column changes)
-- Population refinement (data params)
-- Refinement intent detection
-- Partial regeneration logic
-
-**Complexity:** 🟡 Medium  
-**Risk:** 🟡 Medium (must maintain integrity)  
-**Value:** 🟢 High (huge time saver)
-
----
-
-### Phase 4: Viz Refinement (Week 7)
-**Goal:** Modify visualizations independently
-
-**Deliverables:**
-- Visualization refiner class
-- Chart type changes
-- Filter modifications
-- Measure/dimension updates
-
-**Complexity:** 🟡 Medium  
-**Risk:** 🟢 Low (isolated feature)  
-**Value:** 🟡 Medium (nice to have)
-
----
-
-### Phase 5: New Stages (Weeks 8-10)
-**Goal:** Site and bot creation
-
-**Deliverables:**
-- Site creator (HTML generation)
-- Bot creator (config generation)
-- Branding application
-- End-to-end flow
-
-**Complexity:** 🔴 High  
-**Risk:** 🟡 Medium (new territory)  
-**Value:** 🟢 High (differentiator)
-
----
-
-## 📊 Success Metrics
-
-### User Experience Metrics
-- [ ] **Demo completion time**: Reduce by 40%
-- [ ] **User errors**: Reduce by 60%
-- [ ] **Refinement iterations**: Enable 3+ per stage
-- [ ] **User satisfaction**: > 4.5/5 rating
-- [ ] **First-time success**: 80% complete without help
-
-### Technical Metrics
-- [ ] **Intent accuracy**: > 90% correct classification
-- [ ] **Response time**: < 500ms to first token
-- [ ] **Uptime**: 99.5% availability
-- [ ] **Error recovery**: 80% auto-resolved
-- [ ] **Test coverage**: > 85%
-
-### Business Metrics
-- [ ] **Adoption rate**: 70% of users prefer chat mode
-- [ ] **Demo quality**: Higher win rates
-- [ ] **Time to value**: 50% faster
-- [ ] **Support tickets**: 40% reduction
-
----
-
-## 🎯 Key Design Decisions
-
-### 1. Keep Existing Code
-**Decision:** Wrap existing functions, don't rewrite  
-**Rationale:** Lower risk, faster implementation, proven functionality  
-**Trade-off:** Some technical debt, not perfectly optimized
-
-### 2. Gradio for Chat UI
-**Decision:** Extend existing Gradio app with chat tab  
-**Rationale:** Consistent UX, faster development, user familiarity  
-**Trade-off:** Gradio limitations vs custom UI
-
-### 3. Approval Required for Major Stages
-**Decision:** Research, DDL, Population, Liveboard require approval  
-**Rationale:** Prevent bad outputs from propagating, give user control  
-**Trade-off:** More clicks, but better quality
-
-### 4. Two-Tier Intent Classification
-**Decision:** Phase 1 = rules, Phase 2 = LLM  
-**Rationale:** Quick MVP, then upgrade as needed  
-**Trade-off:** Phase 1 less accurate, but faster/cheaper
-
-### 5. Streaming Everything
-**Decision:** All AI responses stream  
-**Rationale:** Better perceived performance, real-time feedback  
-**Trade-off:** More complex code, state management
-
-### 6. Stage Executors Pattern
-**Decision:** One executor class per stage  
-**Rationale:** Separation of concerns, easier to test, extend  
-**Trade-off:** More files, need consistent interface
-
----
-
-## 🚧 Risks & Mitigation
-
-| Risk | Probability | Impact | Mitigation |
-|------|------------|--------|------------|
-| **Intent misclassification** | High | Medium | Clarification questions, confidence thresholds |
-| **State management bugs** | Medium | High | Comprehensive testing, state snapshots |
-| **LLM API failures** | Medium | High | Retry logic, fallback options, error messages |
-| **User confusion** | Medium | Medium | Clear prompts, help command, tutorials |
-| **Performance degradation** | Low | Medium | Caching, async operations, load testing |
-| **Breaking existing functionality** | Low | High | Separate modules, integration tests |
-
----
-
-## 🎓 Learning Resources
-
-### For Developers
-
-**Must Read:**
-1. IMPLEMENTATION_ROADMAP.md - Start here
-2. Phase 1 code examples - Understand the pattern
-3. Stage executor interface - See how to extend
-
-**Reference:**
-- CHAT_ARCHITECTURE_PLAN.md - Full technical design
-- CONVERSATION_PATTERNS.md - UX patterns
-- Existing codebase - Reuse existing functions
-
-### For Product/UX
-
-**Must Read:**
-1. This document (TRANSFORMATION_SUMMARY.md)
-2. CONVERSATION_PATTERNS.md - All user interactions
-3. Phase 2 approval gates - Key UX improvement
-
-**Reference:**
-- Example conversations in CONVERSATION_PATTERNS.md
-- Success metrics section
-- User experience transformation section
-
----
-
-## 📞 Next Steps
-
-### Immediate (This Week)
-1. ✅ Review all documentation
-2. ⬜ Discuss priorities and timeline
-3. ⬜ Approve Phase 1 scope
-4. ⬜ Set up development environment
-5. ⬜ Create GitHub issues for Phase 1
-
-### Short Term (Weeks 1-2)
-1. ⬜ Implement Phase 1 (foundation)
-2. ⬜ Internal testing and feedback
-3. ⬜ Iterate on conversation patterns
-4. ⬜ Plan Phase 2 approval gates
-
-### Medium Term (Weeks 3-6)
-1. ⬜ Implement Phases 2-3 (approval + refinement)
-2. ⬜ User testing with pilot group
-3. ⬜ Gather metrics on usage patterns
-4. ⬜ Refine intent classification
-
-### Long Term (Weeks 7-10)
-1. ⬜ Implement Phases 4-5 (viz + new stages)
-2. ⬜ Full rollout to users
-3. ⬜ Monitor adoption and satisfaction
-4. ⬜ Plan next enhancements
-
----
-
-## 🤔 Open Questions for Discussion
-
-### Strategy
-1. Should we keep button UI as an option or fully migrate to chat?
-2. Which stages require approval vs auto-advance?
-3. Priority: refinement capability or new stages (site/bot)?
-
-### Technical
-1. Which LLM for intent classification (speed vs accuracy)?
-2. Should we cache intent classification results?
-3. How to handle very long conversations (context limits)?
-
-### UX
-1. How much guidance vs. letting AI interpret freely?
-2. Should quick action buttons always be visible?
-3. What's the ideal refinement UX (buttons, forms, pure chat)?
-
-### Business
-1. Timeline constraints or flexibility?
-2. Budget for LLM API costs?
-3. Success criteria for each phase?
-
----
-
-## 📖 Glossary
-
-**Stage** - A major step in the workflow (e.g., Research, DDL Creation)
-
-**Intent** - What the user wants to do (e.g., approve, refine, get info)
-
-**Approval Gate** - A required checkpoint where user must explicitly approve before proceeding
-
-**Refinement** - Targeted modification of output without full regeneration
-
-**Executor** - A specialized handler for a particular stage
-
-**Controller** - The orchestrator that manages workflow and state
-
-**Streaming** - Sending partial results as they're generated (not waiting for completion)
-
-**Context** - The current state of the conversation and workflow
-
----
-
-## 🎉 Vision Statement
-
-**"Create perfect ThoughtSpot demos through natural conversation, not button clicks."**
-
-Imagine a future where:
-- Sales engineers chat naturally with AI to create demos
-- No training required - the AI guides them
-- Bad outputs never make it to production (approval gates)
-- Refinement is instant (no waiting for regeneration)
-- Demos are created in 15 minutes instead of hours
-- Quality is consistent and high across all demos
-
-**This transformation makes that future possible.**
-
----
-
-## 📝 Version History
-
-| Version | Date | Changes |
-|---------|------|---------|
-| 1.0 | 2025-11-12 | Initial comprehensive plan created |
-
----
-
-**Questions? Start with IMPLEMENTATION_ROADMAP.md for hands-on guidance!**
-
-**Ready to build? Begin with Phase 1 foundation!**
-
-**Need technical details? See CHAT_ARCHITECTURE_PLAN.md!**
-
-**Designing conversations? Check CONVERSATION_PATTERNS.md!**
-
----
-
-*This transformation will revolutionize how demos are created. Let's build it! 🚀*
-

chat_data_adjuster.py

@@ -0,0 +1,163 @@
+"""
+Simple Chat Interface for Data Adjustment
+
+A basic command-line chat to test the conversational data adjuster.
+User can keep making adjustments until they type 'done' or 'exit'.
+"""
+
+from dotenv import load_dotenv
+import os
+from conversational_data_adjuster import ConversationalDataAdjuster
+
+load_dotenv()
+
+
+def chat_loop():
+    """Main chat loop for data adjustment"""
+    
+    print("""
+╔════════════════════════════════════════════════════════════╗
+║                                                            ║
+║          Data Adjustment Chat                              ║
+║                                                            ║
+╚════════════════════════════════════════════════════════════╝
+
+Commands:
+  - Type your adjustment request naturally
+  - "done" or "exit" to quit
+  - "help" for examples
+
+Examples:
+  - "increase 1080p Webcam sales to 50B"
+  - "set profit margin to 25% for electronics"  
+  - "make Tablet revenue 100 billion"
+
+""")
+    
+    # Initialize adjuster
+    database = os.getenv('SNOWFLAKE_DATABASE')
+    schema = "20251116_140933_AMAZO_SAL"
+    model_id = "3c97b0d6-448b-440a-b628-bac1f3d73049"
+    
+    print(f"📊 Connected to: {database}.{schema}")
+    print(f"🎯 Model: {model_id}\n")
+    
+    adjuster = ConversationalDataAdjuster(database, schema, model_id)
+    adjuster.connect()
+    
+    tables = adjuster.get_available_tables()
+    print(f"📋 Available tables: {', '.join(tables)}\n")
+    print("="*80)
+    print("Ready! Type your adjustment request...")
+    print("="*80 + "\n")
+    
+    while True:
+        # Get user input
+        user_input = input("\n💬 You: ").strip()
+        
+        if not user_input:
+            continue
+        
+        # Check for exit commands
+        if user_input.lower() in ['done', 'exit', 'quit', 'bye']:
+            print("\n👋 Goodbye!")
+            break
+        
+        # Check for help
+        if user_input.lower() == 'help':
+            print("""
+📚 Help - How to make adjustments:
+
+Format: "make/increase/set [entity] [metric] to [value]"
+
+Examples:
+  ✅ "increase 1080p Webcam revenue to 50 billion"
+  ✅ "set profit margin to 25% for electronics"
+  ✅ "make Laptop sales 100B"
+  ✅ "increase customer segment premium revenue by 30%"
+
+You'll see 3 strategy options - type A, B, or C to pick one.
+""")
+            continue
+        
+        try:
+            # Step 1: Parse the request
+            print(f"\n🤔 Parsing your request...")
+            adjustment = adjuster.parse_adjustment_request(user_input, tables)
+            
+            if 'error' in adjustment:
+                print(f"❌ {adjustment['error']}")
+                print("💡 Try rephrasing or type 'help' for examples")
+                continue
+            
+            # Step 2: Analyze current data
+            print(f"📊 Analyzing current data...")
+            analysis = adjuster.analyze_current_data(adjustment)
+            
+            if analysis['current_total'] == 0:
+                print(f"⚠️  No data found for '{adjustment['entity_value']}'")
+                print("💡 Try a different product/entity name")
+                continue
+            
+            # Step 3: Generate strategies
+            strategies = adjuster.generate_strategy_options(adjustment, analysis)
+            
+            # Step 4: Present options
+            adjuster.present_options(adjustment, analysis, strategies)
+            
+            # Step 5: Get user's strategy choice
+            print("\n" + "="*80)
+            choice = input("Which strategy? [A/B/C or 'skip']: ").strip().upper()
+            
+            if choice == 'SKIP' or not choice:
+                print("⏭️  Skipping this adjustment")
+                continue
+            
+            # Find the chosen strategy
+            chosen = None
+            for s in strategies:
+                if s['id'] == choice:
+                    chosen = s
+                    break
+            
+            if not chosen:
+                print(f"❌ Invalid choice: {choice}")
+                continue
+            
+            # Step 6: Confirm
+            print(f"\n⚠️  About to execute: {chosen['name']}")
+            print(f"   This will affect {chosen.get('details', {}).get('rows_affected', 'some')} rows")
+            confirm = input("   Confirm? [yes/no]: ").strip().lower()
+            
+            if confirm not in ['yes', 'y']:
+                print("❌ Cancelled")
+                continue
+            
+            # Step 7: Execute
+            result = adjuster.execute_strategy(chosen)
+            
+            if result['success']:
+                print(f"\n✅ {result['message']}")
+                print(f"🔄 Data updated! Refresh your ThoughtSpot liveboard to see changes.")
+            else:
+                print(f"\n❌ Failed: {result.get('error')}")
+        
+        except KeyboardInterrupt:
+            print("\n\n⚠️  Interrupted")
+            break
+        except Exception as e:
+            print(f"\n❌ Error: {e}")
+            import traceback
+            print(traceback.format_exc())
+    
+    # Cleanup
+    adjuster.close()
+    print("\n✅ Connection closed")
+
+
+if __name__ == "__main__":
+    try:
+        chat_loop()
+    except KeyboardInterrupt:
+        print("\n\n👋 Goodbye!")
+

chat_interface.py

@@ -42,7 +42,8 @@ class ChatDemoInterface:
             'model': 'claude-sonnet-4.5',
             'fact_table_size': '1000',
             'dim_table_size': '100',
-            'stage': 'initialization'
+            'stage': 'initialization',
+            'tag_name': None
         }
         
         try:
@@ -64,6 +65,8 @@ class ChatDemoInterface:
                     defaults['fact_table_size'] = settings.get('fact_table_size')
                 if settings.get('dim_table_size'):
                     defaults['dim_table_size'] = settings.get('dim_table_size')
+                if settings.get('tag_name'):
+                    defaults['tag_name'] = settings.get('tag_name')
         except Exception as e:
             print(f"Could not load settings from Supabase: {e}")
         
@@ -507,6 +510,9 @@ Cannot deploy to ThoughtSpot without tables."""
                     # Get currently selected model
                     llm_model = self.settings.get('model', 'claude-sonnet-4.5')
                     
+                    tag_name_value = self.settings.get('tag_name')
+                    print(f"🔍 DEBUG: tag_name from settings = '{tag_name_value}'")
+                    
                     results = deployer.deploy_all(
                         ddl=ddl,
                         database=database,
@@ -515,6 +521,7 @@ Cannot deploy to ThoughtSpot without tables."""
                         use_case=use_case,
                         liveboard_name=liveboard_name,
                         llm_model=llm_model,  # Pass selected model
+                        tag_name=tag_name_value,  # Pass tag from settings
                         progress_callback=progress_callback
                     )
                     
@@ -739,144 +746,211 @@ Type **'done'** to finish."""
             
             # Handle deployment errors (usually population failures)
             if hasattr(self, '_last_population_error'):
-                if 'retry' in message_lower:
+                # Handle '1' or 'retry' - retry with same code
+                if 'retry' in message_lower or message_lower.strip() == '1':
                     # Retry with same code
                     chat_history[-1] = (message, "🔄 Retrying population...")
                     yield chat_history, current_stage, current_model, company, use_case, ""
                     
-                    from demo_prep import execute_population_script
-                    is_template = getattr(self.demo_builder, 'population_code_source', 'llm') == 'template'
-                    success, msg = execute_population_script(
-                        self.demo_builder.data_population_results,
-                        self._last_schema_name,
-                        skip_modifications=is_template
-                    )
-                    
-                    if success:
-                        response = f"✅ **Population Successful!**\n\n{msg}\n\nDemo deployed to Snowflake! 🎉"
-                        del self._last_population_error
-                        del self._last_schema_name
-                    else:
-                        response = f"❌ Still failed: {msg[:200]}...\n\nTry 'truncate' or 'fix'?"
+                    try:
+                        # Check required attributes exist
+                        if not hasattr(self.demo_builder, 'data_population_results') or not self.demo_builder.data_population_results:
+                            response = "❌ **Error:** Population code not found. Please run population again first."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        if not hasattr(self, '_last_schema_name') or not self._last_schema_name:
+                            response = "❌ **Error:** Schema name not found. Please run deployment again first."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        from demo_prep import execute_population_script
+                        is_template = getattr(self.demo_builder, 'population_code_source', 'llm') == 'template'
+                        success, msg = execute_population_script(
+                            self.demo_builder.data_population_results,
+                            self._last_schema_name,
+                            skip_modifications=is_template
+                        )
+                        
+                        if success:
+                            response = f"✅ **Population Successful!**\n\n{msg}\n\nDemo deployed to Snowflake! 🎉"
+                            del self._last_population_error
+                            del self._last_schema_name
+                        else:
+                            response = f"❌ Still failed: {msg[:200]}...\n\nTry 'truncate' (or '2') or 'fix' (or '3')?"
+                        
+                    except Exception as e:
+                        import traceback
+                        error_details = traceback.format_exc()
+                        self.log_feedback(f"❌ Retry error: {error_details}")
+                        response = f"❌ **Retry failed with error:**\n\n```\n{str(e)}\n```\n\nPlease try 'truncate' (or '2') to clear tables first."
                     
                     chat_history[-1] = (message, response)
                     yield chat_history, current_stage, current_model, company, use_case, ""
                     return
                 
-                elif 'truncate' in message_lower:
+                elif 'truncate' in message_lower or message_lower.strip() == '2':
                     # Truncate tables and retry
                     chat_history[-1] = (message, "🗑️ Truncating tables and retrying...")
                     yield chat_history, current_stage, current_model, company, use_case, ""
                     
-                    from cdw_connector import SnowflakeDeployer
-                    from demo_prep import execute_population_script
-                    
-                    deployer = SnowflakeDeployer()
-                    deployer.connect()
-                    
-                    # Truncate all tables in schema
                     try:
-                        cursor = deployer.connection.cursor()
-                        cursor.execute(f"USE SCHEMA {self._last_schema_name}")
-                        cursor.execute("SHOW TABLES")
-                        tables = cursor.fetchall()
+                        # Check required attributes exist
+                        if not hasattr(self, '_last_schema_name') or not self._last_schema_name:
+                            response = "❌ **Error:** Schema name not found. Please run deployment again first."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        if not hasattr(self.demo_builder, 'data_population_results') or not self.demo_builder.data_population_results:
+                            response = "❌ **Error:** Population code not found. Please run population again first."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
                         
-                        for table in tables:
-                            table_name = table[1]
-                            self.log_feedback(f"Truncating {table_name}...")
-                            cursor.execute(f"TRUNCATE TABLE {table_name}")
+                        from cdw_connector import SnowflakeDeployer
+                        from demo_prep import execute_population_script
                         
-                        cursor.close()
-                        self.log_feedback("✅ Tables truncated")
-                    except Exception as e:
-                        self.log_feedback(f"⚠️ Truncate warning: {e}")
-                    
-                    # Retry population
-                    is_template = getattr(self.demo_builder, 'population_code_source', 'llm') == 'template'
-                    success, msg = execute_population_script(
-                        self.demo_builder.data_population_results,
-                        self._last_schema_name,
-                        skip_modifications=is_template
-                    )
+                        deployer = SnowflakeDeployer()
+                        deployer.connect()
+                        
+                        # Truncate all tables in schema
+                        try:
+                            cursor = deployer.connection.cursor()
+                            cursor.execute(f"USE SCHEMA {self._last_schema_name}")
+                            cursor.execute("SHOW TABLES")
+                            tables = cursor.fetchall()
+                            
+                            for table in tables:
+                                table_name = table[1]
+                                self.log_feedback(f"Truncating {table_name}...")
+                                cursor.execute(f"TRUNCATE TABLE {table_name}")
+                            
+                            cursor.close()
+                            deployer.disconnect()
+                            self.log_feedback("✅ Tables truncated")
+                        except Exception as e:
+                            self.log_feedback(f"⚠️ Truncate warning: {e}")
+                            if deployer.connection:
+                                deployer.disconnect()
+                        
+                        # Retry population
+                        is_template = getattr(self.demo_builder, 'population_code_source', 'llm') == 'template'
+                        success, msg = execute_population_script(
+                            self.demo_builder.data_population_results,
+                            self._last_schema_name,
+                            skip_modifications=is_template
+                        )
+                        
+                        if success:
+                            response = f"✅ **Population Successful!**\n\n{msg}\n\nDemo deployed to Snowflake! 🎉"
+                            del self._last_population_error
+                            del self._last_schema_name
+                        else:
+                            response = f"❌ Still failed: {msg[:200]}...\n\nTry 'fix' (or '3') to let AI correct the code?"
                     
-                    if success:
-                        response = f"✅ **Population Successful!**\n\n{msg}\n\nDemo deployed to Snowflake! 🎉"
-                        del self._last_population_error
-                        del self._last_schema_name
-                    else:
-                        response = f"❌ Still failed: {msg[:200]}...\n\nTry 'fix' to let AI correct the code?"
+                    except Exception as e:
+                        import traceback
+                        error_details = traceback.format_exc()
+                        self.log_feedback(f"❌ Truncate/retry error: {error_details}")
+                        response = f"❌ **Truncate/retry failed with error:**\n\n```\n{str(e)}\n```\n\nPlease check the error details above."
                     
                     chat_history[-1] = (message, response)
                     yield chat_history, current_stage, current_model, company, use_case, ""
                     return
                 
-                elif 'fix' in message_lower:
+                elif 'fix' in message_lower or message_lower.strip() == '3':
                     # Regenerate the code using the fixed template
                     chat_history[-1] = (message, "🔧 Regenerating population code with fixed template...")
                     yield chat_history, current_stage, current_model, company, use_case, ""
                     
-                    self.log_feedback("🔧 Regenerating population code from scratch...")
-                    
-                    # Regenerate using the reliable template
-                    from schema_utils import parse_ddl_schema
-                    
-                    schema_info = parse_ddl_schema(self.demo_builder.schema_generation_results)
-                    if not schema_info:
-                        response = "❌ Failed to parse DDL schema. Cannot regenerate."
-                        chat_history[-1] = (message, response)
-                        yield chat_history, current_stage, current_model, company, use_case, ""
-                        return
-                    
-                    # Generate new code using the template (which includes all fixes)
-                    fixed_code = self.get_fallback_population_code(schema_info)
-                    
-                    # Validate it compiles
                     try:
-                        compile(fixed_code, '<regenerated>', 'exec')
-                        self.log_feedback("✅ Regenerated code validated")
-                    except SyntaxError as e:
-                        response = f"❌ Template generation bug: {e}\n\nPlease contact support."
-                        chat_history[-1] = (message, response)
-                        yield chat_history, current_stage, current_model, company, use_case, ""
-                        return
-                    
-                    # Update the code and mark as template-generated
-                    self.demo_builder.data_population_results = fixed_code
-                    self.population_code = fixed_code
-                    self.demo_builder.population_code_source = "template"  # Mark as template
-                    
-                    self.log_feedback("🔧 Code regenerated, retrying deployment...")
-                    
-                    # Truncate and retry
-                    from cdw_connector import SnowflakeDeployer
-                    from demo_prep import execute_population_script
-                    
-                    deployer = SnowflakeDeployer()
-                    deployer.connect()
+                        # Check required attributes exist
+                        if not hasattr(self.demo_builder, 'schema_generation_results') or not self.demo_builder.schema_generation_results:
+                            response = "❌ **Error:** DDL schema not found. Please run DDL creation again first."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        if not hasattr(self, '_last_schema_name') or not self._last_schema_name:
+                            response = "❌ **Error:** Schema name not found. Please run deployment again first."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        self.log_feedback("🔧 Regenerating population code from scratch...")
+                        
+                        # Regenerate using the reliable template
+                        from schema_utils import parse_ddl_schema
+                        
+                        schema_info = parse_ddl_schema(self.demo_builder.schema_generation_results)
+                        if not schema_info:
+                            response = "❌ Failed to parse DDL schema. Cannot regenerate."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        # Generate new code using the template (which includes all fixes)
+                        fixed_code = self.get_fallback_population_code(schema_info)
+                        
+                        # Validate it compiles
+                        try:
+                            compile(fixed_code, '<regenerated>', 'exec')
+                            self.log_feedback("✅ Regenerated code validated")
+                        except SyntaxError as e:
+                            response = f"❌ Template generation bug: {e}\n\nPlease contact support."
+                            chat_history[-1] = (message, response)
+                            yield chat_history, current_stage, current_model, company, use_case, ""
+                            return
+                        
+                        # Update the code and mark as template-generated
+                        self.demo_builder.data_population_results = fixed_code
+                        self.population_code = fixed_code
+                        self.demo_builder.population_code_source = "template"  # Mark as template
+                        
+                        self.log_feedback("🔧 Code regenerated, retrying deployment...")
+                        
+                        # Truncate and retry
+                        from cdw_connector import SnowflakeDeployer
+                        from demo_prep import execute_population_script
+                        
+                        deployer = SnowflakeDeployer()
+                        deployer.connect()
+                        
+                        try:
+                            cursor = deployer.connection.cursor()
+                            cursor.execute(f"USE SCHEMA {self._last_schema_name}")
+                            cursor.execute("SHOW TABLES")
+                            tables = cursor.fetchall()
+                            for table in tables:
+                                cursor.execute(f"TRUNCATE TABLE {table[1]}")
+                            cursor.close()
+                            deployer.disconnect()
+                        except Exception as e:
+                            self.log_feedback(f"⚠️ Truncate warning: {e}")
+                            if deployer.connection:
+                                deployer.disconnect()
+                        
+                        success, msg = execute_population_script(
+                            fixed_code,
+                            self._last_schema_name,
+                            skip_modifications=True  # Template code, don't modify
+                        )
+                        
+                        if success:
+                            response = f"✅ **Fixed and Successful!**\n\n{msg}\n\nDemo deployed to Snowflake! 🎉"
+                            del self._last_population_error
+                            del self._last_schema_name
+                        else:
+                            response = f"❌ AI fix didn't work: {msg[:200]}...\n\nTry 'fix' again or 'retry'?"
                     
-                    try:
-                        cursor = deployer.connection.cursor()
-                        cursor.execute(f"USE SCHEMA {self._last_schema_name}")
-                        cursor.execute("SHOW TABLES")
-                        tables = cursor.fetchall()
-                        for table in tables:
-                            cursor.execute(f"TRUNCATE TABLE {table[1]}")
-                        cursor.close()
                     except Exception as e:
-                        self.log_feedback(f"⚠️ Truncate warning: {e}")
-                    
-                    success, msg = execute_population_script(
-                        fixed_code,
-                        self._last_schema_name,
-                        skip_modifications=True  # Template code, don't modify
-                    )
-                    
-                    if success:
-                        response = f"✅ **Fixed and Successful!**\n\n{msg}\n\nDemo deployed to Snowflake! 🎉"
-                        del self._last_population_error
-                        del self._last_schema_name
-                    else:
-                        response = f"❌ AI fix didn't work: {msg[:200]}...\n\nTry 'fix' again or 'retry'?"
+                        import traceback
+                        error_details = traceback.format_exc()
+                        self.log_feedback(f"❌ Fix/regenerate error: {error_details}")
+                        response = f"❌ **Fix/regenerate failed with error:**\n\n```\n{str(e)}\n```\n\nPlease check the error details above."
                     
                     chat_history[-1] = (message, response)
                     yield chat_history, current_stage, current_model, company, use_case, ""
@@ -1082,16 +1156,20 @@ Try a different request or type **'done'** to finish."""
         import re
         
         # Pattern: "company: XYZ" or "for the company: XYZ"
+        # Stop at "use case:", "and", "for", or end of string
         patterns = [
-            r'company:\s*([^,\n]+?)(?:\s+and|\s+for|$)',
-            r'for\s+(?:the\s+)?company:\s*([^,\n]+?)(?:\s+and|\s+for|$)',
-            r'demo for\s+(?:the\s+)?company:\s*([^,\n]+?)(?:\s+and|\s+for|$)'
+            r'company:\s*([^,\n]+?)(?:\s+use\s+case:|\s+and|\s+for|$)',
+            r'for\s+(?:the\s+)?company:\s*([^,\n]+?)(?:\s+use\s+case:|\s+and|\s+for|$)',
+            r'demo for\s+(?:the\s+)?company:\s*([^,\n]+?)(?:\s+use\s+case:|\s+and|\s+for|$)'
         ]
         
         for pattern in patterns:
             match = re.search(pattern, message, re.IGNORECASE)
             if match:
-                return match.group(1).strip()
+                company = match.group(1).strip()
+                # Additional cleanup: remove any trailing "use case" text that might have been captured
+                company = re.sub(r'\s+use\s+case:.*$', '', company, flags=re.IGNORECASE).strip()
+                return company
         
         return None
     
@@ -1182,9 +1260,20 @@ To change settings, use:
         yield progress_message
         
         try:
-            # Initialize demo builder if needed
-            if not self.demo_builder:
-                self.log_feedback("Initializing DemoBuilder...")
+            # Initialize demo builder if needed OR if company/use_case changed
+            # CRITICAL: Always create fresh DemoBuilder when company/use_case changes
+            # to avoid persisting prompts/data from previous runs
+            needs_new_builder = (
+                not self.demo_builder or
+                self.demo_builder.company_url != company or
+                self.demo_builder.use_case != use_case
+            )
+            
+            if needs_new_builder:
+                if self.demo_builder:
+                    self.log_feedback(f"🔄 Company/use case changed - creating fresh DemoBuilder (was: {self.demo_builder.company_url}/{self.demo_builder.use_case})")
+                else:
+                    self.log_feedback("Initializing DemoBuilder...")
                 progress_message += "✓ Initializing DemoBuilder...\n"
                 yield progress_message
                 
@@ -1193,8 +1282,12 @@ To change settings, use:
                     company_url=company
                 )
             
-            # Prepare URL
-            url = company if company.startswith('http') else f"https://{company}"
+            # Prepare URL - clean up any extra text that might have been captured
+            # Remove "use case:" and anything after it, and clean whitespace
+            import re
+            clean_company = re.sub(r'\s+use\s+case:.*$', '', company, flags=re.IGNORECASE).strip()
+            clean_company = re.sub(r'\s+and\s+.*$', '', clean_company, flags=re.IGNORECASE).strip()
+            url = clean_company if clean_company.startswith('http') else f"https://{clean_company}"
             
             # Check for cached research results
             domain = url.replace('https://', '').replace('http://', '').replace('www.', '').split('/')[0]
@@ -1275,6 +1368,25 @@ To change settings, use:
         
         website = Website(url)
         self.demo_builder.website_data = website
+        
+        # Check if website extraction failed
+        if not website.text or len(website.text) == 0:
+            error_msg = f"❌ **Couldn't access the website**\n\n"
+            if website.error_message:
+                error_msg += f"**Error:** {website.error_message}\n\n"
+            error_msg += f"**URL:** {url}\n\n"
+            error_msg += "**Troubleshooting:**\n"
+            error_msg += "- Verify the URL is correct and accessible in your browser\n"
+            error_msg += "- Check if the site requires authentication\n"
+            error_msg += "- The site may be blocking automated requests\n"
+            error_msg += "- Try accessing the site manually to confirm it's working\n\n"
+            error_msg += "Please check the URL and try again."
+            
+            self.log_feedback(error_msg)
+            progress_message += error_msg
+            yield progress_message
+            return
+        
         self.log_feedback(f"Extracted {len(website.text)} characters from {website.title}")
         progress_message += f"✓ Extracted {len(website.text)} characters from {website.title}\n\n"
         yield progress_message
@@ -1442,9 +1554,20 @@ To change settings, use:
         self.log_feedback(f"🔍 Starting research for {company} - {use_case}")
         
         try:
-            # Initialize demo builder if needed
-            if not self.demo_builder:
-                self.log_feedback("Initializing DemoBuilder...")
+            # Initialize demo builder if needed OR if company/use_case changed
+            # CRITICAL: Always create fresh DemoBuilder when company/use_case changes
+            # to avoid persisting prompts/data from previous runs
+            needs_new_builder = (
+                not self.demo_builder or
+                self.demo_builder.company_url != company or
+                self.demo_builder.use_case != use_case
+            )
+            
+            if needs_new_builder:
+                if self.demo_builder:
+                    self.log_feedback(f"🔄 Company/use case changed - creating fresh DemoBuilder (was: {self.demo_builder.company_url}/{self.demo_builder.use_case})")
+                else:
+                    self.log_feedback("Initializing DemoBuilder...")
                 self.demo_builder = DemoBuilder(
                     use_case=use_case,
                     company_url=company
@@ -1455,6 +1578,22 @@ To change settings, use:
             url = company if company.startswith('http') else f"https://{company}"
             website = Website(url)
             self.demo_builder.website_data = website
+            
+            # Check if website extraction failed
+            if not website.text or len(website.text) == 0:
+                error_msg = f"❌ **Couldn't access the website**\n\n"
+                if website.error_message:
+                    error_msg += f"**Error:** {website.error_message}\n\n"
+                error_msg += f"**URL:** {url}\n\n"
+                error_msg += "**Troubleshooting:**\n"
+                error_msg += "- Verify the URL is correct and accessible in your browser\n"
+                error_msg += "- Check if the site requires authentication\n"
+                error_msg += "- The site may be blocking automated requests\n"
+                error_msg += "- Try accessing the site manually to confirm it's working\n\n"
+                error_msg += "Please check the URL and try again."
+                self.log_feedback(error_msg)
+                return None
+            
             self.log_feedback(f"Extracted {len(website.text)} characters from {website.title}")
             
             # Get LLM provider
@@ -1586,6 +1725,13 @@ TECHNICAL REQUIREMENTS:
 - Include realistic column names that match the business context
 - Add proper constraints and relationships
 
+TABLE NAMING REQUIREMENTS:
+- **DO NOT use DIM_ or FACT_ prefixes** (e.g., NOT DIM_PRODUCT or FACT_SALES)
+- Use simple, descriptive table names (e.g., PRODUCTS, CUSTOMERS, SALES, ORDERS)
+- Dimension tables: Use plural nouns (CUSTOMERS, PRODUCTS, WAREHOUSES)
+- Fact tables: Use descriptive names (SALES, TRANSACTIONS, ORDERS, INVENTORY_MOVEMENTS)
+- Keep names concise and business-friendly
+
 SNOWFLAKE SYNTAX EXAMPLES:
 - Auto-increment: ColumnID INT IDENTITY(1,1) PRIMARY KEY
 - NOT: ColumnID INT PRIMARY KEY AUTO_INCREMENT
@@ -1783,46 +1929,59 @@ Generate complete CREATE TABLE statements with proper Snowflake syntax and depen
                     fake_values.append("random.choice(['A', 'B', 'C'])")
                 
                 elif 'VARCHAR' in col_type or 'TEXT' in col_type or 'STRING' in col_type or 'CHAR' in col_type:
-                    # Generate domain-specific realistic data based on column name
+                    # Extract VARCHAR length - always truncate generated values to fit
+                    import re
+                    length_match = re.search(r'\((\d+)\)', col_type)
+                    varchar_length = int(length_match.group(1)) if length_match else 255
+                    
+                    # Generate domain-specific realistic data based on column name, then truncate to fit
+                    base_value = None
                     if 'NAME' in col_name_upper and 'COMPANY' not in col_name_upper:
                         if 'PRODUCT' in col_name_upper:
-                            fake_values.append("random.choice(['Laptop Pro 15', 'Wireless Mouse 2.4GHz', 'USB-C Cable 6ft', 'Monitor Stand Adjustable', 'Mechanical Keyboard RGB', 'Noise Canceling Headphones', '1080p Webcam', 'Portable SSD 1TB', 'Power Bank 20000mAh', 'Tablet 10 inch', 'Smart Watch', 'Bluetooth Speaker', 'Gaming Mouse Pad', 'Phone Case', 'Screen Protector', 'Charging Cable', 'Desk Lamp LED', 'Laptop Bag', 'Wireless Earbuds', 'USB Hub'])")
+                            base_value = "random.choice(['Laptop Pro 15', 'Wireless Mouse 2.4GHz', 'USB-C Cable 6ft', 'Monitor Stand Adjustable', 'Mechanical Keyboard RGB', 'Noise Canceling Headphones', '1080p Webcam', 'Portable SSD 1TB', 'Power Bank 20000mAh', 'Tablet 10 inch', 'Smart Watch', 'Bluetooth Speaker', 'Gaming Mouse Pad', 'Phone Case', 'Screen Protector', 'Charging Cable', 'Desk Lamp LED', 'Laptop Bag', 'Wireless Earbuds', 'USB Hub'])"
                         elif 'CUSTOMER' in col_name_upper or 'USER' in col_name_upper:
-                            fake_values.append("fake.name()[:50]")  # Truncate to 50 chars
+                            base_value = "fake.name()"
                         elif 'SELLER' in col_name_upper or 'VENDOR' in col_name_upper:
-                            fake_values.append("random.choice(['Amazon', 'Best Buy', 'Walmart', 'Target', 'Costco', 'Home Depot', 'Lowes', 'Macys', 'Nordstrom', 'Kohls'])")
+                            base_value = "random.choice(['Amazon', 'Best Buy', 'Walmart', 'Target', 'Costco', 'Home Depot', 'Lowes', 'Macys', 'Nordstrom', 'Kohls'])"
                         else:
-                            fake_values.append("fake.name()[:50]")
+                            base_value = "fake.name()"
                     elif 'CATEGORY' in col_name_upper:
-                        fake_values.append("random.choice(['Electronics', 'Home & Kitchen', 'Books', 'Clothing', 'Sports', 'Toys', 'Beauty', 'Automotive'])")
+                        base_value = "random.choice(['Electronics', 'Home & Kitchen', 'Books', 'Clothing', 'Sports', 'Toys', 'Beauty', 'Automotive'])"
                     elif 'BRAND' in col_name_upper:
-                        fake_values.append("random.choice(['Samsung', 'Apple', 'Sony', 'LG', 'Dell', 'HP', 'Lenovo', 'Amazon Basics', 'Anker', 'Logitech'])")
+                        base_value = "random.choice(['Samsung', 'Apple', 'Sony', 'LG', 'Dell', 'HP', 'Lenovo', 'Amazon Basics', 'Anker', 'Logitech'])"
+                    elif 'CHANNEL' in col_name_upper or 'SOURCE' in col_name_upper:
+                        # Marketing channels for lead generation / call tracking
+                        base_value = "random.choice(['Google Ads Search', 'Bing Ads', 'Facebook Ads', 'LinkedIn Ads', 'Instagram Ads', 'Twitter Ads', 'Display Network', 'Programmatic Display', 'Retargeting', 'TV Commercial', 'Radio Ads', 'Billboard', 'Print Ads', 'Direct Mail', 'Email Newsletter', 'Organic Search', 'Social Media Organic', 'Google My Business', 'Referral', 'Affiliate Marketing', 'Content Marketing', 'Webinar', 'Podcast Sponsorship'])"
+                    elif 'CAMPAIGN' in col_name_upper and ('NAME' in col_name_upper or col_name_upper == 'CAMPAIGN_NAME'):
+                        # Marketing campaign names (usually reference the channel)
+                        base_value = "random.choice(['Google Ads Q4 Lead Gen', 'Facebook Black Friday Promo', 'LinkedIn Spring Campaign', 'Instagram New Product Launch', 'Email Brand Awareness', 'Display Holiday Special', 'Google Ads Summer Sale', 'Facebook Back to School', 'LinkedIn Valentine Promo', 'Google Shopping Cyber Monday', 'Email Free Trial Offer', 'Webinar Registration Q3', 'Email Nurture Series', 'Display Retargeting Q3', 'Google Ads Demo Request', 'Referral Rewards Program', 'Google Ads Year End Sale', 'Facebook New Year Campaign', 'Instagram Flash Sale', 'Email Limited Time Offer', 'Google Ads Early Bird', 'LinkedIn VIP Member Drive', 'Facebook Product Teaser', 'Display Conference Promo', 'Email Partner Campaign', 'Google Ads Seasonal', 'Facebook Customer Appreciation', 'Email Win Back Campaign', 'LinkedIn Upsell Drive', 'Display Cross-Sell Q4'])"
+                    elif ('CENTER' in col_name_upper and 'NAME' in col_name_upper) or ('CALL' in col_name_upper and 'CENTER' in col_name_upper):
+                        # Call center names
+                        base_value = "random.choice(['New York Contact Center', 'Los Angeles Support Hub', 'Chicago Call Center', 'Dallas Operations Center', 'Phoenix Customer Care', 'Philadelphia Service Center', 'San Diego Support Center', 'Miami Contact Hub', 'Atlanta Operations', 'Denver Call Center', 'Seattle Support Center', 'Boston Customer Service', 'Portland Contact Center', 'Austin Operations Hub', 'Las Vegas Call Center', 'Toronto Support Center', 'Offshore Manila Center', 'Offshore Bangalore Hub', 'Remote East Coast Team', 'Remote West Coast Team', 'Central Support Center', 'National Call Center', 'Regional North Hub', 'Regional South Hub', 'Enterprise Support Center'])"
                     elif 'DESCRIPTION' in col_name_upper or 'DESC' in col_name_upper:
-                        fake_values.append("random.choice(['High quality product', 'Best seller', 'Customer favorite', 'New arrival', 'Limited edition', 'Premium quality'])")
+                        base_value = "random.choice(['High quality product', 'Best seller', 'Customer favorite', 'New arrival', 'Limited edition', 'Premium quality'])"
                     elif 'EMAIL' in col_name_upper:
-                        fake_values.append("fake.email()[:50]")  # Truncate to 50 chars
+                        base_value = "fake.email()"
                     elif 'PHONE' in col_name_upper:
-                        fake_values.append("f'{random.randint(200, 999)}-{random.randint(200, 999)}-{random.randint(1000, 9999)}'")
+                        base_value = "f'{random.randint(200, 999)}-{random.randint(200, 999)}-{random.randint(1000, 9999)}'"
                     elif 'ADDRESS' in col_name_upper or 'STREET' in col_name_upper:
-                        fake_values.append("f'{random.randint(1, 9999)} {random.choice([\"Main\", \"Oak\", \"Park\", \"Maple\", \"Cedar\", \"Elm\", \"Washington\", \"Lake\", \"Hill\", \"Broadway\"])} {random.choice([\"St\", \"Ave\", \"Blvd\", \"Dr\", \"Ln\"])}'")
+                        base_value = "f'{random.randint(1, 9999)} {random.choice([\"Main\", \"Oak\", \"Park\", \"Maple\", \"Cedar\", \"Elm\", \"Washington\", \"Lake\", \"Hill\", \"Broadway\"])} {random.choice([\"St\", \"Ave\", \"Blvd\", \"Dr\", \"Ln\"])}'"
                     elif 'CITY' in col_name_upper:
-                        fake_values.append("random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose', 'Austin', 'Seattle', 'Denver', 'Boston', 'Portland', 'Miami', 'Atlanta', 'Detroit', 'Las Vegas', 'Toronto'])")
+                        base_value = "random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego', 'Dallas', 'San Jose', 'Austin', 'Seattle', 'Denver', 'Boston', 'Portland', 'Miami', 'Atlanta', 'Detroit', 'Las Vegas', 'Toronto'])"
                     elif 'STATE' in col_name_upper or 'PROVINCE' in col_name_upper:
-                        fake_values.append("random.choice(['California', 'Texas', 'New York', 'Florida', 'Illinois', 'Ohio', 'Georgia', 'Washington', 'Virginia', 'Arizona', 'Colorado', 'Oregon', 'Nevada', 'Utah', 'Iowa'])")
+                        base_value = "random.choice(['California', 'Texas', 'New York', 'Florida', 'Illinois', 'Ohio', 'Georgia', 'Washington', 'Virginia', 'Arizona', 'Colorado', 'Oregon', 'Nevada', 'Utah', 'Iowa'])"
                     elif 'COUNTRY' in col_name_upper:
-                        fake_values.append("random.choice(['USA', 'Canada', 'UK', 'Germany', 'France', 'Japan', 'Australia', 'India', 'China', 'Brazil', 'Mexico', 'Spain', 'Italy', 'Netherlands', 'Sweden'])")
+                        base_value = "random.choice(['USA', 'Canada', 'UK', 'Germany', 'France', 'Japan', 'Australia', 'India', 'China', 'Brazil', 'Mexico', 'Spain', 'Italy', 'Netherlands', 'Sweden'])"
                     elif 'ZIP' in col_name_upper or 'POSTAL' in col_name_upper:
-                        fake_values.append("random.choice(['10001', '90210', '60601', '77001', '85001', '19101', '78201', '92101', '75201', '95101', '78701', '98101', '80201', '02101', '97201'])")
+                        base_value = "random.choice(['10001', '90210', '60601', '77001', '85001', '19101', '78201', '92101', '75201', '95101', '78701', '98101', '80201', '02101', '97201'])"
                     elif 'COMPANY' in col_name_upper:
-                        fake_values.append("random.choice(['Amazon', 'Microsoft', 'Apple Inc', 'Google LLC', 'Meta', 'Tesla Inc', 'Netflix', 'Adobe Inc', 'Oracle Corp', 'Salesforce', 'IBM Corp', 'Intel Corp', 'Cisco Systems', 'Dell Technologies', 'HP Inc'])")
+                        base_value = "random.choice(['Amazon', 'Microsoft', 'Apple Inc', 'Google LLC', 'Meta', 'Tesla Inc', 'Netflix', 'Adobe Inc', 'Oracle Corp', 'Salesforce', 'IBM Corp', 'Intel Corp', 'Cisco Systems', 'Dell Technologies', 'HP Inc'])"
                     else:
-                        # Default: generate realistic short text
-                        import re
-                        length_match = re.search(r'\((\d+)\)', col_type)
-                        if length_match and int(length_match.group(1)) < 20:
-                            fake_values.append("fake.word()[:10]")
-                        else:
-                            fake_values.append("fake.word()")
+                        # Default: use faker word
+                        base_value = "fake.word()"
+                    
+                    # Always truncate to VARCHAR length - simple and works for all cases
+                    fake_values.append(f"({base_value})[:{varchar_length}]")
                 elif 'INT' in col_type or 'NUMBER' in col_type or 'BIGINT' in col_type:
                     fake_values.append("random.randint(1, 1000)")
                 elif 'DECIMAL' in col_type or 'FLOAT' in col_type or 'DOUBLE' in col_type or 'NUMERIC' in col_type:
@@ -2406,8 +2565,9 @@ def create_chat_interface():
                     settings.get("liveboard_name", ""),                     # 4. liveboard_name (moved)
                     str(settings.get("fact_table_size", "1000")),           # 5. fact_table_size (moved)
                     str(settings.get("dim_table_size", "100")),             # 6. dim_table_size
-                    settings.get("object_naming_prefix", ""),               # 7. object_naming_prefix
-                    float(settings.get("temperature", 0.3)),                # 8. temperature_slider
+                    settings.get("tag_name", ""),                           # 7. tag_name
+                    settings.get("object_naming_prefix", ""),               # 8. object_naming_prefix
+                    float(settings.get("temperature", 0.3)),                # 9. temperature_slider
                     int(settings.get("max_tokens", 4000)),                  # 9. max_tokens
                     int(settings.get("batch_size", 5000)),                  # 10. batch_size
                     int(settings.get("thread_count", 4)),                   # 11. thread_count
@@ -2427,8 +2587,9 @@ def create_chat_interface():
                     "claude-sonnet-4.5", "", "Sales Analytics",       # 1-3
                     "",                                               # 4: liveboard_name
                     "1000", "100",                                    # 5-6: fact_table_size, dim_table_size
-                    "",                                               # 7: object_naming_prefix
-                    0.3, 4000, 5000, 4,                              # 8-11: temperature, max_tokens, batch, threads
+                    "",                                               # 7: tag_name
+                    "",                                               # 8: object_naming_prefix
+                    0.3, 4000, 5000, 4,                              # 9-12: temperature, max_tokens, batch, threads
                     "", "", "ACCOUNTADMIN",                          # 12-14: sf settings
                     "COMPUTE_WH", "DEMO_DB", "PUBLIC",               # 15-17: warehouse, db, schema
                     "", "",                                           # 18-19: ts url, ts username
@@ -2446,8 +2607,9 @@ def create_chat_interface():
                 settings_components['liveboard_name'],             # 4 (moved)
                 settings_components['fact_table_size'],            # 5 (moved)
                 settings_components['dim_table_size'],             # 6
-                settings_components['object_naming_prefix'],       # 7
-                settings_components['temperature_slider'],         # 8
+                settings_components['tag_name'],                   # 7
+                settings_components['object_naming_prefix'],       # 8
+                settings_components['temperature_slider'],         # 9
                 settings_components['max_tokens'],                 # 9
                 settings_components['batch_size'],                 # 10
                 settings_components['thread_count'],               # 11
@@ -2684,6 +2846,13 @@ def create_settings_tab():
                 info="Number of rows in dimension tables"
             )
             
+            tag_name = gr.Textbox(
+                label="Tag Name",
+                placeholder="e.g., 'Sales_Demo' or 'Q4_2024'",
+                value="",
+                info="Tag to apply to ThoughtSpot objects (tables and models)"
+            )
+            
             object_naming_prefix = gr.Textbox(
                 label="Object Naming Prefix",
                 placeholder="e.g., 'ACME_' or 'DEMO_'",
@@ -2810,7 +2979,7 @@ def create_settings_tab():
     
     def save_settings_handler(
         ai_model, company_url, use_case, 
-        lb_name, fact_size, dim_size, obj_prefix,
+        lb_name, fact_size, dim_size, tag, obj_prefix,
         temp, max_tok, batch, threads,
         sf_acc, sf_user, sf_role, wh, db, schema,
         ts_url, ts_user
@@ -2832,6 +3001,7 @@ def create_settings_tab():
                 "default_use_case": use_case,
                 "fact_table_size": fact_size,
                 "dim_table_size": dim_size,
+                "tag_name": tag or "",
                 "temperature": str(temp),
                 "max_tokens": str(int(max_tok)),
                 "batch_size": str(int(batch)),
@@ -2865,7 +3035,7 @@ def create_settings_tab():
         fn=save_settings_handler,
         inputs=[
             default_ai_model, default_company_url, default_use_case, 
-            liveboard_name, fact_table_size, dim_table_size, object_naming_prefix,
+            liveboard_name, fact_table_size, dim_table_size, tag_name, object_naming_prefix,
             temperature_slider, max_tokens, batch_size, thread_count,
             sf_account, sf_user, sf_role, default_warehouse, default_database, default_schema,
             ts_instance_url, ts_username
@@ -2898,6 +3068,7 @@ def create_settings_tab():
         'default_schema': default_schema,
         'ts_instance_url': ts_instance_url,
         'ts_username': ts_username,
+        'tag_name': tag_name,
         'object_naming_prefix': object_naming_prefix,
         'liveboard_name': liveboard_name,
         'settings_status': settings_status

conversational_data_adjuster.py

@@ -0,0 +1,447 @@
+"""
+Conversational Data Adjuster
+
+Allows natural language data adjustments with strategy selection:
+User: "Make 1080p webcam sales 50B"
+System: Analyzes data, presents options
+User: Picks strategy
+System: Executes SQL
+"""
+
+import os
+from typing import Dict, List, Optional
+from openai import OpenAI
+from snowflake_auth import get_snowflake_connection
+import json
+
+
+class ConversationalDataAdjuster:
+    """Interactive data adjustment with user choice of strategy"""
+    
+    def __init__(self, database: str, schema: str, model_id: str):
+        self.database = database
+        self.schema = schema
+        self.model_id = model_id
+        self.conn = None
+        self.openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+        self.current_context = {}
+    
+    def connect(self):
+        """Connect to Snowflake"""
+        self.conn = get_snowflake_connection()
+        cursor = self.conn.cursor()
+        cursor.execute(f"USE DATABASE {self.database}")
+        cursor.execute(f'USE SCHEMA "{self.schema}"')  # Quote schema name (may start with number)
+        print(f"✅ Connected to {self.database}.{self.schema}")
+    
+    def parse_adjustment_request(self, request: str, available_tables: List[str]) -> Dict:
+        """
+        Parse natural language request to identify what to adjust
+        
+        Args:
+            request: e.g., "increase 1080p webcam sales to 50B"
+            available_tables: List of table names in schema
+        
+        Returns:
+            {
+                'table': 'SALES_TRANSACTIONS',
+                'entity_column': 'product_name',
+                'entity_value': '1080p webcam',
+                'metric_column': 'total_revenue',
+                'target_value': 50000000000,
+                'current_value': 30000000000  # if known
+            }
+        """
+        prompt = f"""Parse this data adjustment request.
+
+Request: "{request}"
+
+Available tables: {', '.join(available_tables)}
+
+Common columns:
+- SALES_TRANSACTIONS: PRODUCT_ID, CUSTOMER_ID, SELLER_ID, TOTAL_REVENUE, QUANTITY_SOLD, PROFIT_MARGIN, ORDER_DATE
+- PRODUCTS: PRODUCT_ID, PRODUCT_NAME, CATEGORY
+- CUSTOMERS: CUSTOMER_ID, CUSTOMER_SEGMENT
+
+IMPORTANT - Column Meanings:
+- TOTAL_REVENUE = dollar value of sales (e.g., $50B means fifty billion dollars)
+- QUANTITY_SOLD = number of units sold (e.g., 1000 units)
+
+When user says "sales", "revenue", or dollar amounts → use TOTAL_REVENUE
+When user says "quantity", "units", or "items sold" → use QUANTITY_SOLD
+
+Note: To filter by product name, you'll need to reference PRODUCTS table or use PRODUCT_ID directly
+
+Extract:
+1. table: Which table to modify (likely SALES_TRANSACTIONS for revenue/sales changes)
+2. entity_column: Column to filter by (e.g., product_name, customer_segment)
+3. entity_value: Specific value to filter (e.g., "1080p webcam", "Electronics")
+4. metric_column: Numeric column to change
+   - If request mentions "sales", "revenue", or dollar amounts → TOTAL_REVENUE
+   - If request mentions "quantity", "units", "items" → QUANTITY_SOLD
+   - If request mentions "profit margin" → PROFIT_MARGIN
+5. target_value: The target numeric value (convert B to billions, M to millions)
+
+Return ONLY valid JSON: {{"table": "...", "entity_column": "...", "entity_value": "...", "metric_column": "...", "target_value": 123}}
+
+Examples:
+- "increase 1080p webcam sales to 50B" → {{"table": "SALES_TRANSACTIONS", "entity_column": "PRODUCT_ID", "entity_value": "1080p Webcam", "metric_column": "TOTAL_REVENUE", "target_value": 50000000000, "needs_join": "PRODUCTS", "join_column": "PRODUCT_NAME"}}
+- "make tablet revenue 100 billion" → {{"table": "SALES_TRANSACTIONS", "entity_column": "PRODUCT_ID", "entity_value": "Tablet", "metric_column": "TOTAL_REVENUE", "target_value": 100000000000, "needs_join": "PRODUCTS", "join_column": "PRODUCT_NAME"}}
+- "increase laptop quantity to 50000 units" → {{"table": "SALES_TRANSACTIONS", "entity_column": "PRODUCT_ID", "entity_value": "Laptop", "metric_column": "QUANTITY_SOLD", "target_value": 50000, "needs_join": "PRODUCTS", "join_column": "PRODUCT_NAME"}}
+- "set profit margin to 25% for electronics" → {{"table": "SALES_TRANSACTIONS", "entity_column": "PRODUCT_ID", "entity_value": "electronics", "metric_column": "PROFIT_MARGIN", "target_value": 25, "needs_join": "PRODUCTS", "join_column": "CATEGORY"}}
+
+If the entity refers to a column not in the target table (e.g., product_name when modifying SALES_TRANSACTIONS),
+include "needs_join" with the table name and "join_column" with the column to match on.
+"""
+        
+        response = self.openai_client.chat.completions.create(
+            model="gpt-4o",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0
+        )
+        
+        content = response.choices[0].message.content
+        
+        # Strip markdown code blocks if present
+        if content.startswith('```'):
+            lines = content.split('\n')
+            content = '\n'.join(lines[1:-1])  # Remove first and last line (``` markers)
+        
+        try:
+            result = json.loads(content)
+            print(f"✅ Parsed request: {result.get('entity_value')} - {result.get('metric_column')}")
+            return result
+        except json.JSONDecodeError as e:
+            print(f"❌ Failed to parse JSON: {e}")
+            print(f"Content was: {content}")
+            return {'error': f'Failed to parse request: {content}'}
+    
+    def analyze_current_data(self, adjustment: Dict) -> Dict:
+        """
+        Query current state of the data
+        
+        Returns:
+            {
+                'current_total': float,
+                'row_count': int,
+                'avg_value': float,
+                'min_value': float,
+                'max_value': float,
+                'gap': float  # target - current
+            }
+        """
+        cursor = self.conn.cursor()
+        
+        table = adjustment['table']
+        entity_col = adjustment['entity_column']
+        entity_val = adjustment['entity_value']
+        metric_col = adjustment['metric_column']
+        target = adjustment['target_value']
+        
+        # Build WHERE clause - handle joins if needed
+        if adjustment.get('needs_join'):
+            join_table = adjustment['needs_join']
+            join_col = adjustment['join_column']
+            where_clause = f"""WHERE {entity_col} IN (
+                SELECT PRODUCT_ID FROM {self.database}."{self.schema}".{join_table}
+                WHERE LOWER({join_col}) = LOWER('{entity_val}')
+            )"""
+        else:
+            where_clause = f"WHERE LOWER({entity_col}) = LOWER('{entity_val}')"
+        
+        # Query current state
+        query = f"""
+        SELECT 
+            SUM({metric_col}) as total,
+            COUNT(*) as row_count,
+            AVG({metric_col}) as avg_value,
+            MIN({metric_col}) as min_value,
+            MAX({metric_col}) as max_value
+        FROM {self.database}."{self.schema}".{table}
+        {where_clause}
+        """
+        
+        print(f"\n🔍 Analyzing current data...")
+        print(f"   Query: {query}")
+        
+        cursor.execute(query)
+        row = cursor.fetchone()
+        
+        current_total = float(row[0]) if row[0] else 0
+        row_count = int(row[1])
+        avg_value = float(row[2]) if row[2] else 0
+        min_value = float(row[3]) if row[3] else 0
+        max_value = float(row[4]) if row[4] else 0
+        
+        gap = target - current_total
+        
+        return {
+            'current_total': current_total,
+            'row_count': row_count,
+            'avg_value': avg_value,
+            'min_value': min_value,
+            'max_value': max_value,
+            'gap': gap
+        }
+    
+    def generate_strategy_options(self, adjustment: Dict, analysis: Dict) -> List[Dict]:
+        """
+        Generate 3 strategy options for achieving the target
+        
+        Returns list of strategies with details
+        """
+        table = adjustment['table']
+        entity_col = adjustment['entity_column']
+        entity_val = adjustment['entity_value']
+        metric_col = adjustment['metric_column']
+        target = adjustment['target_value']
+        
+        # Build WHERE clause - handle joins if needed
+        if adjustment.get('needs_join'):
+            join_table = adjustment['needs_join']
+            join_col = adjustment['join_column']
+            where_clause = f"""{entity_col} IN (
+                SELECT PRODUCT_ID FROM {self.database}."{self.schema}".{join_table}
+                WHERE LOWER({join_col}) = LOWER('{entity_val}')
+            )"""
+        else:
+            where_clause = f"LOWER({entity_col}) = LOWER('{entity_val}')"
+        
+        current = analysis['current_total']
+        gap = analysis['gap']
+        row_count = analysis['row_count']
+        
+        if gap <= 0:
+            return [{
+                'id': 'decrease',
+                'name': 'Decrease All',
+                'description': f"Current value ({current:,.0f}) already exceeds target ({target:,.0f})",
+                'sql': None
+            }]
+        
+        strategies = []
+        
+        # Strategy A: Distribute increase across all rows
+        multiplier = target / current if current > 0 else 1
+        percentage_increase = (multiplier - 1) * 100
+        
+        strategies.append({
+            'id': 'A',
+            'name': 'Distribute Across All Transactions',
+            'description': f"Increase all {row_count:,} existing transactions by {percentage_increase:.1f}%",
+            'details': {
+                'approach': 'Multiply all existing values',
+                'rows_affected': row_count,
+                'new_avg': analysis['avg_value'] * multiplier
+            },
+            'sql': f"""UPDATE {self.database}."{self.schema}".{table}
+SET {metric_col} = {metric_col} * {multiplier:.6f}
+WHERE {where_clause}"""
+        })
+        
+        # Strategy B: Add new large transactions
+        num_new_transactions = max(1, int(gap / (analysis['max_value'] * 2)))  # Add transactions 2x the current max
+        value_per_new = gap / num_new_transactions
+        
+        strategies.append({
+            'id': 'B',
+            'name': 'Add New Large Transactions',
+            'description': f"Insert {num_new_transactions} new transactions of ${value_per_new:,.0f} each",
+            'details': {
+                'approach': 'Create new outlier transactions',
+                'rows_to_add': num_new_transactions,
+                'value_each': value_per_new
+            },
+            'sql': f"""-- INSERT new transactions (requires full row data)
+-- INSERT INTO {self.database}."{self.schema}".{table} ({entity_col}, {metric_col}, ...)
+-- VALUES ('{entity_val}', {value_per_new}, ...)
+-- NOTE: This requires knowing all required columns in the table"""
+        })
+        
+        # Strategy C: Boost top transactions
+        top_n = min(10, max(1, row_count // 10))  # Top 10% of transactions
+        boost_needed_per_row = gap / top_n
+        
+        strategies.append({
+            'id': 'C',
+            'name': 'Boost Top Transactions',
+            'description': f"Increase the top {top_n} transactions by ${boost_needed_per_row:,.0f} each",
+            'details': {
+                'approach': 'Create outliers from existing top transactions',
+                'rows_affected': top_n,
+                'boost_per_row': boost_needed_per_row
+            },
+            'sql': f"""WITH top_rows AS (
+  SELECT * FROM {self.database}."{self.schema}".{table}
+  WHERE {where_clause}
+  ORDER BY {metric_col} DESC
+  LIMIT {top_n}
+)
+UPDATE {self.database}."{self.schema}".{table} t
+SET {metric_col} = {metric_col} + {boost_needed_per_row:.2f}
+WHERE EXISTS (
+  SELECT 1 FROM top_rows
+  WHERE top_rows.rowid = t.rowid
+)"""
+        })
+        
+        return strategies
+    
+    def present_options(self, adjustment: Dict, analysis: Dict, strategies: List[Dict]) -> None:
+        """Display options to user in a friendly format"""
+        
+        print("\n" + "="*80)
+        print("📊 DATA ADJUSTMENT OPTIONS")
+        print("="*80)
+        
+        entity = f"{adjustment['entity_column']}='{adjustment['entity_value']}'"
+        metric = adjustment['metric_column']
+        
+        print(f"\n🎯 Goal: Adjust {metric} for {entity}")
+        print(f"   Current Total: ${analysis['current_total']:,.0f}")
+        print(f"   Target Total:  ${adjustment['target_value']:,.0f}")
+        print(f"   Gap to Fill:   ${analysis['gap']:,.0f} ({analysis['gap']/analysis['current_total']*100:.1f}% increase)")
+        
+        print(f"\n📈 Current Data:")
+        print(f"   Rows: {analysis['row_count']:,}")
+        print(f"   Average: ${analysis['avg_value']:,.0f}")
+        print(f"   Range: ${analysis['min_value']:,.0f} - ${analysis['max_value']:,.0f}")
+        
+        print(f"\n" + "="*80)
+        print("STRATEGY OPTIONS:")
+        print("="*80)
+        
+        for strategy in strategies:
+            print(f"\n[{strategy['id']}] {strategy['name']}")
+            print(f"    {strategy['description']}")
+            
+            if 'details' in strategy:
+                details = strategy['details']
+                print(f"    Details:")
+                for key, value in details.items():
+                    if isinstance(value, float):
+                        print(f"      - {key}: ${value:,.0f}")
+                    else:
+                        print(f"      - {key}: {value}")
+            
+            if strategy['sql']:
+                print(f"\n    SQL Preview:")
+                sql_preview = strategy['sql'].strip().split('\n')
+                for line in sql_preview[:3]:  # Show first 3 lines
+                    print(f"      {line}")
+                if len(sql_preview) > 3:
+                    print(f"      ... ({len(sql_preview)-3} more lines)")
+        
+        print("\n" + "="*80)
+    
+    def execute_strategy(self, strategy: Dict) -> Dict:
+        """Execute the chosen strategy"""
+        
+        if not strategy['sql']:
+            return {
+                'success': False,
+                'error': 'This strategy requires manual implementation (INSERT statements)'
+            }
+        
+        cursor = self.conn.cursor()
+        
+        print(f"\n⚙️  Executing strategy: {strategy['name']}")
+        print(f"   SQL: {strategy['sql'][:200]}...")
+        
+        try:
+            cursor.execute(strategy['sql'])
+            rows_affected = cursor.rowcount
+            self.conn.commit()
+            
+            return {
+                'success': True,
+                'message': f"✅ Updated {rows_affected} rows",
+                'rows_affected': rows_affected
+            }
+        except Exception as e:
+            self.conn.rollback()
+            return {
+                'success': False,
+                'error': str(e)
+            }
+    
+    def get_available_tables(self) -> List[str]:
+        """Get list of tables in the schema"""
+        cursor = self.conn.cursor()
+        cursor.execute(f"""
+            SELECT TABLE_NAME 
+            FROM {self.database}.INFORMATION_SCHEMA.TABLES 
+            WHERE TABLE_SCHEMA = '{self.schema}'
+        """)
+        tables = [row[0] for row in cursor.fetchall()]
+        return tables
+    
+    def close(self):
+        """Close connection"""
+        if self.conn:
+            self.conn.close()
+
+
+# Test/demo function
+def demo_conversation():
+    """Simulate the conversational flow"""
+    
+    print("""
+╔════════════════════════════════════════════════════════════╗
+║                                                            ║
+║     Conversational Data Adjuster Demo                     ║
+║                                                            ║
+╚════════════════════════════════════════════════════════════╝
+    """)
+    
+    # Setup from environment variables
+    from dotenv import load_dotenv
+    load_dotenv()
+    
+    adjuster = ConversationalDataAdjuster(
+        database=os.getenv('SNOWFLAKE_DATABASE'),
+        schema="20251116_140933_AMAZO_SAL",  # Schema from deployment
+        model_id="3c97b0d6-448b-440a-b628-bac1f3d73049"
+    )
+    
+    print(f"Using database: {os.getenv('SNOWFLAKE_DATABASE')}")
+    print(f"Using schema: 20251116_140933_AMAZO_SAL")
+    
+    adjuster.connect()
+    
+    # User request (using actual product from our data)
+    user_request = "increase 1080p Webcam sales to 50 billion"
+    print(f"\n💬 User: \"{user_request}\"")
+    print(f"   (Current: ~$17.6B, Target: $50B)")
+    
+    # Step 1: Parse request
+    tables = adjuster.get_available_tables()
+    adjustment = adjuster.parse_adjustment_request(user_request, tables)
+    
+    # Step 2: Analyze current data
+    analysis = adjuster.analyze_current_data(adjustment)
+    
+    # Step 3: Generate strategies
+    strategies = adjuster.generate_strategy_options(adjustment, analysis)
+    
+    # Step 4: Present options
+    adjuster.present_options(adjustment, analysis, strategies)
+    
+    # Step 5: User picks (simulated)
+    print("\n💬 User: \"Use strategy A\"")
+    chosen_strategy = strategies[0]  # Strategy A
+    
+    # Step 6: Execute
+    result = adjuster.execute_strategy(chosen_strategy)
+    
+    if result['success']:
+        print(f"\n{result['message']}")
+    else:
+        print(f"\n❌ Error: {result.get('error')}")
+    
+    adjuster.close()
+
+
+if __name__ == "__main__":
+    demo_conversation()
+

data_adjuster.py

@@ -0,0 +1,212 @@
+"""
+Data Adjustment Module for Liveboard Refinement
+
+Allows natural language adjustments to demo data:
+- "make Product A 55% higher"
+- "increase Customer B revenue by 20%"
+- "set profit margin to 15% for Segment C"
+"""
+
+import re
+from typing import Dict, Optional
+from snowflake_auth import get_snowflake_connection
+from openai import OpenAI
+import os
+
+
+class DataAdjuster:
+    """Adjust demo data based on natural language requests"""
+    
+    def __init__(self, database: str, schema: str):
+        self.database = database
+        self.schema = schema
+        self.conn = None
+        self.openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+    
+    def connect(self):
+        """Connect to Snowflake"""
+        self.conn = get_snowflake_connection()
+        self.conn.cursor().execute(f"USE DATABASE {self.database}")
+        self.conn.cursor().execute(f"USE SCHEMA {self.schema}")
+        print(f"✅ Connected to {self.database}.{self.schema}")
+    
+    def parse_adjustment_request(self, request: str, available_columns: list) -> Dict:
+        """
+        Parse natural language adjustment request using AI
+        
+        Args:
+            request: e.g., "make Product A 55% higher" or "increase revenue for Customer B by 20%"
+            available_columns: List of column names in the data
+        
+        Returns:
+            {
+                'entity_column': 'product_name',
+                'entity_value': 'Product A',
+                'metric_column': 'total_revenue',
+                'adjustment_type': 'percentage_increase',
+                'adjustment_value': 55
+            }
+        """
+        prompt = f"""Parse this data adjustment request and extract structured information.
+
+Request: "{request}"
+
+Available columns in the dataset: {', '.join(available_columns)}
+
+Extract:
+1. entity_column: Which column identifies what to change (e.g., product_name, customer_segment)
+2. entity_value: The specific value to filter by (e.g., "Product A", "Electronics")
+3. metric_column: Which numeric column to adjust (e.g., total_revenue, profit_margin, quantity_sold)
+4. adjustment_type: One of: "percentage_increase", "percentage_decrease", "set_value", "add_value"
+5. adjustment_value: The numeric value (e.g., 55 for "55%", 1000 for "add 1000")
+
+Return ONLY a JSON object with these fields. If you can't parse it, return {{"error": "description"}}.
+
+Examples:
+- "make Product A 55% higher" → {{"entity_column": "product_name", "entity_value": "Product A", "metric_column": "total_revenue", "adjustment_type": "percentage_increase", "adjustment_value": 55}}
+- "set profit margin to 15% for Electronics" → {{"entity_column": "category", "entity_value": "Electronics", "metric_column": "profit_margin", "adjustment_type": "set_value", "adjustment_value": 15}}
+"""
+        
+        response = self.openai_client.chat.completions.create(
+            model="gpt-4o",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0
+        )
+        
+        import json
+        result = json.loads(response.choices[0].message.content)
+        return result
+    
+    def get_available_columns(self, table_name: str) -> list:
+        """Get list of columns from a table"""
+        cursor = self.conn.cursor()
+        cursor.execute(f"""
+            SELECT COLUMN_NAME 
+            FROM {self.database}.INFORMATION_SCHEMA.COLUMNS 
+            WHERE TABLE_SCHEMA = '{self.schema}' 
+            AND TABLE_NAME = '{table_name.upper()}'
+        """)
+        columns = [row[0].lower() for row in cursor.fetchall()]
+        return columns
+    
+    def apply_adjustment(self, table_name: str, adjustment: Dict) -> Dict:
+        """
+        Apply the parsed adjustment to the database
+        
+        Returns:
+            {'success': bool, 'message': str, 'rows_affected': int}
+        """
+        if 'error' in adjustment:
+            return {'success': False, 'message': adjustment['error']}
+        
+        cursor = self.conn.cursor()
+        
+        # Build the UPDATE statement
+        entity_col = adjustment['entity_column']
+        entity_val = adjustment['entity_value']
+        metric_col = adjustment['metric_column']
+        adj_type = adjustment['adjustment_type']
+        adj_value = adjustment['adjustment_value']
+        
+        # Calculate new value based on adjustment type
+        if adj_type == 'percentage_increase':
+            new_value_expr = f"{metric_col} * (1 + {adj_value}/100.0)"
+        elif adj_type == 'percentage_decrease':
+            new_value_expr = f"{metric_col} * (1 - {adj_value}/100.0)"
+        elif adj_type == 'set_value':
+            new_value_expr = f"{adj_value}"
+        elif adj_type == 'add_value':
+            new_value_expr = f"{metric_col} + {adj_value}"
+        else:
+            return {'success': False, 'message': f"Unknown adjustment type: {adj_type}"}
+        
+        # Execute UPDATE
+        update_sql = f"""
+        UPDATE {self.database}.{self.schema}.{table_name}
+        SET {metric_col} = {new_value_expr}
+        WHERE LOWER({entity_col}) = LOWER('{entity_val}')
+        """
+        
+        print(f"\n🔧 Executing adjustment:")
+        print(f"   SQL: {update_sql}")
+        
+        try:
+            cursor.execute(update_sql)
+            rows_affected = cursor.rowcount
+            self.conn.commit()
+            
+            return {
+                'success': True,
+                'message': f"Updated {rows_affected} rows: {entity_col}='{entity_val}', adjusted {metric_col} by {adj_type}",
+                'rows_affected': rows_affected
+            }
+        except Exception as e:
+            return {
+                'success': False,
+                'message': f"Database error: {str(e)}"
+            }
+    
+    def adjust_data_for_liveboard(self, request: str, table_name: str) -> Dict:
+        """
+        Full workflow: parse request, update data
+        
+        Args:
+            request: Natural language request like "make Product A 55% higher"
+            table_name: Name of the table to update
+        
+        Returns:
+            Result dictionary with success status and details
+        """
+        if not self.conn:
+            self.connect()
+        
+        # Get available columns
+        columns = self.get_available_columns(table_name)
+        print(f"📋 Available columns: {', '.join(columns)}")
+        
+        # Parse the request
+        print(f"\n🤔 Parsing request: '{request}'")
+        adjustment = self.parse_adjustment_request(request, columns)
+        print(f"✅ Parsed: {adjustment}")
+        
+        if 'error' in adjustment:
+            return {'success': False, 'error': adjustment['error']}
+        
+        # Apply the adjustment
+        result = self.apply_adjustment(table_name, adjustment)
+        
+        return result
+    
+    def close(self):
+        """Close database connection"""
+        if self.conn:
+            self.conn.close()
+
+
+# Example usage function
+def test_data_adjustment():
+    """Test the data adjustment functionality"""
+    adjuster = DataAdjuster(
+        database="DEMO_DATABASE",
+        schema="DEMO_SCHEMA"
+    )
+    
+    # Example: "make Product A 55% higher"
+    result = adjuster.adjust_data_for_liveboard(
+        request="make Product A 55% higher",
+        table_name="FACT_SALES"
+    )
+    
+    print(f"\n{'='*60}")
+    if result['success']:
+        print(f"✅ SUCCESS: {result['message']}")
+        print(f"📊 Rows affected: {result['rows_affected']}")
+    else:
+        print(f"❌ FAILED: {result.get('error', result.get('message'))}")
+    
+    adjuster.close()
+
+
+if __name__ == "__main__":
+    test_data_adjustment()
+

demo_prep.py

@@ -362,33 +362,33 @@ def execute_population_script(python_code, schema_name, skip_modifications=False
             cleaned_code = cleaned_code.replace('os.getenv("SNOWFLAKE_SCHEMA")', f'"{schema_name}"')
         else:
             print("⚠️ LLM-generated code - applying safety fixes")
-            # CRITICAL FIX: Remove schema from conn_params to avoid duplicate schema parameter
+        # CRITICAL FIX: Remove schema from conn_params to avoid duplicate schema parameter
             # Only add if not already present (new templates include it by default)
             if "conn_params.pop('schema'" not in clean_code:
                 cleaned_code = replace_with_indentation(
                     clean_code,
-                    "conn_params = get_snowflake_connection_params()",
+            "conn_params = get_snowflake_connection_params()",
                     ["conn_params.pop('schema', None)  # Remove schema to avoid duplicate"]
-                )
+        )
             else:
                 cleaned_code = clean_code
                 print("✅ Schema pop already in code, skipping injection")
-            
-            # Simple and safe schema replacement - just replace the placeholder
-            cleaned_code = cleaned_code.replace("os.getenv('SNOWFLAKE_SCHEMA')", f"'{schema_name}'")
-            cleaned_code = cleaned_code.replace('os.getenv("SNOWFLAKE_SCHEMA")', f'"{schema_name}"')
-            
-            # FIX: Remove fake.unique() calls that cause "duplicated values after 1,000 iterations" error
-            cleaned_code = cleaned_code.replace("fake.unique.word()", "fake.word()")
-            cleaned_code = cleaned_code.replace("fake.unique.email()", "fake.email()")
-            cleaned_code = cleaned_code.replace("fake.unique.company()", "fake.company()")
+        
+        # Simple and safe schema replacement - just replace the placeholder
+        cleaned_code = cleaned_code.replace("os.getenv('SNOWFLAKE_SCHEMA')", f"'{schema_name}'")
+        cleaned_code = cleaned_code.replace('os.getenv("SNOWFLAKE_SCHEMA")', f'"{schema_name}"')
+        
+        # FIX: Remove fake.unique() calls that cause "duplicated values after 1,000 iterations" error
+        cleaned_code = cleaned_code.replace("fake.unique.word()", "fake.word()")
+        cleaned_code = cleaned_code.replace("fake.unique.email()", "fake.email()")
+        cleaned_code = cleaned_code.replace("fake.unique.company()", "fake.company()")
 
-            # FIX: Truncate phone numbers to avoid extension overflow (e.g., '790-923-3730x07350')
-            cleaned_code = cleaned_code.replace("fake.phone_number()", "fake.phone_number()[:20]")
+        # FIX: Truncate phone numbers to avoid extension overflow (e.g., '790-923-3730x07350')
+        cleaned_code = cleaned_code.replace("fake.phone_number()", "fake.phone_number()[:20]")
 
-            # FIX: Convert SQLite-style ? placeholders to Snowflake-style %s placeholders
-            cleaned_code = re.sub(r'\bVALUES\s*\(\?', 'VALUES (%s', cleaned_code)
-            cleaned_code = re.sub(r',\s*\?', ', %s', cleaned_code)
+        # FIX: Convert SQLite-style ? placeholders to Snowflake-style %s placeholders
+        cleaned_code = re.sub(r'\bVALUES\s*\(\?', 'VALUES (%s', cleaned_code)
+        cleaned_code = re.sub(r',\s*\?', ', %s', cleaned_code)
         
         # DEBUG: Save modified code
         with open(os_module.path.join(debug_dir, '2_after_modifications.py'), 'w') as f:
@@ -1548,6 +1548,13 @@ TECHNICAL REQUIREMENTS:
 - Include realistic column names that match the business context
 - Add proper constraints and relationships
 
+TABLE NAMING REQUIREMENTS:
+- **DO NOT use DIM_ or FACT_ prefixes** (e.g., NOT DIM_PRODUCT or FACT_SALES)
+- Use simple, descriptive table names (e.g., PRODUCTS, CUSTOMERS, SALES, ORDERS)
+- Dimension tables: Use plural nouns (CUSTOMERS, PRODUCTS, WAREHOUSES)
+- Fact tables: Use descriptive names (SALES, TRANSACTIONS, ORDERS, INVENTORY_MOVEMENTS)
+- Keep names concise and business-friendly
+
 SNOWFLAKE SYNTAX EXAMPLES:
 - Auto-increment: ColumnID INT IDENTITY(1,1) PRIMARY KEY
 - NOT: ColumnID INT PRIMARY KEY AUTO_INCREMENT
@@ -1664,15 +1671,21 @@ SCRIPT REQUIREMENTS:
    **SNOWFLAKE SYNTAX**: Use %s placeholders, NOT ? placeholders
    Example: cursor.executemany("INSERT INTO table (col1, col2, col3) VALUES (%s, %s, %s)", batch_data_list)
 3. Build data in Python lists/arrays first, then batch insert (do NOT use individual cursor.execute in loops)
-4. Populate tables with realistic data volumes (1000+ rows per table)
-5. Create baseline normal data patterns
-6. Inject strategic outliers with STRUCTURED COMMENTS (see format below)
-7. Include scenarios showcasing: {persona_config['demo_objectives']}
-8. NO explanatory text, just executable Python code
-9. Focus on scenarios that resonate with {persona_config['target_persona']} and prove ROI
-10. Include data validation to ensure referential integrity
-11. Add progress logging for each table population
-12. Ensure all foreign key relationships are maintained
+4. Populate tables with realistic data volumes (10,000+ rows for transactions)
+5. **REALISTIC TRANSACTION AMOUNTS**:
+   - For e-commerce/retail: $50-$2,000 per order (rare large orders up to $50,000)
+   - For B2B: $1,000-$50,000 per order (enterprise orders up to $500,000)
+   - **NEVER create individual transactions over $1M** - use many small transactions
+   - To reach high totals: generate MANY transactions, not huge individual amounts
+   - Example: $40B total = 100,000+ transactions averaging $400k each
+6. Create baseline normal data patterns
+7. Inject strategic outliers with STRUCTURED COMMENTS (see format below)
+8. Include scenarios showcasing: {persona_config['demo_objectives']}
+9. NO explanatory text, just executable Python code
+10. Focus on scenarios that resonate with {persona_config['target_persona']} and prove ROI
+11. Include data validation to ensure referential integrity
+12. Add progress logging for each table population
+13. Ensure all foreign key relationships are maintained
 
 OUTLIER DOCUMENTATION FORMAT (REQUIRED):
 For each strategic outlier, add structured comments BEFORE the code that creates it:

launch_chat.py

@@ -4,8 +4,15 @@ Quick launcher for the new chat-based interface
 """
 
 from chat_interface import create_chat_interface
+from datetime import datetime
 
 if __name__ == "__main__":
+    # Write directly to log file
+    with open('/tmp/chat_output.log', 'a') as f:
+        f.write(f"\n{'='*70}\n")
+        f.write(f"TSDB APP START: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
+        f.write(f"{'='*70}\n\n")
+    
     print("🚀 Starting Chat-Based Demo Builder...")
     print("=" * 60)
     print()
@@ -16,14 +23,14 @@ if __name__ == "__main__":
     print("  • Editable AI model selector")
     print("  • Quick action buttons")
     print()
-    print("🌐 Opening in browser at http://localhost:7862")
+    print("🌐 Opening in browser at http://localhost:7863")
     print("=" * 60)
     
     app = create_chat_interface()
     
     app.launch(
         server_name="0.0.0.0",
-        server_port=7862,
+        server_port=7863,
         share=False,
         inbrowser=True,
         debug=True

liveboard_creator.py

@@ -15,6 +15,7 @@ import json
 import yaml
 import os
 import re
+import requests
 from typing import Dict, List, Optional
 
 
@@ -145,6 +146,132 @@ class OutlierParser:
         return outliers
 
 
+def clean_viz_title(question: str) -> str:
+    """
+    Extract a clean, short title from a verbose question
+    
+    Examples:
+        "What is the total revenue? Show only..." -> "Total Revenue"
+        "Show the top 10 products by revenue..." -> "Top 10 Products by Revenue"
+    """
+    # Remove instructions after question mark or period
+    question = question.split('?')[0].strip()
+    question = question.split('.')[0].strip()
+    
+    # Common patterns to clean
+    patterns = [
+        (r'^What is the ', ''),
+        (r'^What are the ', ''),
+        (r'^Show me the ', ''),
+        (r'^Show the ', ''),
+        (r'^Show ', ''),
+        (r'^Create a detailed table showing ', ''),
+        (r'^How (?:does|do) ', ''),
+        (r' for Amazon\.com', ''),
+        (r' for the company', ''),
+    ]
+    
+    clean = question
+    for pattern, replacement in patterns:
+        clean = re.sub(pattern, replacement, clean, flags=re.IGNORECASE)
+    
+    # Capitalize first letter
+    if clean:
+        clean = clean[0].upper() + clean[1:]
+    
+    # Limit length
+    if len(clean) > 80:
+        clean = clean[:77] + "..."
+    
+    return clean or question[:80]
+
+
+def extract_brand_colors_from_css(css_url: str) -> List[str]:
+    """Extract color codes from a CSS file, filtering for brand-appropriate colors"""
+    try:
+        response = requests.get(css_url, timeout=5)
+        if response.status_code == 200:
+            css_content = response.text
+            # Find all hex colors
+            hex_colors = re.findall(r'#([0-9A-Fa-f]{6}|[0-9A-Fa-f]{3})\b', css_content)
+            
+            # Convert 3-digit to 6-digit and calculate brightness
+            color_data = []
+            for color in hex_colors:
+                if len(color) == 3:
+                    color = ''.join([c*2 for c in color])
+                
+                # Calculate brightness
+                r, g, b = int(color[0:2], 16), int(color[2:4], 16), int(color[4:6], 16)
+                brightness = (r * 299 + g * 587 + b * 114) / 1000
+                
+                # Filter out too dark (< 30) or too light (> 240) colors
+                # These are usually backgrounds, not brand colors
+                if 30 < brightness < 240:
+                    # Calculate saturation
+                    max_c = max(r, g, b)
+                    min_c = min(r, g, b)
+                    saturation = (max_c - min_c) / max_c if max_c > 0 else 0
+                    
+                    color_data.append({
+                        'color': f'#{color}',
+                        'brightness': brightness,
+                        'saturation': saturation
+                    })
+            
+            # Deduplicate
+            seen = set()
+            unique_colors = []
+            for c in color_data:
+                if c['color'] not in seen:
+                    seen.add(c['color'])
+                    unique_colors.append(c)
+            
+            # Sort by saturation (vibrant colors first) then brightness
+            unique_colors.sort(key=lambda x: (x['saturation'], x['brightness']), reverse=True)
+            
+            # Take top 5 most vibrant colors
+            brand_colors = [c['color'] for c in unique_colors[:5]]
+            return brand_colors if brand_colors else None
+    except:
+        pass
+    return None
+
+
+def create_branded_note_tile(company_data: Dict, use_case: str, answers: List[Dict], 
+                              source_info: List[str], outlier_section: str = "") -> str:
+    """
+    Create a branded note tile matching golden demo format
+    Uses ThoughtSpot's native theme classes for proper rendering
+    
+    Args:
+        company_data: Company information including logo_url and brand_colors
+        use_case: Use case name
+        answers: List of visualization answers
+        source_info: List of source information strings
+        outlier_section: HTML for outlier highlights section
+    
+    Returns:
+        HTML string for the note tile
+    """
+    # Extract company info
+    company_name = company_data.get('name', 'Company')
+    industry = company_data.get('industry', 'business')
+    viz_count = len(answers)
+    
+    # Build source info text
+    if source_info:
+        source_text = ', '.join(source_info)
+    else:
+        source_text = 'business data'
+    
+    # Golden demo format: EXACT structure from "Weekly Updates" tile
+    # Multiple paragraphs with white text and bold teal highlights
+    note_tile = f"""<h2 class="theme-module__editor-h2" dir="ltr"><span style="color: rgb(255, 255, 255); white-space: pre-wrap;">{company_name}</span></h2><hr><p class="theme-module__editor-paragraph" dir="ltr"><span style="color: rgb(255, 255, 255); white-space: pre-wrap;">This liveboard features </span><b><strong class="theme-module__editor-text-bold" style="color: rgb(64, 193, 192); white-space: pre-wrap;">{viz_count} AI-generated visualizations</strong></b><span style="color: rgb(255, 255, 255); white-space: pre-wrap;"> analyzing {industry} performance</span></p><p class="theme-module__editor-paragraph"><br></p><p class="theme-module__editor-paragraph" dir="ltr"><span style="color: rgb(255, 255, 255); white-space: pre-wrap;">Insights powered by </span><b><strong class="theme-module__editor-text-bold" style="color: rgb(64, 193, 192); white-space: pre-wrap;">ThoughtSpot AI</strong></b><span style="color: rgb(255, 255, 255); white-space: pre-wrap;"> across </span><b><strong class="theme-module__editor-text-bold" style="color: rgb(64, 193, 192); white-space: pre-wrap;">{source_text}</strong></b></p><p class="theme-module__editor-paragraph"><br></p><p class="theme-module__editor-paragraph" dir="ltr"><span style="color: rgb(255, 255, 255); white-space: pre-wrap;">Created with </span><b><strong class="theme-module__editor-text-bold" style="color: rgb(64, 193, 192); white-space: pre-wrap;">Demo Wire</strong></b><span style="color: rgb(255, 255, 255); white-space: pre-wrap;"> automation platform</span></p>"""
+    
+    return note_tile
+
+
 class QueryTranslator:
     """Translate natural language queries to ThoughtSpot search syntax"""
 
@@ -302,7 +429,8 @@ class LiveboardCreator:
         self.model_columns = self._fetch_model_columns()
         
         # Use selected LLM model instead of hardcoded OpenAI
-        from main_research import MultiLLMResearcher, map_llm_display_to_provider
+        from main_research import MultiLLMResearcher
+        from demo_prep import map_llm_display_to_provider
         model_to_use = llm_model or 'claude-sonnet-4.5'
         provider_name, model_name_llm = map_llm_display_to_provider(model_to_use)
         self.llm_researcher = MultiLLMResearcher(provider=provider_name, model=model_name_llm)
@@ -1013,7 +1141,7 @@ Examples:
                 'name': viz_config.get('name', 'Text'),
                 'description': viz_config.get('description', ''),
                 'tables': [{
-                    'id': self.model_name,
+                    'id': self.model_name,  # Use model NAME for TML (not GUID!)
                     'name': self.model_name
                 }],
                 'text_tile': {
@@ -1134,7 +1262,7 @@ Examples:
                 'name': viz_config['name'],
                 'description': viz_config.get('description', ''),
                 'tables': [{
-                    'id': self.model_name,  # Use model name not GUID for TML
+                    'id': self.model_name,  # Use model NAME for TML (not GUID!)
                     'name': self.model_name
                 }],
                 'search_query': search_query,
@@ -1219,6 +1347,9 @@ For each visualization, create a JSON object with:
 Guidelines:
 - Mix chart types (don't use all the same type)
 - Include at least 1-2 KPI charts for key metrics
+  * IMPORTANT: For KPIs, ALWAYS include time_column and granularity (monthly/quarterly/yearly)
+  * This enables sparklines and percent change comparisons (MoM/QoQ/YoY)
+  * Example KPI: measure="Total_revenue", time_column="Order_date", granularity="monthly"
 - Include trend analysis with LINE or AREA charts
 - Include comparisons with COLUMN or BAR charts
 - Use appropriate time filters for business context
@@ -1239,10 +1370,31 @@ Return ONLY a valid JSON object with structure:
         try:
             messages = [{"role": "user", "content": prompt}]
             response_text = self.llm_researcher.make_request(messages, temperature=0.7, max_tokens=4000, stream=False)
+            
+            # Debug: Check what we got back
+            print(f"🔍 DEBUG: AI response type: {type(response_text)}")
+            print(f"🔍 DEBUG: AI response length: {len(response_text) if response_text else 0}")
+            if response_text:
+                print(f"🔍 DEBUG: AI response first 200 chars: {response_text[:200]}")
+            else:
+                print(f"❌ ERROR: AI returned empty response!")
+                return self._generate_fallback_visualizations(available_measures, date_columns)
+            
+            # Strip markdown code fences if present
+            response_text = response_text.strip()
+            if response_text.startswith('```'):
+                # Remove opening fence (```json or ```)
+                lines = response_text.split('\n')
+                response_text = '\n'.join(lines[1:])
+                # Remove closing fence
+                if response_text.endswith('```'):
+                    response_text = response_text[:-3].strip()
+            
             result = json.loads(response_text)
             return result.get('visualizations', [])
         except Exception as e:
             print(f"Error generating visualizations: {e}")
+            print(f"   Response text was: {response_text[:500] if response_text else 'None'}")
             # Return fallback simple visualizations
             return self._generate_fallback_visualizations(available_measures, date_columns)
 
@@ -1255,11 +1407,13 @@ Return ONLY a valid JSON object with structure:
         fallback_viz = []
 
         if measures and date_columns:
-            # KPI of first measure
+            # KPI of first measure with time-series for sparkline
             fallback_viz.append({
                 'name': f'Total {measures[0]}',
                 'chart_type': 'KPI',
-                'measure': measures[0]
+                'measure': measures[0],
+                'time_column': date_columns[0],
+                'granularity': 'monthly'
             })
 
             # Trend of first measure
@@ -1273,11 +1427,13 @@ Return ONLY a valid JSON object with structure:
             })
 
             if len(measures) > 1:
-                # Second measure KPI
+                # Second measure KPI with time-series for sparkline
                 fallback_viz.append({
                     'name': f'Total {measures[1]}',
                     'chart_type': 'KPI',
-                    'measure': measures[1]
+                    'measure': measures[1],
+                    'time_column': date_columns[0],
+                    'granularity': 'quarterly'
                 })
 
         return fallback_viz
@@ -1341,28 +1497,30 @@ Return ONLY a valid JSON object with structure:
         else:
             viz_configs = []
 
+        # TEMPORARILY DISABLED - Text tiles causing TML import errors
         # Add text tiles for context (like in sample liveboard)
-        text_tiles = [
-            {
-                'id': 'Text_1',
-                'name': '📊 Dashboard Overview',
-                'chart_type': 'TEXT',
-                'text_content': f"## {company_data.get('name', 'Company')} Analytics\n\n{use_case} insights and metrics",
-                'background_color': '#2E3D4D'  # Dark blue-gray
-            },
-            {
-                'id': 'Text_2',
-                'name': 'Key Insights',
-                'chart_type': 'TEXT',
-                'text_content': "💡 **Key Performance Indicators**\n\nTrack trends and identify opportunities",
-                'background_color': '#85016b'  # Pink (from sample)
-            }
-        ]
+        text_tiles = []
+        # text_tiles = [
+        #     {
+        #         'id': 'Text_1',
+        #         'name': '📊 Dashboard Overview',
+        #         'chart_type': 'TEXT',
+        #         'text_content': f"## {company_data.get('name', 'Company')} Analytics\n\n{use_case} insights and metrics",
+        #         'background_color': '#2E3D4D'  # Dark blue-gray
+        #     },
+        #     {
+        #         'id': 'Text_2',
+        #         'name': 'Key Insights',
+        #         'chart_type': 'TEXT',
+        #         'text_content': "💡 **Key Performance Indicators**\n\nTrack trends and identify opportunities",
+        #         'background_color': '#85016b'  # Pink (from sample)
+        #     }
+        # ]
 
         # Create text tile visualizations
-        for text_config in text_tiles:
-            viz_tml = self.create_visualization_tml(text_config)
-            visualizations.append(viz_tml)
+        # for text_config in text_tiles:
+        #     viz_tml = self.create_visualization_tml(text_config)
+        #     visualizations.append(viz_tml)
 
         # Create visualization TML objects
         if viz_configs:
@@ -1392,7 +1550,8 @@ Return ONLY a valid JSON object with structure:
             }
         }
 
-        return json.dumps(liveboard_tml, indent=2)
+        # Convert to YAML format (TML is YAML, not JSON)
+        return yaml.dump(liveboard_tml, default_flow_style=False, sort_keys=False)
 
     def _check_liveboard_errors(self, liveboard_id: str) -> Dict:
         """
@@ -1484,6 +1643,11 @@ Return ONLY a valid JSON object with structure:
                 - error: Error message if failed
         """
         try:
+            # Debug: Log the TML being sent
+            print(f"🔍 DEBUG: Sending TML to ThoughtSpot")
+            print(f"🔍 DEBUG: TML length: {len(liveboard_tml_json)}")
+            print(f"🔍 DEBUG: TML first 500 chars:\n{liveboard_tml_json[:500]}")
+            
             response = self.ts_client.session.post(
                 f"{self.ts_client.base_url}/api/rest/2.0/metadata/tml/import",
                 headers=self.ts_client.headers,
@@ -1634,6 +1798,9 @@ def _create_kpi_question_from_outlier(outlier: Dict) -> Optional[str]:
     """
     Create companion KPI question from outlier if KPI metric is specified.
     
+    For sparklines, KPI questions need time dimension!
+    Example: "What is the total revenue by week over the last 8 quarters?"
+    
     Args:
         outlier: Dictionary with outlier metadata
     
@@ -1645,14 +1812,14 @@ def _create_kpi_question_from_outlier(outlier: Dict) -> Optional[str]:
     
     kpi_metric = outlier.get('kpi_metric', '')
     if kpi_metric:
-        # Create a question about the total/aggregate
-        return f"What is the total {kpi_metric}?"
+        # Include time dimension for sparkline visualization
+        return f"What is the total {kpi_metric} by week over the last 8 quarters?"
     
     # Fallback: extract first measure from viz_measure_types
     measure_types = outlier.get('viz_measure_types', '')
     if measure_types:
         first_measure = measure_types.split(',')[0].strip()
-        return f"What is the total {first_measure}?"
+        return f"What is the total {first_measure} by week over the last 8 quarters?"
     
     return None
 
@@ -1677,7 +1844,8 @@ def _generate_smart_questions_with_ai(
     """
     try:
         # Use the selected LLM model
-        from main_research import MultiLLMResearcher, map_llm_display_to_provider
+        from main_research import MultiLLMResearcher
+        from demo_prep import map_llm_display_to_provider
         
         model_to_use = llm_model or 'claude-sonnet-4.5'
         provider_name, model_name = map_llm_display_to_provider(model_to_use)
@@ -1690,18 +1858,57 @@ def _generate_smart_questions_with_ai(
 Company: {company_data.get('name', 'Unknown Company')}
 Use Case: {use_case}
 
-Generate questions that would make compelling visualizations for a demo. Each question should:
-- Be specific and actionable (not generic like "What is total sales?")
-- Include time periods when relevant (last quarter, this year vs last year, etc.)
-- Reference business concepts relevant to {use_case}
-- Ask about trends, patterns, comparisons, or top/bottom N
-- Sound like questions a real business analyst would ask
-
-Examples of GOOD questions:
-- "What are the top 10 products by revenue in the last quarter?"
-- "How do sales compare across regions this year versus last year?"
-- "Which customers have the highest lifetime value but declining engagement?"
-- "What is the monthly revenue trend for the past 12 months?"
+Create questions that will produce high-quality visualizations. Each question MUST:
+- Be specific and actionable
+- Include a visualization type (line chart, bar chart, stacked column chart, pie chart, table, or single number KPI)
+- Use "top N" with specific numbers (top 5, top 10, top 15)
+- Include time periods (last 12 months, this year vs last year, last quarter, past 18 months)
+- Specify sorting (ranked from highest to lowest, ordered by)
+- Use "exactly N" when appropriate to enforce result counts
+
+REQUIRED FORMAT - Each question should be SHORT (under 80 chars preferred) but include viz type at the end:
+
+KPI (Single Number with Sparkline):
+"Total revenue by week - last 8 quarters (KPI)"
+"Profit margin weekly over time (single number)"
+NOTE: KPIs need time dimension for sparklines and trend comparisons!
+
+Time Trend:
+"Monthly sales - last 12 months (line chart)"
+"Revenue trend this year vs last year (line chart)"
+
+Top N Ranking:
+"Top 10 products by revenue (bar chart)"
+"Top 15 customers by orders - last 18 months (bar chart)"
+
+Comparison Chart:
+"Revenue by region and quarter (stacked column chart)"
+"Sales by customer segment and month (stacked column)"
+
+Detailed Table:
+"Revenue, units sold, avg order value by month and category (table)"
+
+Examples of PERFECT questions (SHORT with viz type):
+- "Total profit margin by week - last 8 quarters (KPI)"
+- "Monthly sales trend - 12 months (line chart)"
+- "Top 10 customers by revenue (bar chart)"
+- "Revenue by region and quarter (stacked column)"
+- "Profit margins by category (horizontal bar chart)"
+- "Sales detail by month and category (table)"
+
+REQUIRED QUESTIONS - Include these in THIS ORDER:
+1. "Total revenue by week - last 8 quarters (KPI)"
+2. "Total revenue by month month over month (line chart)"
+3. "Top 10 product_name by total revenue (bar chart)"
+4. "Profit margin weekly week over week (line chart)"
+5. "Profit margin by category_l1 (bar chart)"
+6. "Product performance by category and brand (table)"
+7. "Total revenue by region (geo map)"
+8. "Top products by region last month (stacked bar chart)"
+
+Then generate {num_questions - 8} additional creative questions if needed.
+
+CRITICAL: Keep questions SHORT (50-80 chars) since they become visualization titles!
 
 Return ONLY a JSON object with this exact structure (no other text):
 {{
@@ -1720,6 +1927,17 @@ Return ONLY a JSON object with this exact structure (no other text):
         if not content or content.strip() == '':
             print(f"   ⚠️  AI returned empty content")
             raise ValueError("Empty AI response")
+        
+        # Try to extract JSON if AI added extra text
+        content = content.strip()
+        if not content.startswith('{') and not content.startswith('['):
+            # Look for JSON in the response
+            json_match = re.search(r'(\{[\s\S]*\})', content)
+            if json_match:
+                content = json_match.group(1)
+            else:
+                print(f"   ⚠️  No JSON found in AI response")
+                raise ValueError("No JSON in response")
             
         result = json.loads(content)
         
@@ -1736,11 +1954,16 @@ Return ONLY a JSON object with this exact structure (no other text):
     
     except Exception as e:
         print(f"   ⚠️  AI question generation failed: {e}")
-        # Fallback to simple questions
+        print(f"   🔍 DEBUG: AI response was: {response_text[:500] if 'response_text' in locals() else 'N/A'}")
+        # Fallback to better generic questions (not using use_case literally)
+        company_name = company_data.get('name', 'the company')
         return [
-            f"What is the total revenue for {use_case}?",
-            f"Show me the trend over time for {use_case}",
-            f"What are the top categories in {use_case}?"
+            f"Total revenue (KPI)",
+            f"Monthly revenue trend - last 12 months (line chart)",
+            f"Top 10 products by revenue (bar chart)",
+            f"Revenue by category (bar chart)",
+            f"Sales by region and month (stacked column)",
+            f"Revenue and profit detail by category (table)"
         ][:num_questions]
 
 
@@ -1982,25 +2205,14 @@ def create_liveboard_from_model_mcp(
                     if mcp_count > 0:
                         source_info.append(f"⚡ {mcp_count} MCP-suggested")
                     
-                    note_tile = f"""
-                    <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
-                                padding: 40px; border-radius: 20px; color: white; font-family: system-ui;">
-                        <h1 style="margin: 0 0 20px 0; font-size: 36px;">
-                            {company_data.get('name', 'Company')} {use_case}
-                        </h1>
-                        <p style="margin: 0 0 25px 0; font-size: 18px; opacity: 0.95; line-height: 1.5;">
-                            {company_data.get('description', 'AI-powered analytics dashboard')}
-                        </p>
-                        {outlier_section}
-                        <div style="margin-top: 25px; padding: 20px; background: rgba(255,255,255,0.1); 
-                                    border-radius: 12px;">
-                            <p style="margin: 0; font-size: 13px; opacity: 0.9;">
-                                📊 {len(answers)} visualizations | {' | '.join(source_info) if source_info else 'AI-powered insights'} | 
-                                🚀 Created with ThoughtSpot MCP
-                            </p>
-                        </div>
-                    </div>
-                    """
+                    # Create branded note tile with logo and colors
+                    note_tile = create_branded_note_tile(
+                        company_data=company_data,
+                        use_case=use_case,
+                        answers=answers,
+                        source_info=source_info,
+                        outlier_section=outlier_section
+                    )
                     
                     print(f"🎨 Creating liveboard: {final_liveboard_name}")
                     print(f"📊 Preparing to send {len(answers)} answers to createLiveboard")
@@ -2042,6 +2254,16 @@ def create_liveboard_from_model_mcp(
                         result_text = liveboard_result.content[0].text
                         print(f"🔍 DEBUG: Parsing response text for URL...")
                         
+                        # Check if MCP returned an error
+                        if result_text.startswith('ERROR:'):
+                            error_msg = result_text.replace('ERROR:', '').strip()
+                            print(f"❌ MCP createLiveboard failed: {error_msg}")
+                            return {
+                                'success': False,
+                                'error': f'MCP liveboard creation failed: {error_msg}',
+                                'liveboard_name': final_liveboard_name
+                            }
+                        
                     except Exception as create_error:
                         print(f"❌ createLiveboard failed: {str(create_error)}")
                         print(f"   Error type: {type(create_error).__name__}")
@@ -2067,6 +2289,136 @@ def create_liveboard_from_model_mcp(
                     print(f"🔗 URL: {liveboard_url}")
                     print(f"🆔 GUID: {liveboard_guid}")
                     
+                    # POST-MCP PROCESSING: Fix note tile layout
+                    # MCP creates note tiles with height: 8, but we want height: 2 like golden demo
+                    if liveboard_guid:
+                        print(f"🔧 Post-processing: Fixing note tile layout...")
+                        try:
+                            # Use ts_client session (already authenticated)
+                            ts_base_url = ts_client.base_url
+                            
+                            # Export liveboard TML using authenticated session
+                            export_response = ts_client.session.post(
+                                f"{ts_base_url}/api/rest/2.0/metadata/tml/export",
+                                json={
+                                    'metadata': [{'identifier': liveboard_guid}],
+                                    'export_associated': False
+                                }
+                            )
+                            
+                            if export_response.status_code == 200:
+                                tml_data = export_response.json()
+                                if tml_data and len(tml_data) > 0:
+                                    # Parse YAML TML
+                                    import yaml
+                                    tml_str = tml_data[0].get('edoc', '')
+                                    liveboard_tml = yaml.safe_load(tml_str)
+                                    
+                                    # Fix Viz_1 layout (note tile)
+                                    layout = liveboard_tml.get('liveboard', {}).get('layout', {})
+                                    tiles = layout.get('tiles', [])
+                                    
+                                    # Find and fix Viz_1 note tile dimensions (readable size)
+                                    for tile in tiles:
+                                        if tile.get('visualization_id') == 'Viz_1':
+                                            # Make it readable: height 4, width 6 (half screen)
+                                            tile['height'] = 4
+                                            tile['width'] = 6
+                                            print(f"   ✓ Fixed Viz_1: height={tile['height']}, width={tile['width']}")
+                                            break
+                                    
+                                    # Replace Viz_1 content with company name using golden demo styling
+                                    company_name = company_data.get('name', 'Company')
+                                    
+                                    # Use the actual create_branded_note_tile function
+                                    company_note = create_branded_note_tile(
+                                        company_data=company_data,
+                                        use_case=use_case or '',
+                                        answers=answers,
+                                        source_info=source_info,
+                                        outlier_section=''
+                                    )
+                                    
+                                    # Find and update Viz_1 content in visualizations
+                                    visualizations = liveboard_tml.get('liveboard', {}).get('visualizations', [])
+                                    for viz in visualizations:
+                                        if viz.get('id') == 'Viz_1':
+                                            # Handle different TML structures
+                                            if 'note_tile' in viz:
+                                                # MCP creates note tiles with note_tile structure
+                                                # note_tile is a DICT with html_parsed_string key
+                                                if isinstance(viz['note_tile'], dict) and 'html_parsed_string' in viz['note_tile']:
+                                                    viz['note_tile']['html_parsed_string'] = company_note
+                                                else:
+                                                    viz['note_tile'] = {'html_parsed_string': company_note}
+                                            elif 'answer' in viz:
+                                                viz['answer']['text_data'] = company_note
+                                                viz['answer']['name'] = f'{company_name} Info'
+                                            else:
+                                                # Direct structure without wrappers
+                                                if 'text_data' in viz:
+                                                    viz['text_data'] = company_note
+                                                if 'name' in viz:
+                                                    viz['name'] = f'{company_name} Info'
+                                            print(f"   ✓ Replaced Viz_1 content with {company_name}")
+                                            break
+                                    
+                                    # Add style_properties to make note tile dark themed (like golden demo)
+                                    style = liveboard_tml.get('liveboard', {}).get('style', {})
+                                    if 'overrides' not in style:
+                                        style['overrides'] = []
+                                        liveboard_tml['liveboard']['style'] = style
+                                    
+                                    # Check if Viz_1 already has style override
+                                    viz_1_has_style = False
+                                    for override in style['overrides']:
+                                        if override.get('object_id') == 'Viz_1':
+                                            viz_1_has_style = True
+                                            # Ensure it has tile_brand_color for dark background
+                                            if 'style_properties' not in override:
+                                                override['style_properties'] = []
+                                            has_brand_color = any(prop.get('name') == 'tile_brand_color' for prop in override['style_properties'])
+                                            if not has_brand_color:
+                                                override['style_properties'].append({
+                                                    'name': 'tile_brand_color',
+                                                    'value': 'TBC_I'
+                                                })
+                                                print(f"   ✓ Added dark theme to Viz_1")
+                                            break
+                                    
+                                    if not viz_1_has_style:
+                                        # Add new style override for Viz_1 with dark background
+                                        style['overrides'].append({
+                                            'object_id': 'Viz_1',
+                                            'style_properties': [{
+                                                'name': 'tile_brand_color',
+                                                'value': 'TBC_I'
+                                            }]
+                                        })
+                                        print(f"   ✓ Added dark theme style to Viz_1")
+                                    
+                                    # Re-import fixed TML using authenticated session
+                                    import_response = ts_client.session.post(
+                                        f"{ts_base_url}/api/rest/2.0/metadata/tml/import",
+                                        json={
+                                            'metadata_tmls': [yaml.dump(liveboard_tml, default_flow_style=False, sort_keys=False)],
+                                            'import_policy': 'PARTIAL',
+                                            'create_new': False
+                                        }
+                                    )
+                                    
+                                    if import_response.status_code == 200:
+                                        print(f"   ✅ Layout fixed successfully!")
+                                    else:
+                                        print(f"   ⚠️ Could not re-import TML: {import_response.status_code}")
+                                else:
+                                    print(f"   ⚠️ No TML data in export response")
+                            else:
+                                print(f"   ⚠️ Could not export TML: {export_response.status_code}")
+                        except Exception as fix_error:
+                            print(f"   ⚠️ Layout fix failed: {str(fix_error)}")
+                            # Don't fail the whole operation if post-processing fails
+                    
                     return {
                         'success': True,
                         'liveboard_name': final_liveboard_name,

main_research.py

@@ -39,8 +39,23 @@ class Website:
         Enhanced Website object creation with better content extraction
         """
         self.url = url
+        self.error_message = None
+        self.error_type = None
+        
         try:
-            response = requests.get(url, headers=headers, timeout=10)
+            # Ensure URL has protocol
+            if not url.startswith(('http://', 'https://')):
+                url = 'https://' + url
+                self.url = url
+            
+            # Try with SSL verification first, then without if it fails
+            try:
+                response = requests.get(url, headers=headers, timeout=30, verify=True, allow_redirects=True)
+            except requests.exceptions.SSLError:
+                # Retry without SSL verification for sites with certificate issues
+                print(f"⚠️ SSL verification failed for {url}, retrying without verification...")
+                response = requests.get(url, headers=headers, timeout=30, verify=False, allow_redirects=True)
+            
             response.raise_for_status()
             soup = BeautifulSoup(response.content, 'html.parser')
             
@@ -98,8 +113,47 @@ class Website:
             else:
                 self.text = soup.get_text(separator="\n", strip=True)
                 
+        except requests.exceptions.Timeout:
+            self.error_type = "timeout"
+            self.error_message = f"Request timed out after 30 seconds. The website may be slow or unresponsive."
+            print(f"❌ Timeout accessing {url}: {self.error_message}")
+            self.title = "Error loading website - Timeout"
+            self.text = ""
+            self.css_links = []
+            self.inline_styles = []
+            self.logo_candidates = []
+        except requests.exceptions.ConnectionError as e:
+            self.error_type = "connection"
+            self.error_message = f"Could not connect to {url}. Check if the URL is correct and the site is accessible."
+            print(f"❌ Connection error accessing {url}: {str(e)}")
+            self.title = "Error loading website - Connection Failed"
+            self.text = ""
+            self.css_links = []
+            self.inline_styles = []
+            self.logo_candidates = []
+        except requests.exceptions.HTTPError as e:
+            self.error_type = "http"
+            status_code = e.response.status_code if hasattr(e, 'response') and e.response else 'unknown'
+            
+            if status_code == 404:
+                self.error_message = f"HTTP 404 - Page not found at {url}. The URL may be incorrect, the page may have been moved, or the site may require authentication."
+            elif status_code == 403:
+                self.error_message = f"HTTP 403 - Access forbidden. The site may be blocking automated requests or require authentication."
+            elif status_code == 401:
+                self.error_message = f"HTTP 401 - Authentication required. This site requires login credentials."
+            else:
+                self.error_message = f"HTTP error {status_code} accessing {url}. The server returned an error."
+            
+            print(f"❌ HTTP error accessing {url}: {status_code} - {self.error_message}")
+            self.title = f"Error loading website - HTTP {status_code}"
+            self.text = ""
+            self.css_links = []
+            self.inline_styles = []
+            self.logo_candidates = []
         except Exception as e:
-            print(f"Error processing website {url}: {str(e)}")
+            self.error_type = "unknown"
+            self.error_message = f"Unexpected error accessing {url}: {str(e)}"
+            print(f"❌ Error processing website {url}: {str(e)}")
             self.title = "Error loading website"
             self.text = ""
             self.css_links = []

prompts.py

@@ -256,8 +256,17 @@ PERFORMANCE REQUIREMENTS:
 - Generate data in batches of 100-500 rows per executemany call
 - Use efficient data generation (avoid expensive operations in loops)
 
+REALISTIC DATA REQUIREMENTS:
+- **CRITICAL**: Individual transaction amounts MUST be realistic (e.g., $20-$50,000)
+- For e-commerce/retail: typical orders are $50-$2,000, with rare large orders up to $50,000
+- For B2B: typical orders are $1,000-$50,000, with enterprise orders up to $500,000
+- **NEVER create individual transactions over $1 million** - use many small transactions instead
+- To reach high totals (e.g., $40B revenue), generate MANY transactions (10,000+), not huge individual amounts
+- Example: Product with $40B total revenue = 100,000 transactions × $400,000 avg (NOT 5 transactions × $8B each!)
+
 OUTLIER REQUIREMENTS:
 - Create 5-10 specific dramatic outliers (5-10x normal values)
+- Outliers should be relative to realistic baselines (e.g., $10,000 order vs $500 baseline)
 - Don't rely on random chance for interesting patterns
 - Include clear before/after patterns for demo storytelling
 

requirements.txt

@@ -21,5 +21,8 @@ supabase>=2.0.0  # PostgreSQL-based settings persistence
 faker>=20.1.0
 pandas>=2.0.0
 
+# MCP - Model Context Protocol for ThoughtSpot
+mcp>=1.0.0
+
 # Existing dependencies (if any)
 # Add any other dependencies your project needs

smart_chat.py

@@ -0,0 +1,225 @@
+"""
+Smart Chat Interface for Data Adjustment
+
+Uses liveboard context to intelligently understand requests
+and bundle confirmations into smart prompts.
+"""
+
+from dotenv import load_dotenv
+import os
+from smart_data_adjuster import SmartDataAdjuster
+
+load_dotenv()
+
+
+def chat_loop():
+    """Main smart chat loop"""
+    
+    print("""
+╔════════════════════════════════════════════════════════════╗
+║                                                            ║
+║          Smart Data Adjustment Chat                        ║
+║                                                            ║
+╚════════════════════════════════════════════════════════════╝
+
+This chat understands your liveboard and visualizations!
+
+Commands:
+  - Type your adjustment request naturally
+  - Reference visualizations by number (e.g., "viz 2")
+  - "done" or "exit" to quit
+  - "help" for examples
+
+Examples:
+  - "make 1080p Webcam 40 billion"
+  - "increase tablet revenue to 100B"  
+  - "in viz 2, set laptops to 50B"
+  - "set profit margin to 30% for electronics"
+
+""")
+    
+    # Setup - can be overridden by environment or passed as arguments
+    database = os.getenv('SNOWFLAKE_DATABASE')
+    schema = os.getenv('DEMO_SCHEMA', "20251116_140933_AMAZO_SAL")
+    liveboard_guid = os.getenv('DEMO_LIVEBOARD_GUID', "9a30c9e4-efba-424a-8359-b16eb3a43ec3")
+    
+    print(f"📊 Initializing...")
+    adjuster = SmartDataAdjuster(database, schema, liveboard_guid)
+    adjuster.connect()
+    
+    # Load liveboard context
+    if not adjuster.load_liveboard_context():
+        print("❌ Failed to load liveboard context")
+        return
+    
+    print("\n" + "="*80)
+    print("Ready! I understand your liveboard context.")
+    print("="*80 + "\n")
+    
+    # Show numbered visualizations
+    print("📊 Available Visualizations:")
+    print("-" * 80)
+    for idx, viz in enumerate(adjuster.visualizations, start=1):
+        print(f"  [{idx}] {viz['name']}")
+        cols = ', '.join(viz['columns'][:5])  # Show first 5 columns
+        if len(viz['columns']) > 5:
+            cols += f"... (+{len(viz['columns'])-5} more)"
+        print(f"      Columns: {cols}")
+    print("-" * 80)
+    print("💡 TIP: You can reference visualizations by number (e.g., 'viz 2') or naturally!")
+    print("="*80 + "\n")
+    
+    while True:
+        # Get user input
+        user_input = input("\n💬 You: ").strip()
+        
+        if not user_input:
+            continue
+        
+        # Check for exit
+        if user_input.lower() in ['done', 'exit', 'quit', 'bye']:
+            print("\n👋 Goodbye!")
+            break
+        
+        # Check for help
+        if user_input.lower() == 'help':
+            print("""
+📚 Help - Smart Data Adjustment
+
+I understand your liveboard context, so you can be natural:
+
+Examples:
+  ✅ "make 1080p webcam 40 billion"
+     → I'll find the viz with products and TOTAL_REVENUE
+  
+  ✅ "increase tablet to 100B"
+     → I'll match to the right product visualization
+  
+  ✅ "in viz 2, set laptops to 50B"
+     → Use viz numbers to be specific!
+  
+  ✅ "set profit margin to 30% for electronics"
+     → I'll find the viz with profit margin and categories
+
+I'll show you what I understood and ask for one yes/no confirmation!
+""")
+            continue
+        
+        try:
+            # Check if user specified a viz number
+            viz_number = None
+            import re
+            viz_match = re.search(r'\bviz\s+(\d+)\b', user_input, re.IGNORECASE)
+            if viz_match:
+                viz_number = int(viz_match.group(1))
+                if viz_number < 1 or viz_number > len(adjuster.visualizations):
+                    print(f"❌ Invalid viz number. Please use 1-{len(adjuster.visualizations)}")
+                    continue
+                # Remove viz reference from request
+                user_input = re.sub(r'\bviz\s+\d+\b', '', user_input, flags=re.IGNORECASE).strip()
+                user_input = re.sub(r'^,?\s*', '', user_input)  # Clean up leading comma/space
+            
+            # Match request to visualization
+            print(f"\n🤔 Analyzing request...")
+            match = adjuster.match_request_to_viz(user_input)
+            
+            if not match:
+                print("❌ I couldn't understand that request.")
+                print("💡 Try being more specific or type 'help' for examples")
+                continue
+            
+            # If user specified viz number, override the match
+            if viz_number:
+                match['viz'] = adjuster.visualizations[viz_number - 1]
+                print(f"   → Using specified viz: [{match['viz']['name']}]")
+            else:
+                print(f"   → Matched to: [{match['viz']['name']}]")
+            
+            print(f"   → Entity: {match['entity_value']}")
+            print(f"   → Confidence: {match['confidence'].upper()}")
+            
+            # If low confidence, ask for confirmation
+            if match['confidence'] == 'low':
+                print(f"\n⚠️  I'm not very confident about this match.")
+                confirm_match = input("   Is this correct? [yes/no]: ").strip().lower()
+                if confirm_match not in ['yes', 'y']:
+                    print("💡 Try rephrasing your request")
+                    continue
+            
+            # Get current value
+            print(f"\n📊 Querying current data...")
+            current_value = adjuster.get_current_value(
+                match['entity_value'],
+                match['metric_column']
+            )
+            
+            if current_value == 0:
+                print(f"⚠️  No data found for '{match['entity_value']}'")
+                print("💡 Check the spelling or try a different entity")
+                continue
+            
+            # Calculate target value if percentage
+            target_value = match.get('target_value')
+            if match.get('is_percentage'):
+                percentage = match.get('percentage', 0)
+                target_value = current_value * (1 + percentage / 100)
+                print(f"   💡 {percentage:+.1f}% change = ${current_value:,.0f} → ${target_value:,.0f}")
+            
+            # Generate strategy
+            strategy = adjuster.generate_strategy(
+                match['entity_value'],
+                match['metric_column'],
+                current_value,
+                target_value
+            )
+            
+            # Present smart bundled confirmation
+            # Update match with calculated target if percentage
+            if match.get('is_percentage'):
+                match['target_value'] = target_value
+            confirmation = adjuster.present_smart_confirmation(match, current_value, strategy)
+            print(confirmation)
+            
+            # Get user decision
+            response = input("Run SQL? [yes/no]: ").strip().lower()
+            
+            if response not in ['yes', 'y']:
+                print("\n❌ Cancelled - no changes made")
+                print("💡 You can try a different adjustment or rephrase")
+                continue
+            
+            # Execute
+            print(f"\n⚙️  Executing SQL...")
+            result = adjuster.execute_sql(strategy['sql'])
+            
+            if result['success']:
+                print(f"\n✅ SUCCESS! Updated {result['rows_affected']} rows")
+                print(f"🔄 Refresh your ThoughtSpot liveboard to see changes")
+                print(f"   URL: https://se-thoughtspot-cloud.thoughtspot.cloud/#/pinboard/{liveboard_guid}")
+            else:
+                print(f"\n❌ FAILED: {result['error']}")
+                
+                # Check for common errors
+                if 'out of representable range' in result['error'].lower():
+                    print("\n💡 The number is too large for the database column.")
+                    print("   Try a smaller target value (e.g., 40B instead of 50B)")
+        
+        except KeyboardInterrupt:
+            print("\n\n⚠️  Interrupted")
+            break
+        except Exception as e:
+            print(f"\n❌ Error: {e}")
+            import traceback
+            print(traceback.format_exc())
+    
+    # Cleanup
+    adjuster.close()
+    print("\n✅ Connection closed")
+
+
+if __name__ == "__main__":
+    try:
+        chat_loop()
+    except KeyboardInterrupt:
+        print("\n\n👋 Goodbye!")
+

smart_data_adjuster.py

@@ -0,0 +1,604 @@
+"""
+Smart Conversational Data Adjuster
+
+Understands liveboard context and asks smart qualifying questions.
+Bundles confirmations into one step when confident.
+"""
+
+import os
+from typing import Dict, List, Optional, Tuple
+from openai import OpenAI
+from snowflake_auth import get_snowflake_connection
+from thoughtspot_deployer import ThoughtSpotDeployer
+import json
+
+
+class SmartDataAdjuster:
+    """Smart adjuster with liveboard context and conversational flow"""
+    
+    def __init__(self, database: str, schema: str, liveboard_guid: str):
+        self.database = database
+        self.schema = schema
+        self.liveboard_guid = liveboard_guid
+        self.conn = None
+        self.ts_client = None
+        self.openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+        
+        # Context about the liveboard
+        self.liveboard_name = None
+        self.visualizations = []  # List of viz metadata
+    
+    def connect(self):
+        """Connect to Snowflake and ThoughtSpot"""
+        # Snowflake
+        self.conn = get_snowflake_connection()
+        cursor = self.conn.cursor()
+        cursor.execute(f"USE DATABASE {self.database}")
+        cursor.execute(f'USE SCHEMA "{self.schema}"')
+        
+        # ThoughtSpot
+        self.ts_client = ThoughtSpotDeployer()
+        self.ts_client.authenticate()
+        
+        print(f"✅ Connected to {self.database}.{self.schema}")
+        print(f"✅ Connected to ThoughtSpot")
+    
+    def load_liveboard_context(self):
+        """Load liveboard metadata and visualization details"""
+        print(f"\n📊 Loading liveboard context...")
+        
+        # Get liveboard metadata
+        response = self.ts_client.session.post(
+            f"{self.ts_client.base_url}/api/rest/2.0/metadata/search",
+            json={
+                "metadata": [{"type": "LIVEBOARD", "identifier": self.liveboard_guid}],
+                "include_visualization_headers": True
+            }
+        )
+        
+        if response.status_code != 200:
+            print(f"❌ Failed to load liveboard")
+            return False
+        
+        data = response.json()[0]
+        self.liveboard_name = data.get('metadata_name', 'Unknown Liveboard')
+        
+        viz_headers = data.get('visualization_headers', [])
+        
+        print(f"   Liveboard: {self.liveboard_name}")
+        print(f"   Visualizations: {len(viz_headers)}")
+        
+        # Extract viz details
+        for viz in viz_headers:
+            name = viz.get('name', '')
+            viz_id = viz.get('id')
+            
+            # Skip note tiles
+            if 'note-tile' in name.lower():
+                continue
+            
+            # Parse the name to extract columns used
+            # Names like "top 10 product_name by total revenue"
+            viz_info = {
+                'id': viz_id,
+                'name': name,
+                'columns': self._extract_columns_from_name(name)
+            }
+            
+            self.visualizations.append(viz_info)
+            print(f"   - {name}")
+        
+        return True
+    
+    def _extract_columns_from_name(self, name: str) -> List[str]:
+        """Extract column names from visualization name"""
+        # Simple heuristic: look for column-like words
+        # e.g., "top 10 product_name by total revenue" → [product_name, total_revenue]
+        
+        columns = []
+        name_lower = name.lower()
+        
+        # Common column patterns
+        if 'product_name' in name_lower:
+            columns.append('PRODUCT_NAME')
+        if 'total revenue' in name_lower or 'total_revenue' in name_lower:
+            columns.append('TOTAL_AMOUNT')
+        if 'quantity' in name_lower:
+            columns.append('QUANTITY_SOLD')
+        if 'profit margin' in name_lower or 'profit_margin' in name_lower:
+            columns.append('PROFIT_MARGIN')
+        if 'customer_segment' in name_lower:
+            columns.append('CUSTOMER_SEGMENT')
+        if 'category' in name_lower:
+            columns.append('CATEGORY')
+        if 'seller' in name_lower:
+            columns.append('SELLER_NAME')
+        
+        return columns
+    
+    def _simple_parse(self, message: str) -> Optional[Dict]:
+        """Simple regex-based parser for common patterns like 'decrease phone case by 10%' or 'decrease seller acme by 10%'"""
+        import re
+        
+        print(f"🔍 DEBUG _simple_parse: message='{message}'")
+        msg_lower = message.lower()
+        
+        # Detect if user specified a viz number
+        viz_match = re.search(r'(?:viz|visualization)\s+(\d+)', msg_lower)
+        viz_number = int(viz_match.group(1)) if viz_match else None
+        
+        # Detect entity type (product or seller)
+        # Check explicit "seller" keyword, or infer from viz number
+        is_seller = 'seller' in msg_lower
+        
+        # If viz number is specified, check if it's a seller viz
+        if viz_number and not is_seller and len(self.visualizations) >= viz_number:
+            viz = self.visualizations[viz_number - 1]
+            if 'seller' in viz['name'].lower():
+                is_seller = True
+        
+        entity_type = 'seller' if is_seller else 'product'
+        
+        # Extract entity name - try quotes first, then words after action verbs
+        entity_match = re.search(r'"([^"]+)"', message)
+        if not entity_match:
+            # Try to find entity name after action words, but stop before numbers
+            # Include "seller" keyword if present
+            if is_seller:
+                # Match: "decrease seller home depot by 20%"
+                action_pattern = r'(?:decrease|increase|make|set|adjust)\s+(?:the\s+)?(?:profit\s+margin\s+for\s+)?seller\s+([a-z\s]+?)(?:\s+\d|\s+by|\s+to|\s*$)'
+            else:
+                # Match: "decrease bluetooth speaker by 10%" OR "decrease the revenue for bluetooth speaker by 10%"
+                action_pattern = r'(?:decrease|increase|make|set|adjust)\s+(?:the\s+)?(?:revenue\s+for\s+|profit\s+margin\s+for\s+)?([a-z\s]+?)(?:\s+\d|\s+by|\s+to|\s*$)'
+            entity_match = re.search(action_pattern, msg_lower, re.I)
+        
+        if not entity_match:
+            return None
+        
+        entity = entity_match.group(1).strip()
+        
+        # Find percentage or absolute value
+        is_percentage = False
+        percentage = None
+        target_value = None
+        
+        # Look for percentage like "by 10%" or "10%"
+        pct_match = re.search(r'by\s+(\d+\.?\d*)%|(\d+\.?\d*)%', msg_lower)
+        if pct_match:
+            is_percentage = True
+            percentage = float(pct_match.group(1) or pct_match.group(2))
+            # Check if it's decrease or increase
+            if 'decrease' in msg_lower or 'reduce' in msg_lower or 'lower' in msg_lower:
+                percentage = -percentage
+        else:
+            # Look for absolute value like "50B", "50 billion", "1000000"
+            val_match = re.search(r'(\d+\.?\d*)\s*([bBmMkK]|billion|million|thousand)?', message)
+            if val_match:
+                num = float(val_match.group(1))
+                unit = (val_match.group(2) or '').lower()
+                if unit in ['b', 'billion']:
+                    target_value = num * 1_000_000_000
+                elif unit in ['m', 'million']:
+                    target_value = num * 1_000_000
+                elif unit in ['k', 'thousand']:
+                    target_value = num * 1_000
+                else:
+                    target_value = num
+        
+        if not is_percentage and not target_value:
+            return None
+        
+        # Find appropriate viz and determine metric column
+        # If user specified viz number, use it; otherwise search for matching viz
+        if viz_number:
+            viz_num = viz_number
+        elif is_seller:
+            # Look for seller-related viz
+            viz_num = 1  # Default
+            for i, viz in enumerate(self.visualizations, 1):
+                if 'seller' in viz['name'].lower():
+                    viz_num = i
+                    break
+        else:
+            # Look for product-related viz
+            viz_num = 1  # Default
+            for i, viz in enumerate(self.visualizations, 1):
+                if 'product' in viz['name'].lower():
+                    viz_num = i
+                    break
+        
+        # Determine metric based on entity type
+        if is_seller:
+            metric_column = 'PROFIT_MARGIN'  # Sellers typically use profit margin
+        else:
+            metric_column = 'TOTAL_AMOUNT'  # Products typically use revenue (column is TOTAL_AMOUNT)
+        
+        result = {
+            'viz_number': viz_num,
+            'entity_value': entity,
+            'entity_type': entity_type,
+            'metric_column': metric_column,
+            'target_value': target_value,
+            'is_percentage': is_percentage,
+            'percentage': percentage,
+            'confidence': 'medium',
+            'reasoning': f'Simple {entity_type} parse'
+        }
+        
+        print(f"🔍 DEBUG _simple_parse result: entity='{entity}', percentage={percentage}, metric={metric_column}, viz_num={viz_num}")
+        
+        if viz_num <= len(self.visualizations):
+            result['viz'] = self.visualizations[viz_num - 1]
+        
+        return result
+    
+    def match_request_to_viz(self, user_request: str) -> Optional[Dict]:
+        """
+        Use AI to match user request to specific visualization
+        
+        Returns:
+            {
+                'viz': {...},
+                'confidence': 'high'|'medium'|'low',
+                'entity_value': '1080p Webcam',
+                'metric_column': 'TOTAL_AMOUNT',
+                'target_value': 50000000000
+            }
+        """
+        # Try simple parse first (faster, no AI needed)
+        simple_result = self._simple_parse(user_request)
+        if simple_result:
+            print(f"   ⚡ Quick parse: '{simple_result['entity_value']}' → {simple_result.get('percentage', simple_result.get('target_value'))}")
+            return simple_result
+        
+        viz_list = "\n".join([
+            f"{i+1}. {v['name']} (columns: {', '.join(v['columns'])})"
+            for i, v in enumerate(self.visualizations)
+        ])
+        
+        prompt = f"""User is looking at a ThoughtSpot liveboard and wants to adjust data.
+
+User request: "{user_request}"
+
+Available visualizations on the liveboard:
+{viz_list}
+
+Analyze the request and determine:
+1. Which visualization (by number) is the user referring to?
+2. What entity/product are they talking about? (e.g., "1080p Webcam")
+3. What metric should be adjusted? (TOTAL_AMOUNT, QUANTITY_SOLD, PROFIT_MARGIN)
+4. What's the target value? 
+   - If absolute value (e.g., "40B", "100M"): convert to number (40B = 40000000000)
+   - If percentage increase (e.g., "increase by 20%"): set is_percentage=true and percentage=20
+5. How confident are you? (high/medium/low)
+
+Return JSON:
+{{
+  "viz_number": 1,
+  "entity_value": "1080p Webcam",
+  "metric_column": "TOTAL_AMOUNT",
+  "target_value": 50000000000,
+  "is_percentage": false,
+  "percentage": null,
+  "confidence": "high",
+  "reasoning": "User mentioned product and the top 10 products viz uses PRODUCT_NAME and TOTAL_AMOUNT"
+}}
+
+OR for percentage increase:
+{{
+  "viz_number": 1,
+  "entity_value": "1080p Webcam",
+  "metric_column": "TOTAL_AMOUNT",
+  "target_value": null,
+  "is_percentage": true,
+  "percentage": 20,
+  "confidence": "high",
+  "reasoning": "User wants to increase revenue by 20%"
+}}
+
+CRITICAL: target_value and percentage must be numbers, never strings.
+If unsure about ANY field, set confidence to "low" or "medium".
+"""
+        
+        response = self.openai_client.chat.completions.create(
+            model="gpt-4o",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0
+        )
+        
+        content = response.choices[0].message.content
+        if content.startswith('```'):
+            lines = content.split('\n')
+            content = '\n'.join(lines[1:-1])
+        
+        try:
+            result = json.loads(content)
+            
+            # Add the actual viz object
+            viz_num = result.get('viz_number', 1)
+            if 1 <= viz_num <= len(self.visualizations):
+                result['viz'] = self.visualizations[viz_num - 1]
+            
+            return result
+        except:
+            return None
+    
+    def _find_closest_entity(self, entity_value: str, entity_type: str = 'product') -> Optional[str]:
+        """Find the closest matching entity name (product or seller) in the database"""
+        cursor = self.conn.cursor()
+        
+        # Get all entity names based on type
+        if entity_type == 'seller':
+            cursor.execute(f"""
+                SELECT DISTINCT SELLER_NAME 
+                FROM {self.database}."{self.schema}".SELLERS
+            """)
+        else:  # product
+            cursor.execute(f"""
+                SELECT DISTINCT PRODUCT_NAME 
+                FROM {self.database}."{self.schema}".PRODUCTS
+            """)
+        
+        entities = [row[0] for row in cursor.fetchall()]
+        
+        # Normalize: lowercase and remove spaces for comparison
+        def normalize(s):
+            return s.lower().replace(' ', '').replace('-', '').replace('_', '')
+        
+        entity_normalized = normalize(entity_value)
+        
+        # First try exact case-insensitive match
+        entity_lower = entity_value.lower()
+        for entity in entities:
+            if entity.lower() == entity_lower:
+                return entity
+        
+        # Try normalized match (ignoring spaces/dashes)
+        for entity in entities:
+            if normalize(entity) == entity_normalized:
+                return entity
+        
+        # Try partial match (contains)
+        for entity in entities:
+            if entity_lower in entity.lower() or entity.lower() in entity_lower:
+                return entity
+        
+        # Try normalized partial match
+        for entity in entities:
+            if entity_normalized in normalize(entity) or normalize(entity) in entity_normalized:
+                return entity
+        
+        return None
+    
+    def _find_closest_product(self, entity_value: str) -> Optional[str]:
+        """Backward compatibility wrapper"""
+        return self._find_closest_entity(entity_value, 'product')
+    
+    def get_current_value(self, entity_value: str, metric_column: str, entity_type: str = 'product') -> float:
+        """Query current value from Snowflake"""
+        cursor = self.conn.cursor()
+        
+        # Find closest matching entity
+        matched_entity = self._find_closest_entity(entity_value, entity_type)
+        
+        if not matched_entity:
+            print(f"⚠️  Could not find {entity_type} matching '{entity_value}'")
+            return 0
+        
+        if matched_entity.lower() != entity_value.lower():
+            print(f"   📝 Using closest match: '{matched_entity}'")
+        
+        # Build query based on entity type
+        if entity_type == 'seller':
+            query = f"""
+            SELECT AVG(st.{metric_column})
+            FROM {self.database}."{self.schema}".SALES_TRANSACTIONS st
+            JOIN {self.database}."{self.schema}".SELLERS s ON st.SELLER_ID = s.SELLER_ID
+            WHERE LOWER(s.SELLER_NAME) = LOWER('{matched_entity}')
+            """
+        else:  # product
+            query = f"""
+            SELECT SUM(st.{metric_column})
+            FROM {self.database}."{self.schema}".SALES_TRANSACTIONS st
+            JOIN {self.database}."{self.schema}".PRODUCTS p ON st.PRODUCT_ID = p.PRODUCT_ID
+            WHERE LOWER(p.PRODUCT_NAME) = LOWER('{matched_entity}')
+            """
+        
+        cursor.execute(query)
+        result = cursor.fetchone()
+        return float(result[0]) if result and result[0] else 0
+    
+    def generate_strategy(self, entity_value: str, metric_column: str, current_value: float, target_value: float = None, percentage: float = None, entity_type: str = 'product') -> Dict:
+        """Generate the best strategy (default to Strategy A for now)"""
+        
+        print(f"🔍 DEBUG generate_strategy: entity='{entity_value}', metric={metric_column}, percentage={percentage}, current={current_value}")
+        
+        # Find the actual entity name
+        matched_entity = self._find_closest_entity(entity_value, entity_type)
+        if not matched_entity:
+            matched_entity = entity_value  # Fallback
+        
+        print(f"🔍 DEBUG matched_entity: '{matched_entity}'")
+        
+        # Calculate multiplier
+        if percentage is not None:
+            # Percentage-based: "decrease by 10%" means multiply by 0.9
+            multiplier = 1 + (percentage / 100)
+            percentage_change = percentage
+            target_value = current_value * multiplier
+        else:
+            # Absolute target value
+            multiplier = target_value / current_value if current_value > 0 else 1
+            percentage_change = (multiplier - 1) * 100
+        
+        # Build SQL based on entity type
+        if entity_type == 'seller':
+            sql = f"""UPDATE {self.database}."{self.schema}".SALES_TRANSACTIONS
+SET {metric_column} = {metric_column} * {multiplier:.6f}
+WHERE SELLER_ID IN (
+    SELECT SELLER_ID FROM {self.database}."{self.schema}".SELLERS
+    WHERE LOWER(SELLER_NAME) = LOWER('{matched_entity}')
+)"""
+        else:  # product
+            sql = f"""UPDATE {self.database}."{self.schema}".SALES_TRANSACTIONS
+SET {metric_column} = {metric_column} * {multiplier:.6f}
+WHERE PRODUCT_ID IN (
+    SELECT PRODUCT_ID FROM {self.database}."{self.schema}".PRODUCTS
+    WHERE LOWER(PRODUCT_NAME) = LOWER('{matched_entity}')
+)"""
+        
+        print(f"🔍 DEBUG SQL generated:\n{sql}")
+        
+        return {
+            'id': 'A',
+            'name': 'Distribute Across All Transactions',
+            'description': f"Multiply all transactions by {multiplier:.2f}x ({percentage_change:+.1f}%)",
+            'sql': sql,
+            'matched_product': matched_entity,  # Keep key name for compatibility
+            'target_value': target_value
+        }
+    
+    def present_smart_confirmation(self, match: Dict, current_value: float, strategy: Dict) -> str:
+        """Create a bundled confirmation prompt"""
+        
+        viz_name = match['viz']['name']
+        entity = match['entity_value']
+        matched_product = strategy.get('matched_product', entity)
+        metric = match['metric_column']
+        target = strategy.get('target_value', match.get('target_value'))  # Use calculated target from strategy
+        confidence = match['confidence']
+        
+        # Show if we fuzzy matched
+        entity_display = entity
+        if matched_product.lower() != entity.lower():
+            entity_display = f"{entity} → '{matched_product}'"
+        
+        confirmation = f"""
+{'='*80}
+📋 SMART CONFIRMATION
+{'='*80}
+
+Liveboard: {self.liveboard_name}
+Visualization: [{viz_name}]
+
+Adjustment:
+  Entity: {entity_display}
+  Metric: {metric}
+  Current Value: ${current_value:,.0f}
+  Target Value: ${target:,.0f}
+  Change: ${target - current_value:+,.0f} ({(target/current_value - 1)*100:+.1f}%)
+
+Strategy: {strategy['name']}
+  {strategy['description']}
+
+Confidence: {confidence.upper()}
+{match.get('reasoning', '')}
+
+SQL Preview:
+{strategy['sql'][:200]}...
+
+"""
+        
+        if confidence == 'low':
+            confirmation += "\n⚠️  Low confidence - please verify this is correct\n"
+        
+        confirmation += "\n" + "="*80 + "\n"
+        return confirmation
+    
+    def execute_sql(self, sql: str) -> Dict:
+        """Execute the SQL update"""
+        print(f"🔍 DEBUG execute_sql: About to execute SQL")
+        print(f"SQL:\n{sql}")
+        cursor = self.conn.cursor()
+        
+        try:
+            cursor.execute(sql)
+            rows_affected = cursor.rowcount
+            self.conn.commit()
+            print(f"✅ SQL executed successfully, rows affected: {rows_affected}")
+            
+            return {
+                'success': True,
+                'rows_affected': rows_affected
+            }
+        except Exception as e:
+            self.conn.rollback()
+            return {
+                'success': False,
+                'error': str(e)
+            }
+    
+    def close(self):
+        """Close connections"""
+        if self.conn:
+            self.conn.close()
+
+
+def test_smart_adjuster():
+    """Test the smart adjuster"""
+    from dotenv import load_dotenv
+    load_dotenv()
+    
+    print("""
+╔════════════════════════════════════════════════════════════╗
+║                                                            ║
+║          Smart Data Adjuster Test                         ║
+║                                                            ║
+╚════════════════════════════════════════════════════════════╝
+    """)
+    
+    adjuster = SmartDataAdjuster(
+        database=os.getenv('SNOWFLAKE_DATABASE'),
+        schema="20251116_140933_AMAZO_SAL",
+        liveboard_guid="9a30c9e4-efba-424a-8359-b16eb3a43ec3"
+    )
+    
+    adjuster.connect()
+    adjuster.load_liveboard_context()
+    
+    # Test request
+    user_request = "make 1080p Webcam 50 billion"
+    print(f"\n💬 User: \"{user_request}\"")
+    
+    # Match to viz
+    print(f"\n🤔 Analyzing request...")
+    match = adjuster.match_request_to_viz(user_request)
+    
+    if not match:
+        print("❌ Could not understand request")
+        return
+    
+    # Get current value
+    current = adjuster.get_current_value(match['entity_value'], match['metric_column'])
+    
+    # Generate strategy (handle both absolute and percentage)
+    strategy = adjuster.generate_strategy(
+        match['entity_value'],
+        match['metric_column'],
+        current,
+        target_value=match.get('target_value'),
+        percentage=match.get('percentage')
+    )
+    
+    # Present confirmation
+    confirmation = adjuster.present_smart_confirmation(match, current, strategy)
+    print(confirmation)
+    
+    # Ask for confirmation
+    response = input("Run SQL? [yes/no]: ").strip().lower()
+    
+    if response in ['yes', 'y']:
+        result = adjuster.execute_sql(strategy['sql'])
+        if result['success']:
+            print(f"\n✅ Success! Updated {result['rows_affected']} rows")
+        else:
+            print(f"\n❌ Failed: {result['error']}")
+    else:
+        print("\n❌ Cancelled")
+    
+    adjuster.close()
+
+
+if __name__ == "__main__":
+    test_smart_adjuster()
+

supabase_client.py

@@ -355,7 +355,7 @@ def load_gradio_settings(email: str) -> Dict[str, Any]:
         "default_data_volume": "Medium (10K rows)",
         "default_warehouse": "COMPUTE_WH",
         "default_database": "DEMO_DB",
-        
+
         # Data Generation Settings
         "fact_table_size": "10000",
         "dim_table_size": "100",
@@ -370,6 +370,10 @@ def load_gradio_settings(email: str) -> Dict[str, Any]:
         "snowflake_user": "",
         "snowflake_role": "ACCOUNTADMIN",
         "default_schema": "PUBLIC",
+        
+        # Demo Configuration
+        "tag_name": "",
+        "object_naming_prefix": "",
 
         # Advanced Options
         "batch_size": 5000,

test_mcp_liveboard_isolated.py

@@ -1,205 +0,0 @@
-"""
-Isolated MCP Liveboard Creation Test
-
-Test MCP liveboard creation with existing ThoughtSpot objects.
-Run this repeatedly to debug MCP issues without full deployment.
-
-Usage:
-    python test_mcp_liveboard_isolated.py
-
-Requirements:
-    - Existing ThoughtSpot connection, schema, and model
-    - Update the CONFIG section below with your details
-"""
-
-import os
-import asyncio
-import json
-from dotenv import load_dotenv
-from datetime import datetime
-
-load_dotenv()
-
-# ============================================================================
-# CONFIG - UPDATE THESE WITH YOUR THOUGHTSPOT OBJECTS
-# ============================================================================
-CONFIG = {
-    # ThoughtSpot credentials
-    'ts_url': os.getenv('THOUGHTSPOT_URL', 'https://your-instance.thoughtspot.cloud'),
-    'ts_username': os.getenv('THOUGHTSPOT_USERNAME', 'your-username'),
-    'ts_password': os.getenv('THOUGHTSPOT_PASSWORD', ''),
-    'ts_secret': os.getenv('THOUGHTSPOT_SECRET_KEY', ''),
-    
-    # Existing ThoughtSpot objects (get these from a successful deployment)
-    'model_id': 'eb600ad2-ad91-4640-819a-f953602bd4c1',  # Working model from user's test
-    'model_name': 'Working_Model',  # Working model from user's test
-    
-    # Company/use case for liveboard
-    'company_name': 'Amazon.com',
-    'use_case': 'Sales Analytics',
-    
-    # Liveboard settings
-    'liveboard_name': 'Test MCP Liveboard',
-    'num_visualizations': 3,  # Start small for testing
-    
-    # Timeout (seconds)
-    'timeout': 120  # 2 minutes max
-}
-
-# ============================================================================
-# TEST RUNNER
-# ============================================================================
-
-def print_section(title):
-    """Print a formatted section header"""
-    print(f"\n{'='*60}")
-    print(f"  {title}")
-    print(f"{'='*60}\n")
-
-
-async def test_mcp_liveboard_creation():
-    """Test MCP liveboard creation with timeout"""
-    
-    print_section("🧪 MCP Liveboard Creation Test")
-    print(f"⏰ Start Time: {datetime.now().strftime('%H:%M:%S')}")
-    print(f"⏱️  Timeout: {CONFIG['timeout']} seconds")
-    
-    # Validate config
-    if not CONFIG['model_id'] or not CONFIG['model_name']:
-        print("❌ ERROR: Please update CONFIG with your model_id and model_name!")
-        print("\nTo find your model ID:")
-        print("1. Go to ThoughtSpot and navigate to your model")
-        print("2. The URL will contain the GUID: /data/models/[GUID]")
-        print("3. Copy that GUID and paste it in CONFIG['model_id']")
-        return False
-    
-    print(f"📊 Model ID: {CONFIG['model_id']}")
-    print(f"📋 Model Name: {CONFIG['model_name']}")
-    print(f"🏢 Company: {CONFIG['company_name']}")
-    print(f"📈 Use Case: {CONFIG['use_case']}")
-    print(f"🎨 Liveboard Name: {CONFIG['liveboard_name']}")
-    print(f"📊 Visualizations: {CONFIG['num_visualizations']}")
-    
-    print_section("1️⃣ Calling MCP Liveboard Creation (with timeout)")
-    print("   ℹ️  Note: MCP handles OAuth authentication automatically via mcp-remote")
-    print("   ℹ️  Browser may open for first-time authentication")
-    
-    try:
-        from liveboard_creator import create_liveboard_from_model_mcp
-        
-        # Build company data
-        company_data = {
-            'name': CONFIG['company_name'],
-            'use_case': CONFIG['use_case']
-        }
-        
-        print(f"🚀 Starting MCP liveboard creation...")
-        print(f"   Watch for progress below...")
-        print(f"   Will timeout after {CONFIG['timeout']}s if no response\n")
-        
-        # Call with timeout (no to_thread needed - function handles its own asyncio.run)
-        start_time = datetime.now()
-        
-        # Run in executor to avoid blocking
-        loop = asyncio.get_event_loop()
-        result = await asyncio.wait_for(
-            loop.run_in_executor(
-                None,
-                lambda: create_liveboard_from_model_mcp(
-                    ts_client=None,  # MCP doesn't need REST client - uses OAuth via mcp-remote
-                    model_id=CONFIG['model_id'],
-                    model_name=CONFIG['model_name'],
-                    company_data=company_data,
-                    use_case=CONFIG['use_case'],
-                    num_visualizations=CONFIG['num_visualizations'],
-                    liveboard_name=CONFIG['liveboard_name']
-                )
-            ),
-            timeout=CONFIG['timeout']
-        )
-        
-        elapsed = (datetime.now() - start_time).total_seconds()
-        
-        if result.get('success'):
-            print_section("✅ SUCCESS!")
-            print(f"⏱️  Time: {elapsed:.1f}s")
-            print(f"📊 Liveboard: {result.get('liveboard_name')}")
-            print(f"🆔 GUID: {result.get('liveboard_guid')}")
-            print(f"🔗 URL: {result.get('liveboard_url', 'N/A')}")
-            print(f"📈 Visualizations: {result.get('visualizations_created', 'N/A')}")
-            return True
-        else:
-            print_section("❌ FAILED")
-            print(f"⏱️  Time: {elapsed:.1f}s")
-            print(f"❌ Error: {result.get('error', 'Unknown error')}")
-            return False
-            
-    except asyncio.TimeoutError:
-        elapsed = (datetime.now() - start_time).total_seconds()
-        print_section("⏰ TIMEOUT")
-        print(f"❌ MCP liveboard creation timed out after {elapsed:.1f}s")
-        print(f"\nPossible causes:")
-        print("1. MCP server not responding")
-        print("2. Network connectivity issues")
-        print("3. ThoughtSpot instance not accessible")
-        print("4. npx not installed or not in PATH")
-        print("\nTry:")
-        print("- Check if 'npx' command works: npx --version")
-        print("- Check network connection to ThoughtSpot")
-        print("- Try with fewer visualizations (CONFIG['num_visualizations'] = 1)")
-        return False
-        
-    except Exception as e:
-        elapsed = (datetime.now() - start_time).total_seconds()
-        print_section("❌ ERROR")
-        print(f"⏱️  Time: {elapsed:.1f}s")
-        print(f"❌ Error: {e}")
-        import traceback
-        print(f"\nTraceback:")
-        print(traceback.format_exc())
-        return False
-
-
-def run_test():
-    """Run the async test"""
-    try:
-        result = asyncio.run(test_mcp_liveboard_creation())
-        
-        print_section("🏁 Test Complete")
-        print(f"⏰ End Time: {datetime.now().strftime('%H:%M:%S')}")
-        
-        if result:
-            print("✅ Test PASSED")
-            exit(0)
-        else:
-            print("❌ Test FAILED")
-            exit(1)
-            
-    except KeyboardInterrupt:
-        print("\n\n⚠️  Test interrupted by user (Ctrl+C)")
-        exit(130)
-
-
-if __name__ == "__main__":
-    print("""
-╔════════════════════════════════════════════════════════════╗
-║                                                            ║
-║          MCP Liveboard Creation Test Suite                ║
-║                                                            ║
-╚════════════════════════════════════════════════════════════╝
-    """)
-    
-    # Check for required environment variables
-    if not os.getenv('THOUGHTSPOT_URL'):
-        print("⚠️  WARNING: THOUGHTSPOT_URL not set in environment")
-        print("   Using value from CONFIG\n")
-    
-    print("📝 INSTRUCTIONS:")
-    print("   1. Update CONFIG section in this file with your model ID")
-    print("   2. Run: python test_mcp_liveboard_isolated.py")
-    print("   3. Press Ctrl+C to cancel if it hangs\n")
-    
-    print("🚀 Starting test...\n")
-    
-    run_test()
-

thoughtspot_deployer.py

@@ -166,7 +166,29 @@ class ThoughtSpotDeployer:
             
             for line in column_lines:
                 line = line.strip()
-                if line and not line.upper().startswith(('PRIMARY KEY', 'FOREIGN KEY', 'CONSTRAINT')):
+                line_upper = line.upper()
+                
+                # Parse FOREIGN KEY constraints
+                if line_upper.startswith('FOREIGN KEY'):
+                    # FOREIGN KEY (CUSTOMER_ID) REFERENCES CUSTOMERS(CUSTOMER_ID)
+                    fk_match = re.match(
+                        r'FOREIGN\s+KEY\s*\((\w+)\)\s*REFERENCES\s+(\w+)\s*\((\w+)\)',
+                        line,
+                        re.IGNORECASE
+                    )
+                    if fk_match:
+                        from_col = fk_match.group(1).upper()
+                        to_table = fk_match.group(2).upper()
+                        to_col = fk_match.group(3).upper()
+                        foreign_keys.append({
+                            'from_table': table_name,
+                            'from_column': from_col,
+                            'to_table': to_table,
+                            'to_column': to_col
+                        })
+                        print(f"   🔗 Found FK: {table_name}.{from_col} -> {to_table}.{to_col}")
+                
+                elif not line_upper.startswith(('PRIMARY KEY', 'CONSTRAINT')):
                     # Parse: COLUMNNAME DATATYPE(params) [IDENTITY] [NOT NULL]
                     parts = line.split()
                     if len(parts) >= 2:
@@ -175,8 +197,6 @@ class ThoughtSpotDeployer:
                         col_type_match = re.match(r'(\w+(?:\([^)]+\))?)', parts[1])
                         col_type = col_type_match.group(1).upper() if col_type_match else parts[1].upper()
                         
-                        # DEBUG: Removed - data type parsing is working correctly
-                        
                         columns.append({
                             'name': col_name,
                             'type': col_type,
@@ -185,7 +205,7 @@ class ThoughtSpotDeployer:
             
             tables[table_name] = columns
             
-        print(f"📊 Found {len(tables)} tables in DDL")
+        print(f"📊 Found {len(tables)} tables and {len(foreign_keys)} foreign keys in DDL")
         return tables, foreign_keys
 
 
@@ -237,11 +257,12 @@ class ThoughtSpotDeployer:
                     print(f"   📋 Response: {response.text}")
 
     def create_table_tml(self, table_name: str, columns: List, connection_name: str,
-                        database: str, schema: str, all_tables: Dict = None, table_guid: str = None) -> str:
+                        database: str, schema: str, all_tables: Dict = None, table_guid: str = None, foreign_keys: List = None) -> str:
         """Generate table TML matching working example structure
 
         Args:
             table_guid: If provided, use this GUID (for updating existing tables with joins)
+            foreign_keys: List of foreign key relationships parsed from DDL
         """
         tml_columns = []
         
@@ -304,7 +325,7 @@ class ThoughtSpotDeployer:
         
         # Add joins_with relationships (matching working example)
         if all_tables:
-            joins_with = self._generate_table_joins(table_name, columns, all_tables)
+            joins_with = self._generate_table_joins(table_name, columns, all_tables, foreign_keys)
             if joins_with:
                 table_tml['table']['joins_with'] = joins_with
         
@@ -313,43 +334,38 @@ class ThoughtSpotDeployer:
         # Keep quotes around 'on' key as shown in working example
         return yaml_output
     
-    def _generate_table_joins(self, table_name: str, columns: List, all_tables: Dict) -> List:
-        """Generate joins_with structure matching working example"""
+    def _generate_table_joins(self, table_name: str, columns: List, all_tables: Dict, foreign_keys: List = None) -> List:
+        """Generate joins_with structure based on parsed foreign keys from DDL"""
         joins = []
         table_name_upper = table_name.upper()
-        table_cols = [col['name'].upper() for col in columns]
-        
-        # Find foreign key relationships
-        for col_name in table_cols:
-            if col_name.endswith('ID') and col_name != f"{table_name_upper}ID":
-                # This looks like a foreign key - find the target table
-                # Handle both CUSTOMER_ID and CUSTOMERID formats
-                if col_name.endswith('_ID'):
-                    # CUSTOMER_ID -> CUSTOMERS
-                    potential_target = col_name[:-3] + 'S'
-                else:
-                    # CUSTOMERID -> CUSTOMERS
-                    potential_target = col_name[:-2] + 'S'
+        
+        if not foreign_keys:
+            print(f"   ⚠️  No foreign keys provided for {table_name_upper}")
+            return joins
+        
+        # Use actual foreign keys from DDL
+        for fk in foreign_keys:
+            if fk['from_table'] == table_name_upper:
+                to_table = fk['to_table']
+                from_col = fk['from_column']
+                to_col = fk['to_column']
 
-                # Check if target table exists in THIS deployment AND it's not the same table
-                # IMPORTANT: Only create joins to tables in the same schema/connection
+                # Check if target table exists in THIS deployment
                 available_tables_upper = [t.upper() for t in all_tables.keys()]
-                if (potential_target in available_tables_upper and
-                    potential_target != table_name_upper):
+                if to_table in available_tables_upper:
                     constraint_id = f"SYS_CONSTRAINT_{self._generate_constraint_id()}"
                     join_def = {
                         'name': constraint_id,
                         'destination': {
-                            'name': potential_target
+                            'name': to_table
                         },
-                        'on': f"[{table_name_upper}::{col_name}] = [{potential_target}::{col_name}]",
+                        'on': f"[{table_name_upper}::{from_col}] = [{to_table}::{to_col}]",
                         'type': 'INNER'
                     }
                     joins.append(join_def)
-                    print(f"   🔗 Generated join: {table_name_upper} -> {potential_target} on {col_name}")
+                    print(f"   🔗 Generated join: {table_name_upper}.{from_col} -> {to_table}.{to_col}")
                 else:
-                    if potential_target not in available_tables_upper and potential_target != table_name_upper:
-                        print(f"   ⏭️  Skipping join: {table_name_upper}.{col_name} -> {potential_target} (table not in this deployment)")
+                    print(f"   ⏭️  Skipping join: {table_name_upper}.{from_col} -> {to_table} (table not in this deployment)")
         
         return joins
 
@@ -1117,6 +1133,49 @@ class ThoughtSpotDeployer:
             print(f"   ⚠️  Could not create schema: {e}")
             print(f"   📝 Will proceed assuming schema exists or will be created by table operations")
 
+    def assign_tags_to_objects(self, object_guids: List[str], object_type: str, tag_name: str) -> bool:
+        """
+        Assign tags to ThoughtSpot objects using REST API v1.
+        
+        Args:
+            object_guids: List of object GUIDs to tag
+            object_type: Type of objects (LOGICAL_TABLE for tables/models, PINBOARD_ANSWER_BOOK for liveboards)
+            tag_name: Tag name to assign
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        if not tag_name or not object_guids:
+            return False
+        
+        try:
+            import json as json_module
+            
+            # Use V1 API which actually works
+            assign_response = self.session.post(
+                f"{self.base_url}/tspublic/v1/metadata/assigntag",
+                data={
+                    'id': json_module.dumps(object_guids),
+                    'type': object_type,
+                    'tagname': json_module.dumps([tag_name])
+                },
+                headers={
+                    'X-Requested-By': 'ThoughtSpot',
+                    'Content-Type': 'application/x-www-form-urlencoded'
+                }
+            )
+            
+            if assign_response.status_code in [200, 204]:
+                print(f"[ThoughtSpot]    ✅ Tagged {len(object_guids)} {object_type} objects with '{tag_name}'", flush=True)
+                return True
+            else:
+                print(f"[ThoughtSpot]    ⚠️  Tag assignment failed: {assign_response.status_code}", flush=True)
+                return False
+                
+        except Exception as e:
+            print(f"[ThoughtSpot]    ⚠️  Tag assignment error: {str(e)}", flush=True)
+            return False
+
     def _generate_demo_names(self, company_name: str = None, use_case: str = None):
         """Generate standardized demo names using DM convention"""
         from datetime import datetime
@@ -1152,7 +1211,8 @@ class ThoughtSpotDeployer:
     def deploy_all(self, ddl: str, database: str, schema: str, 
                   connection_name: str = None, company_name: str = None, 
                   use_case: str = None, liveboard_name: str = None, 
-                  llm_model: str = None, progress_callback=None) -> Dict:
+                  llm_model: str = None, tag_name: str = None, 
+                  progress_callback=None) -> Dict:
         """
         Deploy complete data model to ThoughtSpot
         
@@ -1324,7 +1384,7 @@ class ThoughtSpotDeployer:
             
             for table_name, columns in tables.items():
                 print(f"[ThoughtSpot]    Preparing {table_name.upper()}...", flush=True)
-                table_tml = self.create_table_tml(table_name, columns, connection_name, database, schema, all_tables=None)
+                table_tml = self.create_table_tml(table_name, columns, connection_name, database, schema, all_tables=None, foreign_keys=foreign_keys)
                 table_tmls_batch1.append(table_tml)
                 table_names_order.append(table_name.upper())
 
@@ -1378,6 +1438,12 @@ class ThoughtSpotDeployer:
                 log_progress("   ❌ No tables were created successfully in Batch 1")
                 return results
 
+            # Assign tags to tables
+            table_guid_list = list(table_guids.values())
+            print(f"🔍 DEBUG BEFORE TAG CALL: tag_name='{tag_name}', table_guid_list={table_guid_list}")
+            log_progress(f"🏷️  Assigning tag '{tag_name}' to {len(table_guid_list)} tables...")
+            self.assign_tags_to_objects(table_guid_list, 'LOGICAL_TABLE', tag_name)
+
             batch1_time = time.time() - batch1_start
             log_progress(f"✅ Batch 1 complete: {len(table_guids)} tables created ({batch1_time:.1f}s)")
 
@@ -1404,7 +1470,7 @@ class ThoughtSpotDeployer:
                 # Create table TML WITH joins_with section AND the table GUID
                 table_tml = self.create_table_tml(
                     table_name, columns, connection_name, database, schema,
-                    all_tables=tables, table_guid=table_guid
+                    all_tables=tables, table_guid=table_guid, foreign_keys=foreign_keys
                 )
                 table_tmls_batch2.append(table_tml)
                 table_names_order_batch2.append(table_name_upper)
@@ -1527,6 +1593,11 @@ class ThoughtSpotDeployer:
                         log_progress(f"✅ Model created ({model_time:.1f}s)")
                         results['model'] = model_name
                         results['model_guid'] = model_guid
+                        
+                        # Assign tag to model
+                        print(f"🔍 DEBUG BEFORE TAG CALL: tag_name='{tag_name}', model_guid='{model_guid}'")
+                        log_progress(f"🏷️  Assigning tag '{tag_name}' to model...")
+                        self.assign_tags_to_objects([model_guid], 'LOGICAL_TABLE', tag_name)
 
                         # Step 3.5: Enable Spotter on the model via API
                         try:
@@ -1591,7 +1662,7 @@ class ThoughtSpotDeployer:
                                     liveboard_name=liveboard_name,
                                     llm_model=llm_model  # Pass model selection
                                 )
-                            
+
                             # Check result (for both MCP and TML methods)
                             print(f"🔍 DEBUG: Liveboard result received: {liveboard_result}")
                             print(f"🔍 DEBUG: Success flag: {liveboard_result.get('success')}")
@@ -1617,24 +1688,70 @@ class ThoughtSpotDeployer:
                         obj_response = objects[0].get('response', {})
                         status = obj_response.get('status', {})
                         error_message = status.get('error_message', 'Unknown error')
+                        
+                        # Clean HTML tags from error message (ThoughtSpot sometimes returns HTML)
+                        error_message = re.sub(r'<[^>]+>', '', error_message).strip()
+                        if not error_message:
+                            error_message = 'Schema validation failed (no details provided)'
+                        
                         error_code = status.get('error_code', 'N/A')
                         
+                        # Try to extract additional error details from various response fields
+                        error_details = []
+                        
+                        # Check for detailed error messages in different response structures
+                        if 'error_details' in status:
+                            error_details.append(f"Error details: {status.get('error_details')}")
+                        
+                        if 'validation_errors' in obj_response:
+                            error_details.append(f"Validation errors: {obj_response.get('validation_errors')}")
+                        
+                        if 'warnings' in obj_response:
+                            error_details.append(f"Warnings: {obj_response.get('warnings')}")
+                        
+                        # Check header for additional info
+                        header = obj_response.get('header', {})
+                        if 'error' in header:
+                            error_details.append(f"Header error: {header.get('error')}")
+                        
                         # Get any additional error details
                         full_response = json.dumps(objects[0], indent=2)
                         
+                        # Save the TML that failed for debugging
+                        import tempfile
+                        # os is already imported at module level
+                        try:
+                            debug_dir = os.path.join(tempfile.gettempdir(), 'thoughtspot_debug')
+                            os.makedirs(debug_dir, exist_ok=True)
+                            failed_tml_path = os.path.join(debug_dir, f'failed_model_{datetime.now().strftime("%Y%m%d_%H%M%S")}.tml')
+                            with open(failed_tml_path, 'w') as f:
+                                f.write(model_tml)
+                            log_progress(f"💾 Failed TML saved to: {failed_tml_path}")
+                            print(f"💾 Failed TML saved to: {failed_tml_path}")
+                        except Exception as save_error:
+                            log_progress(f"⚠️ Could not save failed TML: {save_error}")
+                        
                         # Build comprehensive error message
                         error = f"Model validation failed: {error_message}"
                         if error_code != 'N/A':
                             error += f" (Error code: {error_code})"
                         
+                        if error_details:
+                            error += f"\n\nAdditional details:\n" + "\n".join(error_details)
+                        
                         print(f"📋 Full model response: {full_response}")  # DEBUG: Show full response
                         print(f"   ❌ {error}")
                         log_progress(f"   ❌ {error}")
                         log_progress(f"   📋 Full response details:")
                         log_progress(f"{full_response}")
                         
+                        # Include the TML snippet in error for quick debugging
+                        tml_preview = model_tml[:500] + "..." if len(model_tml) > 500 else model_tml
+                        log_progress(f"\n📄 TML that was sent (first 500 chars):\n{tml_preview}")
+                        
                         results['errors'].append(error)
                         results['errors'].append(f"Full API response: {full_response}")
+                        results['errors'].append(f"Failed TML saved to: {failed_tml_path if 'failed_tml_path' in locals() else 'N/A'}")
                 else:
                     error = "Model failed: No objects in response"
                     log_progress(f"   ❌ {error}")

verify_outliers.py

@@ -1,165 +0,0 @@
-#!/usr/bin/env python3
-"""
-Verify the strategic outliers that were injected
-"""
-
-from dotenv import load_dotenv
-import os
-import snowflake.connector
-
-def get_snowflake_connection():
-    """Get Snowflake connection for the specific schema"""
-    from snowflake_auth import get_snowflake_connection_params
-    
-    conn_params = get_snowflake_connection_params()
-    conn_params.pop('schema', None)  # Remove schema to avoid duplicate
-    
-    conn = snowflake.connector.connect(
-        **conn_params,
-        schema='20250923_090309_THOUG_SAL'
-    )
-    return conn
-
-def verify_outliers():
-    """Verify the strategic outliers that were created"""
-    
-    print("🔍 VERIFYING STRATEGIC OUTLIERS")
-    print("=" * 50)
-    
-    conn = get_snowflake_connection()
-    cursor = conn.cursor()
-    
-    try:
-        # 1. Check high-value customers with poor outcomes
-        print("📊 High-value customers with poor interaction outcomes:")
-        cursor.execute("""
-            SELECT 
-                c.CUSTOMERID,
-                c.PREFERENCES,
-                COUNT(DISTINCT st.TRANSACTIONID) as transaction_count,
-                AVG(st.AMOUNT) as avg_transaction_value,
-                COUNT(DISTINCT ci.INTERACTIONID) as interaction_count,
-                SUM(CASE WHEN ci.OUTCOME = 'Unsuccessful' THEN 1 ELSE 0 END) as unsuccessful_interactions
-            FROM CUSTOMERS c
-            LEFT JOIN SALESTRANSACTIONS st ON c.CUSTOMERID = st.CUSTOMERID
-            LEFT JOIN CUSTOMERINTERACTIONS ci ON c.CUSTOMERID = ci.CUSTOMERID
-            WHERE c.PREFERENCES LIKE '%High-value%'
-            GROUP BY c.CUSTOMERID, c.PREFERENCES
-            ORDER BY avg_transaction_value DESC
-            LIMIT 5
-        """)
-        
-        results = cursor.fetchall()
-        for row in results:
-            print(f"   Customer {row[0]}: {row[2]} transactions, avg ${row[3]:.2f}, {row[5]} unsuccessful interactions")
-        
-        # 2. Check channel performance
-        print("\n📊 Channel performance analysis:")
-        cursor.execute("""
-            SELECT 
-                c.CHANNELID,
-                c.NAME,
-                c.TYPE,
-                COUNT(ci.INTERACTIONID) as total_interactions,
-                SUM(CASE WHEN ci.OUTCOME = 'Successful' THEN 1 ELSE 0 END) as successful_interactions,
-                SUM(CASE WHEN ci.OUTCOME = 'Unsuccessful' THEN 1 ELSE 0 END) as unsuccessful_interactions,
-                ROUND(SUM(CASE WHEN ci.OUTCOME = 'Successful' THEN 1 ELSE 0 END) * 100.0 / COUNT(ci.INTERACTIONID), 2) as success_rate
-            FROM CHANNELS c
-            LEFT JOIN CUSTOMERINTERACTIONS ci ON c.CHANNELID = ci.CHANNELID
-            GROUP BY c.CHANNELID, c.NAME, c.TYPE
-            ORDER BY success_rate DESC
-        """)
-        
-        results = cursor.fetchall()
-        for row in results:
-            print(f"   Channel {row[0]} ({row[1]}): {row[3]} interactions, {row[4]} successful, {row[5]} unsuccessful, {row[6]}% success rate")
-        
-        # 3. Check recent performance degradation
-        print("\n📊 Recent performance (last 30 days):")
-        cursor.execute("""
-            SELECT 
-                DATE(ci.DATE) as interaction_date,
-                COUNT(ci.INTERACTIONID) as total_interactions,
-                SUM(CASE WHEN ci.OUTCOME = 'Successful' THEN 1 ELSE 0 END) as successful_interactions,
-                SUM(CASE WHEN ci.OUTCOME = 'Unsuccessful' THEN 1 ELSE 0 END) as unsuccessful_interactions,
-                ROUND(SUM(CASE WHEN ci.OUTCOME = 'Unsuccessful' THEN 1 ELSE 0 END) * 100.0 / COUNT(ci.INTERACTIONID), 2) as failure_rate
-            FROM CUSTOMERINTERACTIONS ci
-            WHERE ci.DATE >= CURRENT_DATE - 30
-            GROUP BY DATE(ci.DATE)
-            ORDER BY interaction_date DESC
-            LIMIT 10
-        """)
-        
-        results = cursor.fetchall()
-        for row in results:
-            print(f"   {row[0]}: {row[1]} interactions, {row[2]} successful, {row[3]} unsuccessful, {row[4]}% failure rate")
-        
-        # 4. Check cross-channel inconsistency
-        print("\n📊 Cross-channel inconsistency patterns:")
-        cursor.execute("""
-            SELECT 
-                ci.CUSTOMERID,
-                c.NAME as channel_name,
-                COUNT(ci.INTERACTIONID) as interactions,
-                SUM(CASE WHEN ci.OUTCOME = 'Successful' THEN 1 ELSE 0 END) as successful,
-                SUM(CASE WHEN ci.OUTCOME = 'Unsuccessful' THEN 1 ELSE 0 END) as unsuccessful
-            FROM CUSTOMERINTERACTIONS ci
-            JOIN CHANNELS c ON ci.CHANNELID = c.CHANNELID
-            WHERE ci.CUSTOMERID IN (
-                SELECT CUSTOMERID FROM CUSTOMERINTERACTIONS 
-                WHERE CUSTOMERID IN (10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
-                GROUP BY CUSTOMERID
-                HAVING COUNT(DISTINCT CHANNELID) > 1
-            )
-            GROUP BY ci.CUSTOMERID, c.NAME
-            ORDER BY ci.CUSTOMERID, c.NAME
-        """)
-        
-        results = cursor.fetchall()
-        current_customer = None
-        for row in results:
-            if current_customer != row[0]:
-                print(f"   Customer {row[0]}:")
-                current_customer = row[0]
-            print(f"     {row[1]}: {row[2]} interactions, {row[3]} successful, {row[4]} unsuccessful")
-        
-        # 5. Check missed opportunities
-        print("\n📊 High-value recent transactions without interactions:")
-        cursor.execute("""
-            SELECT 
-                st.CUSTOMERID,
-                COUNT(st.TRANSACTIONID) as recent_transactions,
-                AVG(st.AMOUNT) as avg_amount,
-                COUNT(ci.INTERACTIONID) as recent_interactions
-            FROM SALESTRANSACTIONS st
-            LEFT JOIN CUSTOMERINTERACTIONS ci ON st.CUSTOMERID = ci.CUSTOMERID 
-                AND ci.DATE >= CURRENT_DATE - 7
-            WHERE st.DATE >= CURRENT_DATE - 7
-                AND st.AMOUNT > 800
-            GROUP BY st.CUSTOMERID
-            HAVING COUNT(ci.INTERACTIONID) = 0
-            ORDER BY avg_amount DESC
-            LIMIT 5
-        """)
-        
-        results = cursor.fetchall()
-        for row in results:
-            print(f"   Customer {row[0]}: {row[1]} high-value transactions (avg ${row[2]:.2f}), 0 recent interactions")
-        
-        print("\n" + "=" * 50)
-        print("✅ OUTLIER VERIFICATION COMPLETE!")
-        print("These patterns are perfect for demonstrating:")
-        print("• Poor customer experience across channels")
-        print("• Missed revenue opportunities") 
-        print("• Channel performance disparities")
-        print("• Need for AI-driven insights and next best actions")
-        
-    except Exception as e:
-        print(f"❌ Error verifying outliers: {e}")
-        raise
-    finally:
-        cursor.close()
-        conn.close()
-
-if __name__ == "__main__":
-    verify_outliers()