initial commit

.env.example

@@ -0,0 +1,14 @@
+# Environment Variables for InsightPilot
+
+# Required: Groq API Key for LLM access
+# Get your key from: https://console.groq.com/
+GROQ_API_KEY=your_groq_api_key_here
+
+# Optional: Database URL (defaults to SQLite)
+# For SQLite (default): sqlite:///./test.db
+# For PostgreSQL: postgresql://user:password@host:port/dbname
+DATABASE_URL=sqlite:///./test.db
+
+# Optional: Configure other settings
+# DEBUG=false
+# LOG_LEVEL=info

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+.venv/
+backend/.env
+backend/backend/static

HF_DEPLOYMENT_CHECKLIST.md

@@ -0,0 +1,156 @@
+# 🚀 HF Spaces Deployment Checklist
+
+Use this checklist to ensure smooth deployment to Hugging Face Spaces.
+
+## Pre-Deployment
+
+- [ ] **Get Groq API Key**
+  - Sign up at https://console.groq.com/
+  - Generate API key
+  - Keep it safe for Step 5
+
+- [ ] **Create HF Account**
+  - Sign up at https://huggingface.co/join
+  - Verify email
+
+- [ ] **Build Frontend (Optional)**
+  - Windows: Run `build_frontend.bat`
+  - Mac/Linux: Run `./build_frontend.sh`
+  - Verify `frontend/dist/` folder exists
+
+## Deployment Steps
+
+### 1. Create New Space
+- [ ] Go to https://huggingface.co/new-space
+- [ ] Set Space name (e.g., `insightpilot`)
+- [ ] Choose SDK: **Gradio**
+- [ ] Choose License: **MIT**
+- [ ] Choose Hardware: **CPU basic** (free)
+- [ ] Set Visibility: Public or Private
+- [ ] Click **Create Space**
+
+### 2. Prepare Files
+- [ ] Copy/create these files:
+  ```
+  ✅ app.py
+  ✅ requirements.txt
+  ✅ README_HF.md → rename to README.md
+  ✅ backend/ (entire folder)
+  ✅ data/ (optional - sample data)
+  ✅ frontend/dist/ (optional - if built)
+  ```
+
+### 3. Upload to Space
+
+**Option A: Git (Recommended)**
+- [ ] Clone your Space:
+  ```bash
+  git clone https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME
+  cd SPACE_NAME
+  ```
+- [ ] Copy files into the cloned directory
+- [ ] Add files:
+  ```bash
+  git add .
+  git commit -m "Initial deployment of InsightPilot"
+  git push
+  ```
+
+**Option B: Web Upload**
+- [ ] Go to your Space page
+- [ ] Click **Files** → **Add file** → **Upload files**
+- [ ] Drag and drop all files
+- [ ] Commit changes
+
+### 4. Configure Space
+
+- [ ] Go to **Settings** → **Repository secrets**
+- [ ] Click **New secret**
+- [ ] Add:
+  - Name: `GROQ_API_KEY`
+  - Value: Your Groq API key
+- [ ] Click **Add**
+
+### 5. Monitor Build
+
+- [ ] Go to **Logs** tab
+- [ ] Wait for build to complete (3-5 minutes)
+- [ ] Check for errors
+- [ ] Wait for "Running" status
+
+### 6. Test Deployment
+
+- [ ] Visit your Space URL
+- [ ] Test **API Access** tab:
+  - [ ] Enter a question
+  - [ ] Click Analyze
+  - [ ] Verify response
+  
+- [ ] Test **Upload Dataset** tab:
+  - [ ] Upload a CSV
+  - [ ] Verify success message
+  
+- [ ] Test **Analytics Dashboard** (if frontend built):
+  - [ ] Check UI loads
+  - [ ] Try sample questions
+  - [ ] Verify visualizations
+  - [ ] Download PDF report
+
+## Post-Deployment
+
+### Optional Enhancements
+
+- [ ] **Add README badge** to your GitHub repo:
+  ```markdown
+  [![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME)
+  ```
+
+- [ ] **Enable Analytics**
+  - Settings → Enable analytics
+  - Track visitor count
+
+- [ ] **Customize Space**
+  - Edit README.md for better description
+  - Add screenshots
+  - Update emoji/colors in metadata
+
+- [ ] **Set up monitoring**
+  - Check Logs regularly
+  - Monitor Usage in Settings
+
+### Troubleshooting Common Issues
+
+- [ ] **Build failed**: Check `requirements.txt` syntax
+- [ ] **Import errors**: Verify all backend files uploaded
+- [ ] **GROQ_API_KEY error**: Check secret name matches exactly
+- [ ] **Frontend not loading**: Verify `frontend/dist/` uploaded
+- [ ] **Port errors**: App uses 7860 (HF default)
+- [ ] **Database errors**: Check file paths in backend
+
+## Success Indicators
+
+✅ Space shows "Running" status
+✅ No errors in Logs
+✅ Can ask questions and get responses
+✅ Can upload CSV files
+✅ Can download PDF reports
+✅ Visualizations appear correctly
+
+## Share Your Space
+
+Once deployed, share at:
+```
+https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME
+```
+
+Add to:
+- [ ] GitHub README
+- [ ] LinkedIn
+- [ ] Twitter
+- [ ] Portfolio
+
+---
+
+**🎉 Congratulations on deploying InsightPilot to Hugging Face Spaces!**
+
+Need help? See `DEPLOYMENT.md` for detailed instructions.

README.md

@@ -1,13 +1,114 @@
----
-title: Insightpilot
-emoji: 🚀
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 6.0.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# InsightPilot – Autonomous Analytics Agent
+
+InsightPilot is a production-style AI analyst that turns natural-language business questions into validated SQL, visualizations, insights, and executive-ready PDF reports. The stack combines LangGraph (deterministic agent workflow), FastAPI, SQLAlchemy, ReportLab, and a modern React dashboard.
+
+## Project Highlights
+
+- **Agentic LangGraph pipeline** – deterministic tool-calling chain (intent → schema → NL2SQL → execution → diagnostics → visualization → PDF) keeps results auditable.
+- **Advanced analytics automation** – dedicated trend and anomaly modules mine every result set and stream context-aware summaries to the UI and PDF.
+- **ReportLab title-page exports** – branded title page, SQL appendix, visuals, and analytics sections ready for exec briefings.
+- **Draw.io workflow assets** – editable diagram (`docs/agent-workflow.drawio`) plus lightweight SVG for README embeds.
+- **Reusable data ingestion** – dataset catalog + multi-table CSV upload lets you retarget the agent to any warehouse quickly.
+- **Groq Llama‑3 tooling** – low-latency NL→SQL and narrative insight generation.
+
+## Workflow Diagram (Draw.io)
+
+![Agent Workflow](docs/agent-workflow.svg)
+
+> Edit the source diagram at `docs/agent-workflow.drawio` in Draw.io/Lucidchart and export a fresh SVG/PNG if the workflow changes.
+
+## Advanced Analytics Modules
+
+| Module | What it does | Output surfaces |
+| --- | --- | --- |
+| **Trend detection** | Converts the query result into a monthly time series, fits a regression, and quantifies direction, slope, and % change. | Chat insights, Latest Analysis cards, PDF “Trend Diagnostics”. |
+| **Anomaly detection** | Computes z-scores across the same period and flags statistically significant outliers. | Latest Analysis cards, PDF “Anomaly Highlights”. |
+
+The insight LLM receives both diagnostics, so follow-up prompts remain grounded in prior conclusions.
+
+## Architecture
+
+| Layer | Tech | Notes |
+| --- | --- | --- |
+| API & Orchestration | FastAPI + LangGraph | Defines graph nodes for schema, NL2SQL, execution, viz, insight, and report building. |
+| LLM | Groq `llama3-70b-8192` | Deterministic, low-latency NL2SQL + insight generation. |
+| Data | SQLite via SQLAlchemy | Auto-generated `sales` dataset for local testing. |
+| Visualization & Reports | Matplotlib, ReportLab/Platypus | Charts saved to `backend/static`, referenced in UI/PDF. |
+| Frontend | React + Vite | Modern dashboard with prompt chips, chat, result stack, PDF download. |
+
+## Prerequisites
+
+- Python 3.9+
+- Node.js 16+
+- Groq API Key (for NL2SQL + insight generation)
+
+## Backend Setup
+
+```bash
+cd backend
+python -m venv venv
+source venv/bin/activate        # Windows: venv\Scripts\activate
+pip install -r requirements.txt
+cp .env.example .env            # add GROQ_API_KEY and optional DATABASE_URL
+python create_db.py             # optional; startup also seeds the sales table
+python -m app.main              # runs on http://localhost:8000
+```
+
+`DATABASE_URL` defaults to `sqlite:///./test.db`. Ensure the `.env` file includes your `GROQ_API_KEY` for Groq access.
+
+## Frontend Setup
+
+```bash
+cd frontend
+npm install
+npm run dev                     # http://localhost:5173
+```
+
+## Usage Flow
+
+1. Start backend and frontend dev servers.
+1. Upload CSVs via the “Dataset Control” card (or call `POST /api/upload-csv` with `file` + `table_name`). The agent now validates table names, swaps in new tables on demand, and refreshes the dataset catalog for quick reuse.
+1. Open the React app, pick a quick prompt (e.g., “What were total sales by category?”) or type your own.
+1. InsightPilot:
+
+    - Reads DB schema & crafts SQL via Groq Llama‑3.
+    - Executes SQL with SQLAlchemy/Pandas.
+    - Generates Matplotlib charts and Groq insight summaries.
+    - Streams insights to the chat, shows SQL/data preview, and produces a PDF.
+
+1. Download the PDF to share with stakeholders.
+
+## Dataset Upload & Multi-table Ingestion
+
+- **Upload endpoint:** `POST /api/upload-csv` accepts `file` + `table_name`. Table names are validated (`[A-Za-z_][A-Za-z0-9_]*`) to keep SQL safe, and uploads replace/create tables atomically.
+- **Catalog endpoint:** `GET /api/datasets` returns discovered tables along with row counts and column names so the frontend can surface a live catalog.
+- **Frontend controls:** the Dataset Control card now includes a target-table input, catalog refresh button, and a list of available tables—making it trivial to pivot the agent to another fact table without redeploying anything.
+
+## Project Structure
+
+```text
+backend/
+  app/
+    agents/graph.py          # LangGraph workflow
+    api/routes.py            # FastAPI routes
+    core/config.py           # Settings + env
+    db/database.py           # Engine + auto seed
+    services/analytics.py    # Trend & anomaly detection modules
+    services/pdf_generator.py# ReportLab report builder
+    services/csv_loader.py   # CSV ingestion + dataset catalog helpers
+  static/                    # Generated charts & PDFs
+  create_db.py               # Manual seed script
+  requirements.txt
+
+frontend/
+  src/App.jsx, App.css       # React dashboard
+  package.json
+```
+
+## Future Ideas
+
+- Intent-specific LangGraph branches (comparison vs. forecasting).
+- Supabase/Postgres adapters with connection pooling.
+- Auth + team workspaces for insights and PDF history.
+
+Enjoy exploring data with InsightPilot! 🚀

README_HF.md

@@ -0,0 +1,158 @@
+---
+title: InsightPilot - Autonomous Analytics Agent
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.16.0
+app_file: app.py
+pinned: false
+license: mit
+python_version: 3.10
+---
+
+# InsightPilot – Autonomous Analytics Agent
+
+<div align="center">
+
+[![Powered by LangGraph](https://img.shields.io/badge/Powered%20by-LangGraph-blue)](https://github.com/langchain-ai/langgraph)
+[![FastAPI](https://img.shields.io/badge/FastAPI-0.109.0-green)](https://fastapi.tiangolo.com/)
+[![Groq](https://img.shields.io/badge/LLM-Groq%20Llama--3-orange)](https://groq.com/)
+
+</div>
+
+InsightPilot is a production-ready AI analyst that transforms natural language questions into validated SQL queries, interactive visualizations, comprehensive insights, and executive-ready PDF reports.
+
+## 🌟 Features
+
+- **🤖 Agentic LangGraph Pipeline** – Deterministic tool-calling workflow (intent → schema → NL2SQL → execution → diagnostics → visualization → PDF)
+- **📊 Advanced Analytics** – Automated trend detection and anomaly analysis with statistical insights
+- **📄 PDF Report Generation** – Executive-ready reports with branded title pages, charts, and SQL appendix
+- **📁 Multi-table Support** – Easy CSV upload and dataset catalog management
+- **⚡ Real-time Streaming** – Live insights streamed to the UI as they're generated
+- **🔍 Groq Llama-3 Powered** – Low-latency NL→SQL and narrative insight generation
+
+## 🚀 Quick Start on Hugging Face Spaces
+
+1. **Set Environment Variables** (Required)
+   - Go to Settings → Repository Secrets
+   - Add `GROQ_API_KEY` with your Groq API key ([Get one here](https://console.groq.com/))
+
+2. **Upload Your Data** (Optional)
+   - Use the "Upload Dataset" tab to add your CSV files
+   - Or work with the pre-loaded sample sales dataset
+
+3. **Ask Questions**
+   - Use the Analytics Dashboard to ask natural language questions
+   - Example: "What were the total sales by category last quarter?"
+   - Get SQL, visualizations, insights, and downloadable PDF reports
+
+## 🏗️ Architecture
+
+| Component | Technology | Purpose |
+|-----------|-----------|---------|
+| **LLM Orchestration** | LangGraph + Groq Llama-3 70B | Deterministic agent workflow with tool calling |
+| **API & Backend** | FastAPI + SQLAlchemy | RESTful API, database management |
+| **Analytics** | Pandas, NumPy, SciPy | Trend detection, anomaly analysis |
+| **Visualization** | Matplotlib, ReportLab | Charts and PDF report generation |
+| **Database** | SQLite | Lightweight, persistent data storage |
+| **Frontend** | React + Vite (optional) | Modern interactive dashboard |
+| **Interface** | Gradio | HF Spaces integration |
+
+## 📊 Advanced Analytics Modules
+
+- **Trend Detection**: Time series regression analysis with slope quantification and % change metrics
+- **Anomaly Detection**: Z-score based statistical outlier identification
+- **Insight Generation**: Context-aware narrative summaries powered by Groq LLM
+
+## 🛠️ Tech Stack
+
+```
+Backend:  FastAPI + LangGraph + LangChain + Groq
+Data:     SQLite + SQLAlchemy + Pandas
+Viz:      Matplotlib + ReportLab/Platypus
+Frontend: React + Vite (embedded in Gradio)
+Deploy:   Hugging Face Spaces (Gradio SDK)
+```
+
+## 📁 Project Structure
+
+```
+.
+├── app.py                      # Gradio wrapper for HF Spaces
+├── requirements.txt            # Python dependencies
+├── backend/
+│   ├── app/
+│   │   ├── main.py            # FastAPI application
+│   │   ├── agents/graph.py    # LangGraph workflow
+│   │   ├── api/routes.py      # API endpoints
+│   │   ├── core/config.py     # Settings & environment
+│   │   ├── db/database.py     # Database engine & seeding
+│   │   └── services/          # Analytics, PDF, CSV modules
+│   ├── static/                # Generated charts & PDFs
+│   └── requirements.txt       # Backend-specific deps
+├── frontend/                  # React dashboard (optional)
+└── data/                      # Sample datasets
+
+```
+
+## 🔑 Environment Variables
+
+| Variable | Description | Required |
+|----------|-------------|----------|
+| `GROQ_API_KEY` | Groq API key for LLM access | ✅ Yes |
+| `DATABASE_URL` | Database connection string | ⚪ Optional (defaults to SQLite) |
+
+## 📖 Usage Examples
+
+**Question:** "What were the top 5 products by revenue last year?"
+
+**InsightPilot will:**
+1. ✅ Analyze your database schema
+2. ✅ Generate optimized SQL query
+3. ✅ Execute query and validate results
+4. ✅ Create visualizations (bar charts, trends)
+5. ✅ Perform trend & anomaly analysis
+6. ✅ Generate narrative insights
+7. ✅ Build downloadable PDF report
+
+## 🎯 Use Cases
+
+- **Business Analytics**: Ad-hoc reporting without SQL knowledge
+- **Executive Briefings**: Automated PDF reports with insights
+- **Data Exploration**: Quick analysis of uploaded CSV datasets
+- **Trend Analysis**: Automated time-series analytics
+- **Anomaly Detection**: Statistical outlier identification
+
+## 🚧 Limitations & Notes
+
+- **Free HF Spaces**: CPU-only tier; suitable for moderate traffic
+- **Database**: SQLite with persistent storage (50GB limit)
+- **File Cleanup**: Old PDFs/charts should be periodically removed
+- **Concurrent Users**: May need rate limiting for production use
+
+## 🔮 Future Enhancements
+
+- Multi-tenant workspaces with authentication
+- Postgres/Supabase adapter for production databases
+- Real-time collaborative dashboards
+- Forecast & prediction modules
+- Custom visualization templates
+
+## 📝 License
+
+MIT License - see LICENSE file for details
+
+## 🤝 Contributing
+
+Contributions welcome! Please open an issue or submit a PR.
+
+## 🔗 Links
+
+- **Repository**: [GitHub](https://github.com/zenitsu0509/InsightPilot)
+- **Documentation**: See original README in repo
+- **Groq Platform**: [Get API Key](https://console.groq.com/)
+
+---
+
+**Built with ❤️ using LangGraph, FastAPI, and Groq**

app.py

@@ -0,0 +1,194 @@
+import gradio as gr
+import os
+import sys
+import uvicorn
+from threading import Thread
+import time
+import shutil
+
+# Add backend to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'backend'))
+
+from app.main import app as fastapi_app
+
+# Set up paths for HF Spaces
+STATIC_DIR = "backend/static"
+FRONTEND_BUILD_DIR = "frontend/dist"
+DB_PATH = "test.db"
+
+os.makedirs(STATIC_DIR, exist_ok=True)
+
+def start_fastapi():
+    """Start FastAPI server in background thread"""
+    uvicorn.run(fastapi_app, host="0.0.0.0", port=7860, log_level="info")
+
+# Start FastAPI in a separate thread
+fastapi_thread = Thread(target=start_fastapi, daemon=True)
+fastapi_thread.start()
+
+# Give FastAPI time to start
+time.sleep(2)
+
+# Create Gradio interface
+with gr.Blocks(title="InsightPilot - Autonomous Analytics Agent", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🚀 InsightPilot – Autonomous Analytics Agent
+    
+    An AI-powered analyst that turns natural language questions into SQL queries, visualizations, and executive-ready PDF reports.
+    
+    **Powered by:** LangGraph + FastAPI + Groq Llama-3
+    """)
+    
+    with gr.Tab("📊 Analytics Dashboard"):
+        gr.Markdown("""
+        ### Access the full React dashboard below
+        
+        The dashboard provides:
+        - Natural language query interface
+        - Real-time SQL generation and execution
+        - Interactive visualizations
+        - PDF report generation
+        - Dataset management and CSV upload
+        """)
+        
+        # Embed the React app if built, otherwise show API info
+        if os.path.exists(FRONTEND_BUILD_DIR):
+            gr.HTML(f"""
+            <iframe src="/frontend" width="100%" height="800px" frameborder="0"></iframe>
+            """)
+        else:
+            gr.Markdown("""
+            ⚠️ **Frontend not built yet.** 
+            
+            To build the frontend:
+            ```bash
+            cd frontend
+            npm install
+            npm run build
+            ```
+            
+            For now, you can interact with the API directly at the endpoints below.
+            """)
+    
+    with gr.Tab("🔧 API Access"):
+        gr.Markdown("""
+        ### Direct API Endpoints
+        
+        - **Base URL:** `http://localhost:7860/api`
+        - **Query Analysis:** `POST /api/analyze` - Send natural language questions
+        - **Upload CSV:** `POST /api/upload-csv` - Upload datasets
+        - **List Datasets:** `GET /api/datasets` - View available tables
+        - **API Docs:** [Interactive Swagger UI](http://localhost:7860/docs)
+        
+        ### Quick Test
+        """)
+        
+        with gr.Row():
+            question_input = gr.Textbox(
+                label="Ask a Question",
+                placeholder="What were the total sales by category?",
+                lines=2
+            )
+            submit_btn = gr.Button("Analyze", variant="primary")
+        
+        result_output = gr.JSON(label="Analysis Result")
+        
+        def analyze_question(question):
+            """Call the FastAPI analyze endpoint"""
+            import requests
+            try:
+                response = requests.post(
+                    "http://localhost:7860/api/analyze",
+                    json={"question": question},
+                    timeout=60
+                )
+                return response.json()
+            except Exception as e:
+                return {"error": str(e)}
+        
+        submit_btn.click(
+            fn=analyze_question,
+            inputs=[question_input],
+            outputs=[result_output]
+        )
+    
+    with gr.Tab("📤 Upload Dataset"):
+        gr.Markdown("""
+        ### Upload CSV Dataset
+        
+        Upload your own CSV files to analyze custom data.
+        """)
+        
+        with gr.Row():
+            csv_file = gr.File(label="Select CSV File", file_types=[".csv"])
+            table_name = gr.Textbox(
+                label="Table Name",
+                placeholder="sales_data",
+                value="uploaded_data"
+            )
+        
+        upload_btn = gr.Button("Upload", variant="primary")
+        upload_result = gr.Textbox(label="Upload Status", lines=3)
+        
+        def upload_csv(file, table):
+            """Upload CSV to the database"""
+            import requests
+            try:
+                if file is None:
+                    return "Please select a CSV file"
+                
+                with open(file.name, 'rb') as f:
+                    files = {'file': (os.path.basename(file.name), f, 'text/csv')}
+                    data = {'table_name': table}
+                    response = requests.post(
+                        "http://localhost:7860/api/upload-csv",
+                        files=files,
+                        data=data,
+                        timeout=30
+                    )
+                    if response.status_code == 200:
+                        result = response.json()
+                        return f"✅ Success! Uploaded {result.get('rows', 0)} rows to table '{table}'"
+                    else:
+                        return f"❌ Error: {response.text}"
+            except Exception as e:
+                return f"❌ Error: {str(e)}"
+        
+        upload_btn.click(
+            fn=upload_csv,
+            inputs=[csv_file, table_name],
+            outputs=[upload_result]
+        )
+    
+    with gr.Tab("ℹ️ About"):
+        gr.Markdown("""
+        ## About InsightPilot
+        
+        InsightPilot is a production-style AI analyst that combines:
+        
+        - **Agentic LangGraph Pipeline:** Deterministic tool-calling workflow
+        - **Advanced Analytics:** Automated trend detection and anomaly analysis
+        - **PDF Reports:** Executive-ready reports with visualizations
+        - **Multi-table Support:** Easy CSV upload and dataset management
+        
+        ### Tech Stack
+        - **Backend:** FastAPI + LangGraph + SQLAlchemy
+        - **LLM:** Groq Llama-3 (70B)
+        - **Frontend:** React + Vite
+        - **Database:** SQLite
+        - **Visualization:** Matplotlib + ReportLab
+        
+        ### Environment Variables
+        Make sure to set your `GROQ_API_KEY` in the Hugging Face Space secrets!
+        
+        ### Repository
+        [View on GitHub](https://github.com/zenitsu0509/InsightPilot)
+        """)
+
+# Mount frontend if built
+if os.path.exists(FRONTEND_BUILD_DIR):
+    from fastapi.staticfiles import StaticFiles
+    fastapi_app.mount("/frontend", StaticFiles(directory=FRONTEND_BUILD_DIR, html=True), name="frontend")
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

backend/.env.example

@@ -0,0 +1,2 @@
+GROQ_API_KEY="your_groq_api_key_here"
+DATABASE_URL=sqlite:///./test.db

backend/app/agents/graph.py

@@ -0,0 +1,424 @@
+import json
+from typing import TypedDict, Annotated, List, Union, Any
+
+import matplotlib
+import matplotlib.pyplot as plt
+import os
+import pandas as pd
+import uuid
+from langchain_groq import ChatGroq
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langgraph.graph import StateGraph, END
+
+from app.core.config import settings
+from app.db.database import get_db_schema, engine
+from app.services.pdf_generator import generate_pdf_report
+from app.services.analytics import run_advanced_analytics
+
+matplotlib.use('Agg') # Use non-interactive backend
+
+# Define State
+class AgentState(TypedDict, total=False):
+    query: str
+    history: List[dict]
+    schema: str
+    sql_query: str
+    data: Any # Pandas DataFrame as dict or list
+    visualization_path: str
+    visualization_summary: str
+    trend_analysis: dict
+    anomaly_analysis: dict
+    forecast_analysis: dict
+    statistical_tests: dict
+    insights: str
+    report_path: str
+    error: str
+def _format_history(history: List[dict]) -> str:
+    if not history:
+        return "None"
+    rendered = []
+    for turn in history[-5:]:
+        question = turn.get("question", "")
+        answer = turn.get("insights", "")
+        rendered.append(f"User: {question}\nAgent: {answer}")
+    return "\n---\n".join(rendered)
+
+
+# LLM Setup
+def get_llm():
+    if not settings.GROQ_API_KEY:
+        # Fallback or mock if needed, but for now assume key is present or will fail
+        return None
+    return ChatGroq(
+        temperature=0,
+        model_name="openai/gpt-oss-120b",
+        api_key=settings.GROQ_API_KEY,
+    )
+
+
+def _summarize_dataframe(df: pd.DataFrame) -> List[dict]:
+    summary = []
+    for col in df.columns:
+        series = df[col]
+        summary.append(
+            {
+                "name": col,
+                "dtype": str(series.dtype),
+                "numeric": pd.api.types.is_numeric_dtype(series),
+                "unique_count": int(series.nunique()),
+                "sample_values": series.dropna().astype(str).unique().tolist()[:3],
+            }
+        )
+    return summary
+
+
+def _fallback_chart_plan(df: pd.DataFrame) -> dict:
+    numeric_cols = list(df.select_dtypes(include=["number", "bool"]).columns)
+    categorical_cols = list(df.select_dtypes(include=["object", "category"]).columns)
+
+    if categorical_cols and numeric_cols:
+        return {
+            "chart_type": "bar",
+            "x_field": categorical_cols[0],
+            "y_field": numeric_cols[0],
+            "aggregation": "sum",
+            "top_n": 10,
+            "explanation": f"Bar chart of {numeric_cols[0]} by {categorical_cols[0]}"
+        }
+
+    if len(numeric_cols) >= 2:
+        return {
+            "chart_type": "line",
+            "x_field": numeric_cols[0],
+            "y_field": numeric_cols[1],
+            "aggregation": "none",
+            "top_n": 50,
+            "explanation": f"Line plot of {numeric_cols[1]} vs {numeric_cols[0]}"
+        }
+
+    return {
+        "chart_type": "table",
+        "x_field": None,
+        "y_field": None,
+        "aggregation": "none",
+        "top_n": 0,
+        "explanation": "No suitable numeric/categorical combination for chart"
+    }
+
+
+def _parse_json_response(text: str) -> dict:
+    text = text.strip()
+    if text.startswith("```"):
+        text = text.strip("`")
+        if text.startswith("json"):
+            text = text[4:]
+    start = text.find("{")
+    end = text.rfind("}")
+    if start != -1 and end != -1:
+        candidate = text[start:end + 1]
+        return json.loads(candidate)
+    return json.loads(text)
+
+
+def _suggest_chart_plan(df: pd.DataFrame, query: str) -> dict:
+    plan = _fallback_chart_plan(df)
+    llm = get_llm()
+    if not llm:
+        return plan
+
+    columns_meta = _summarize_dataframe(df)
+    sample_rows = df.head(5).to_dict(orient="records")
+
+    template = """
+    You are an analytics visualization planner. Based on the user's question, the column metadata, and sample rows, choose the most appropriate chart to highlight the insight.
+
+    Allowed chart_type values: bar, line, area, scatter, pie, table.
+    aggregation can be sum, mean, avg, average, count, or none. Use count when only frequency matters.
+    Return ONLY valid JSON with keys: chart_type, x_field, y_field (nullable), aggregation, top_n (int), explanation.
+    Make sure fields exist in the dataset and chart type matches their dtypes (categorical for x axis on bar/pie, numeric for y).
+    Pick at most top 12 categories when using bar/pie.
+
+    Columns: {columns}
+    Sample rows: {sample}
+    User question: {query}
+    """
+
+    prompt = ChatPromptTemplate.from_template(template)
+    chain = prompt | llm | StrOutputParser()
+
+    try:
+        response = chain.invoke({
+            "columns": json.dumps(columns_meta, ensure_ascii=False),
+            "sample": json.dumps(sample_rows, ensure_ascii=False),
+            "query": query,
+        })
+        plan = _parse_json_response(response)
+    except Exception:
+        # keep fallback plan
+        plan.setdefault("explanation", "Heuristic visualization applied")
+    return plan
+
+
+def _aggregate_for_chart(df: pd.DataFrame, x_field: str, y_field: str, aggregation: str) -> pd.DataFrame:
+    if not x_field or x_field not in df.columns:
+        return pd.DataFrame()
+
+    agg = (aggregation or "sum").lower()
+
+    if agg in ("sum", "total", "mean", "avg", "average"):
+        target_col = y_field if y_field in df.columns else None
+        if not target_col:
+            numeric_cols = df.select_dtypes(include=["number", "bool"]).columns
+            target_col = numeric_cols[0] if len(numeric_cols) else None
+        if not target_col or not pd.api.types.is_numeric_dtype(df[target_col]):
+            return pd.DataFrame()
+        agg_fn = "mean" if agg in ("mean", "avg", "average") else "sum"
+        grouped = df.groupby(x_field)[target_col].agg(agg_fn).reset_index()
+        return grouped.rename(columns={target_col: "value"})
+
+    if agg == "count":
+        grouped = df.groupby(x_field).size().reset_index(name="value")
+        return grouped
+
+    if y_field and y_field in df.columns and pd.api.types.is_numeric_dtype(df[y_field]):
+        subset = df[[x_field, y_field]].copy()
+        subset = subset.rename(columns={y_field: "value"})
+        return subset
+
+    return pd.DataFrame()
+
+
+def _render_chart(path: str, df: pd.DataFrame, plan: dict) -> str:
+    chart_type = (plan.get("chart_type") or "bar").lower()
+    x_field = plan.get("x_field")
+    y_field = plan.get("y_field")
+    agg = plan.get("aggregation")
+    top_n = int(plan.get("top_n") or 12)
+
+    plt.figure(figsize=(10, 6))
+
+    if chart_type == "scatter" and x_field and y_field:
+        if x_field in df.columns and y_field in df.columns and \
+                pd.api.types.is_numeric_dtype(df[x_field]) and pd.api.types.is_numeric_dtype(df[y_field]):
+            plot_df = df[[x_field, y_field]].dropna().head(top_n)
+            if plot_df.empty:
+                return ""
+            plt.scatter(plot_df[x_field], plot_df[y_field], color="#5cd4f4")
+            plt.xlabel(x_field)
+            plt.ylabel(y_field)
+            plt.title(plan.get("explanation", f"{y_field} vs {x_field}"))
+            plt.tight_layout()
+            plt.savefig(path, bbox_inches="tight")
+            plt.close()
+            return path
+        return ""
+
+    if not x_field:
+        return ""
+
+    plot_df = _aggregate_for_chart(df, x_field, y_field, agg)
+    if plot_df.empty:
+        return ""
+
+    plot_df = plot_df.sort_values("value", ascending=False)
+    if top_n > 0:
+        plot_df = plot_df.head(top_n)
+
+    if chart_type == "pie":
+        plot_df.set_index(x_field)["value"].plot(kind="pie", autopct="%1.1f%%")
+        plt.ylabel("")
+    elif chart_type == "line":
+        plt.plot(plot_df[x_field], plot_df["value"], marker="o")
+    elif chart_type == "area":
+        plt.fill_between(plot_df[x_field], plot_df["value"], alpha=0.4)
+        plt.plot(plot_df[x_field], plot_df["value"], color="#7a83ff")
+    else:
+        plt.bar(plot_df[x_field], plot_df["value"], color="#7a83ff")
+
+    plt.xticks(rotation=45, ha="right")
+    plt.xlabel(x_field)
+    plt.ylabel(plan.get("y_field") or "Value")
+    plt.title(plan.get("explanation", "Visualization"))
+    plt.tight_layout()
+    plt.savefig(path, bbox_inches="tight")
+    plt.close()
+    return path
+
+# Nodes
+def get_schema_node(state: AgentState):
+    schema = get_db_schema()
+    return {"schema": schema}
+
+def generate_sql_node(state: AgentState):
+    llm = get_llm()
+    if not llm:
+        return {"error": "LLM not configured"}
+    
+    template = """
+    You are a SQL expert. Convert the following natural language query into a SQL query for SQLite.
+    
+    Schema:
+    {schema}
+    
+    Recent conversation:
+    {history}
+
+    Current Query: {query}
+    
+    Return ONLY the SQL query, nothing else. Do not wrap in markdown code blocks.
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+    chain = prompt | llm | StrOutputParser()
+    
+    try:
+        sql_query = chain.invoke({
+            "schema": state["schema"],
+            "history": _format_history(state.get("history", [])),
+            "query": state["query"],
+        })
+        # Clean up sql if needed
+        sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
+        return {"sql_query": sql_query}
+    except Exception as e:
+        return {"error": str(e)}
+
+def execute_sql_node(state: AgentState):
+    if state.get("error"):
+        return state
+    
+    try:
+        df = pd.read_sql(state["sql_query"], engine)
+        return {"data": df.to_dict(orient="records")}
+    except Exception as e:
+        return {"error": f"SQL Execution failed: {str(e)}"}
+
+def generate_visualization_node(state: AgentState):
+    if state.get("error") or not state.get("data"):
+        return state
+    
+    df = pd.DataFrame(state["data"])
+    if df.empty:
+        return {"visualization_path": None, "visualization_summary": "No data to visualize."}
+
+    plan = _suggest_chart_plan(df, state.get("query", ""))
+
+    filename = f"chart_{uuid.uuid4()}.png"
+    path = os.path.join("backend", "static", filename)
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    image_path = _render_chart(path, df, plan)
+
+    if not image_path:
+        return {"visualization_path": None, "visualization_summary": plan.get("explanation")}
+
+    return {"visualization_path": image_path, "visualization_summary": plan.get("explanation")}
+
+
+def advanced_analytics_node(state: AgentState):
+    if state.get("error") or not state.get("data"):
+        return state
+
+    df = pd.DataFrame(state["data"])
+    if df.empty:
+        return {"trend_analysis": None, "anomaly_analysis": None, "forecast_analysis": None, "statistical_tests": None}
+
+    analytics = run_advanced_analytics(df)
+    return {
+        "trend_analysis": analytics.get("trend"),
+        "anomaly_analysis": analytics.get("anomaly"),
+        "forecast_analysis": analytics.get("forecast"),
+        "statistical_tests": analytics.get("statistical_tests"),
+    }
+
+def generate_insights_node(state: AgentState):
+    if state.get("error"):
+        return state
+    
+    llm = get_llm()
+    if not llm:
+        return {"insights": "LLM not configured"}
+    
+    data_summary = str(state["data"])[:2000] # Truncate if too long
+    
+    template = """
+    You are an analytics copilot. Use the latest query, the conversation history, the data sample, and the derived diagnostics (trends, anomalies, forecasts, and statistical tests) to provide incremental insights. If the user repeats a question, reference earlier answers instead of restating everything.
+
+    History:
+    {history}
+
+    Current Query: {query}
+    Data Sample: {data}
+    Trend Analysis: {trend}
+    Anomaly Analysis: {anomaly}
+    Forecast Analysis: {forecast}
+    Statistical Tests: {stats}
+
+    Provide 3-5 concise bullet insights plus a short summary paragraph. Mention forecasts and statistical significance when available.
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+    chain = prompt | llm | StrOutputParser()
+    
+    try:
+        insights = chain.invoke({
+            "query": state["query"],
+            "history": _format_history(state.get("history", [])),
+            "data": data_summary,
+            "trend": json.dumps(state.get("trend_analysis") or {}, ensure_ascii=False),
+            "anomaly": json.dumps(state.get("anomaly_analysis") or {}, ensure_ascii=False),
+            "forecast": json.dumps(state.get("forecast_analysis") or {}, ensure_ascii=False),
+            "stats": json.dumps(state.get("statistical_tests") or {}, ensure_ascii=False),
+        })
+        return {"insights": insights}
+    except Exception as e:
+        return {"insights": f"Failed to generate insights: {str(e)}"}
+
+def build_report_node(state: AgentState):
+    if state.get("error"):
+        return state
+    
+    filename = f"report_{uuid.uuid4()}.pdf"
+    path = os.path.join("backend", "static", filename)
+    
+    try:
+        generate_pdf_report(
+            report_path=path,
+            title="Autonomous Data Analyst Report",
+            query=state.get("query", ""),
+            sql_query=state.get("sql_query", ""),
+            insights=state.get("insights", "No insights generated."),
+            chart_image_path=state.get("visualization_path"),
+            chart_summary=state.get("visualization_summary"),
+            trend_analysis=state.get("trend_analysis"),
+            anomaly_analysis=state.get("anomaly_analysis"),
+            forecast_analysis=state.get("forecast_analysis"),
+            statistical_tests=state.get("statistical_tests"),
+            data_sample=state.get("data"),
+        )
+        return {"report_path": path}
+    except Exception as e:
+        return {"error": f"Report generation failed: {str(e)}"}
+
+def create_agent_graph():
+    workflow = StateGraph(AgentState)
+    
+    # Add nodes
+    workflow.add_node("get_schema", get_schema_node)
+    workflow.add_node("generate_sql", generate_sql_node)
+    workflow.add_node("execute_sql", execute_sql_node)
+    workflow.add_node("visualize", generate_visualization_node)
+    workflow.add_node("advanced_analytics", advanced_analytics_node)
+    workflow.add_node("generate_insights", generate_insights_node)
+    workflow.add_node("build_report", build_report_node)
+    
+    # Define edges
+    workflow.set_entry_point("get_schema")
+    workflow.add_edge("get_schema", "generate_sql")
+    workflow.add_edge("generate_sql", "execute_sql")
+    workflow.add_edge("execute_sql", "visualize")
+    workflow.add_edge("visualize", "advanced_analytics")
+    workflow.add_edge("advanced_analytics", "generate_insights")
+    workflow.add_edge("generate_insights", "build_report")
+    workflow.add_edge("build_report", END)
+    
+    return workflow.compile()

backend/app/api/routes.py

@@ -0,0 +1,152 @@
+from collections import defaultdict
+from decimal import Decimal
+import math
+from typing import Optional, List, Dict, Any
+
+from fastapi import APIRouter, HTTPException, UploadFile, File, Form
+from fastapi.encoders import jsonable_encoder
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+
+from app.agents.graph import create_agent_graph
+from app.services.csv_loader import ingest_csv_dataset, list_dataset_tables
+
+try:
+    import numpy as np  # type: ignore
+except Exception:  # pragma: no cover - numpy optional at runtime
+    np = None
+
+router = APIRouter()
+agent_graph = create_agent_graph()
+session_histories: Dict[str, List[dict]] = defaultdict(list)
+
+
+def _sanitize_for_json(value: Any):
+    """Recursively coerce values so FastAPI's JSON encoding never sees NaN/Inf."""
+    if value is None:
+        return None
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, (int, str)):
+        return value
+    if isinstance(value, float):
+        return value if math.isfinite(value) else None
+    if isinstance(value, Decimal):
+        as_float = float(value)
+        return as_float if math.isfinite(as_float) else None
+    if np is not None:
+        if isinstance(value, (np.floating, np.integer)):
+            coerced = value.item()
+            if isinstance(coerced, float) and not math.isfinite(coerced):
+                return None
+            return coerced
+        if isinstance(value, np.ndarray):
+            return [_sanitize_for_json(item) for item in value.tolist()]
+    if isinstance(value, dict):
+        return {key: _sanitize_for_json(val) for key, val in value.items()}
+    if isinstance(value, (list, tuple, set)):
+        return [_sanitize_for_json(item) for item in value]
+    # Fallback to string representation for unsupported objects (e.g., Timestamp)
+    return str(value)
+
+
+def _safe_json(payload: Any) -> JSONResponse:
+    sanitized = _sanitize_for_json(payload)
+    encoded = jsonable_encoder(
+        sanitized,
+        custom_encoder={
+            float: lambda v: v if math.isfinite(v) else None,
+            Decimal: lambda v: float(v) if math.isfinite(float(v)) else None,
+        },
+    )
+    return JSONResponse(content=encoded)
+
+class QueryRequest(BaseModel):
+    query: str
+    session_id: Optional[str] = "default"
+
+
+class SessionResetRequest(BaseModel):
+    session_id: str
+
+@router.post("/analyze")
+async def analyze_data(request: QueryRequest):
+    try:
+        history = list(session_histories.get(request.session_id, []))
+        initial_state = {"query": request.query, "history": history}
+        result = agent_graph.invoke(initial_state)
+        
+        # Construct response
+        response = {
+            "status": "success",
+            "query": result.get("query"),
+            "sql_query": result.get("sql_query"),
+            "data": result.get("data"),
+            "insights": result.get("insights"),
+            "visualization_url": None,
+            "visualization_summary": result.get("visualization_summary"),
+            "trend_analysis": result.get("trend_analysis"),
+            "anomaly_analysis": result.get("anomaly_analysis"),
+            "forecast_analysis": result.get("forecast_analysis"),
+            "statistical_tests": result.get("statistical_tests"),
+            "report_url": None,
+            "error": result.get("error")
+        }
+        
+        if result.get("visualization_path"):
+            # Convert path to URL (assuming static mount)
+            filename = result["visualization_path"].split("\\")[-1].split("/")[-1]
+            response["visualization_url"] = f"/static/{filename}"
+            
+        if result.get("report_path"):
+            filename = result["report_path"].split("\\")[-1].split("/")[-1]
+            response["report_url"] = f"/static/{filename}"
+
+        if not result.get("error"):
+            session_histories[request.session_id].append(
+                {
+                    "question": request.query,
+                    "insights": result.get("insights", ""),
+                    "sql": result.get("sql_query", ""),
+                }
+            )
+            # limit memory
+            session_histories[request.session_id] = session_histories[request.session_id][-10:]
+            
+        return _safe_json(response)
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+
+@router.post("/upload-csv")
+async def upload_csv_dataset(
+    file: UploadFile = File(...),
+    table_name: str = Form("sales")
+):
+    try:
+        payload = ingest_csv_dataset(file, table_name)
+        return _safe_json({"status": "success", **payload})
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc))
+
+
+@router.get("/datasets")
+async def get_dataset_catalog():
+    try:
+        catalog = list_dataset_tables()
+        return _safe_json({"tables": catalog})
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc))
+
+
+@router.post("/session/reset")
+async def reset_session(req: SessionResetRequest):
+    session_histories.pop(req.session_id, None)
+    return _safe_json({"status": "cleared", "session_id": req.session_id})

backend/app/core/config.py

@@ -0,0 +1,11 @@
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class Settings:
+    PROJECT_NAME: str = "Autonomous Data Analyst Agent"
+    GROQ_API_KEY: str = os.getenv("GROQ_API_KEY", "")
+    DATABASE_URL: str = os.getenv("DATABASE_URL", "sqlite:///./test.db")
+
+settings = Settings()

backend/app/db/database.py

@@ -0,0 +1,85 @@
+from datetime import datetime, timedelta
+import random
+
+from sqlalchemy import create_engine, MetaData, inspect, text
+
+from app.core.config import settings
+
+engine = create_engine(settings.DATABASE_URL)
+metadata = MetaData()
+
+
+def ensure_sales_dataset():
+    inspector = inspect(engine)
+    if inspector.has_table("sales"):
+        return
+
+    with engine.begin() as conn:
+        conn.exec_driver_sql(
+            """
+            CREATE TABLE IF NOT EXISTS sales (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                date DATE,
+                product_category TEXT,
+                product_name TEXT,
+                quantity INTEGER,
+                unit_price REAL,
+                total_amount REAL,
+                region TEXT
+            )
+            """
+        )
+
+        categories = ["Electronics", "Clothing", "Home", "Books"]
+        products = {
+            "Electronics": ["Laptop", "Smartphone", "Headphones", "Monitor"],
+            "Clothing": ["T-Shirt", "Jeans", "Jacket", "Sneakers"],
+            "Home": ["Sofa", "Table", "Lamp", "Rug"],
+            "Books": ["Fiction", "Non-Fiction", "Sci-Fi", "Biography"],
+        }
+        regions = ["North", "South", "East", "West"]
+
+        rows = []
+        start_date = datetime(2023, 1, 1)
+        for _ in range(365):
+            date = start_date + timedelta(days=random.randint(0, 364))
+            category = random.choice(categories)
+            product = random.choice(products[category])
+            quantity = random.randint(1, 10)
+            unit_price = round(random.uniform(10.0, 1200.0), 2)
+            total_amount = round(quantity * unit_price, 2)
+            region = random.choice(regions)
+
+            rows.append(
+                {
+                    "date": date.strftime("%Y-%m-%d"),
+                    "product_category": category,
+                    "product_name": product,
+                    "quantity": quantity,
+                    "unit_price": unit_price,
+                    "total_amount": total_amount,
+                    "region": region,
+                }
+            )
+
+        conn.execute(
+            text(
+                """
+                INSERT INTO sales (date, product_category, product_name, quantity, unit_price, total_amount, region)
+                VALUES (:date, :product_category, :product_name, :quantity, :unit_price, :total_amount, :region)
+                """
+            ),
+            rows,
+        )
+
+
+ensure_sales_dataset()
+
+
+def get_db_schema():
+    metadata.reflect(bind=engine)
+    schema_info = []
+    for table in metadata.tables.values():
+        columns = [f"{col.name} ({col.type})" for col in table.columns]
+        schema_info.append(f"Table: {table.name}\nColumns: {', '.join(columns)}")
+    return "\n\n".join(schema_info)

backend/app/main.py

@@ -0,0 +1,31 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from app.api import routes
+import uvicorn
+import os
+
+app = FastAPI(title="Autonomous Data Analyst Agent")
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with specific frontend origin
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static files
+static_dir = os.path.join("backend", "static")
+os.makedirs(static_dir, exist_ok=True)
+app.mount("/static", StaticFiles(directory=static_dir), name="static")
+
+app.include_router(routes.router, prefix="/api")
+
+@app.get("/")
+def read_root():
+    return {"message": "Autonomous Data Analyst Agent API is running"}
+
+if __name__ == "__main__":
+    uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)

backend/app/services/analytics.py

@@ -0,0 +1,332 @@
+import math
+from typing import Dict, List, Optional, Tuple
+import warnings
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+
+try:
+    from statsmodels.tsa.holtwinters import ExponentialSmoothing
+    from statsmodels.tsa.stattools import adfuller
+    HAS_STATSMODELS = True
+except ImportError:
+    HAS_STATSMODELS = False
+
+DATE_HINTS = ("date", "time", "month", "year", "period")
+METRIC_HINTS = ("sale", "amount", "revenue", "profit", "price", "total")
+MAX_POINTS = 60
+
+
+def _detect_datetime_column(df: pd.DataFrame) -> Optional[str]:
+    # Prefer columns already datetime typed
+    for col in df.columns:
+        if pd.api.types.is_datetime64_any_dtype(df[col]):
+            return col
+    # Fallback to columns whose names hint at date/time
+    for col in df.columns:
+        low = col.lower()
+        if any(hint in low for hint in DATE_HINTS):
+            try:
+                pd.to_datetime(df[col])
+                return col
+            except Exception:
+                continue
+    return None
+
+
+def _detect_metric_column(df: pd.DataFrame) -> Optional[str]:
+    numeric_cols = [c for c in df.columns if pd.api.types.is_numeric_dtype(df[c])]
+    if not numeric_cols:
+        return None
+    for col in numeric_cols:
+        low = col.lower()
+        if any(hint in low for hint in METRIC_HINTS):
+            return col
+    return numeric_cols[0]
+
+
+def _build_time_series(df: pd.DataFrame) -> Optional[pd.Series]:
+    if df is None or df.empty:
+        return None
+
+    date_col = _detect_datetime_column(df)
+    metric_col = _detect_metric_column(df)
+    if not date_col or not metric_col:
+        return None
+
+    ts = df[[date_col, metric_col]].copy()
+    ts[date_col] = pd.to_datetime(ts[date_col], errors="coerce")
+    ts = ts.dropna(subset=[date_col, metric_col])
+    if ts.empty:
+        return None
+
+    # Aggregate by month for smoother signals
+    ts["period"] = ts[date_col].dt.to_period("M").dt.to_timestamp()
+    grouped = ts.groupby("period")[metric_col].sum().sort_index()
+    if len(grouped) > MAX_POINTS:
+        grouped = grouped[-MAX_POINTS:]
+    return grouped
+
+
+def _linear_trend(series: pd.Series) -> Optional[Dict[str, object]]:
+    if series is None or len(series) < 3:
+        return None
+
+    x = np.arange(len(series))
+    y = series.values.astype(float)
+    
+    # Linear regression with confidence intervals
+    slope, intercept = np.polyfit(x, y, 1)
+    y_pred = slope * x + intercept
+    
+    # Calculate standard error and confidence intervals
+    residuals = y - y_pred
+    n = len(y)
+    degrees_freedom = n - 2
+    residual_std_error = np.sqrt(np.sum(residuals**2) / degrees_freedom) if degrees_freedom > 0 else 0
+    
+    # Standard error of slope
+    x_mean = np.mean(x)
+    se_slope = residual_std_error / np.sqrt(np.sum((x - x_mean)**2)) if np.sum((x - x_mean)**2) > 0 else 0
+    
+    # 95% confidence interval for slope
+    t_val = stats.t.ppf(0.975, degrees_freedom) if degrees_freedom > 0 else 1.96
+    slope_ci_lower = slope - t_val * se_slope
+    slope_ci_upper = slope + t_val * se_slope
+    
+    # Prediction intervals for the trend line
+    prediction_intervals = []
+    for i in range(len(x)):
+        se_pred = residual_std_error * np.sqrt(1 + 1/n + (x[i] - x_mean)**2 / np.sum((x - x_mean)**2))
+        pi_lower = y_pred[i] - t_val * se_pred
+        pi_upper = y_pred[i] + t_val * se_pred
+        prediction_intervals.append({
+            "lower": float(pi_lower),
+            "upper": float(pi_upper)
+        })
+    
+    start_val = float(y[0])
+    end_val = float(y[-1])
+    change_pct = None
+    if not math.isclose(start_val, 0.0):
+        change_pct = ((end_val - start_val) / abs(start_val)) * 100
+
+    direction = "flat"
+    if slope > 0.02 * np.mean(y):
+        direction = "upward"
+    elif slope < -0.02 * np.mean(y):
+        direction = "downward"
+
+    summary = f"{direction.capitalize()} trend detected" if direction != "flat" else "Minimal trend detected"
+    if change_pct is not None:
+        summary += f" ({change_pct:+.1f}% over period)"
+    summary += f" with 95% confidence [slope: {slope_ci_lower:.2f} to {slope_ci_upper:.2f}]"
+
+    return {
+        "summary": summary,
+        "start": start_val,
+        "end": end_val,
+        "slope": float(slope),
+        "slope_ci_lower": float(slope_ci_lower),
+        "slope_ci_upper": float(slope_ci_upper),
+        "std_error": float(residual_std_error),
+        "r_squared": float(1 - np.sum(residuals**2) / np.sum((y - np.mean(y))**2)) if np.sum((y - np.mean(y))**2) > 0 else 0,
+        "change_pct": change_pct,
+        "points": [
+            {"period": period.strftime("%Y-%m"), "value": float(value)}
+            for period, value in series.items()
+        ],
+        "prediction_intervals": prediction_intervals,
+    }
+
+
+def _anomaly_scan(series: pd.Series) -> Optional[Dict[str, object]]:
+    if series is None or len(series) < 4:
+        return None
+
+    values = series.values.astype(float)
+    mean = float(np.mean(values))
+    std = float(np.std(values))
+    if math.isclose(std, 0.0):
+        return None
+
+    z_scores = (values - mean) / std
+    anomalies: List[Dict[str, object]] = []
+    for idx, z in enumerate(z_scores):
+        if abs(z) >= 2.0:
+            period = series.index[idx]
+            anomalies.append(
+                {
+                    "period": period.strftime("%Y-%m"),
+                    "value": float(values[idx]),
+                    "z_score": float(z),
+                }
+            )
+
+    if not anomalies:
+        return None
+
+    top = sorted(anomalies, key=lambda a: abs(a["z_score"]), reverse=True)[:3]
+    summary = "Anomalies detected at " + ", ".join(
+        [f"{a['period']} (z={a['z_score']:+.1f})" for a in top]
+    )
+
+    return {"summary": summary, "anomalies": anomalies, "mean": mean, "std": std}
+
+
+def run_advanced_analytics(df: pd.DataFrame) -> Dict[str, Optional[Dict[str, object]]]:
+    series = _build_time_series(df)
+    trend = _linear_trend(series)
+    anomaly = _anomaly_scan(series)
+    forecast = _forecast_next_periods(series)
+    statistical_tests = _run_statistical_tests(series)
+    return {
+        "trend": trend,
+        "anomaly": anomaly,
+        "forecast": forecast,
+        "statistical_tests": statistical_tests,
+    }
+
+
+def _forecast_next_periods(series: pd.Series, periods: int = 3) -> Optional[Dict[str, object]]:
+    """Generate forecasts using exponential smoothing with prediction intervals."""
+    if series is None or len(series) < 6:
+        return None
+    
+    if not HAS_STATSMODELS:
+        return {
+            "summary": "Forecasting unavailable (statsmodels not installed)",
+            "forecasts": [],
+        }
+    
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            
+            # Try Holt's exponential smoothing (trend method)
+            model = ExponentialSmoothing(
+                series.values,
+                trend='add',
+                seasonal=None,
+                initialization_method="estimated"
+            )
+            fitted = model.fit(optimized=True, remove_bias=False)
+            
+            # Generate forecasts
+            forecast_values = fitted.forecast(steps=periods)
+            
+            # Calculate prediction intervals using residual std
+            residuals = series.values - fitted.fittedvalues
+            residual_std = np.std(residuals)
+            
+            # Generate future periods
+            last_period = series.index[-1]
+            freq = pd.infer_freq(series.index) or 'MS'
+            future_periods = pd.date_range(start=last_period, periods=periods + 1, freq=freq)[1:]
+            
+            forecasts = []
+            for i, (period, value) in enumerate(zip(future_periods, forecast_values)):
+                # Prediction interval widens with forecast horizon
+                interval_width = residual_std * np.sqrt(i + 1) * 1.96
+                forecasts.append({
+                    "period": period.strftime("%Y-%m"),
+                    "value": float(value),
+                    "lower_bound": float(value - interval_width),
+                    "upper_bound": float(value + interval_width),
+                })
+            
+            summary = f"Forecast for next {periods} periods using exponential smoothing"
+            if len(forecasts) > 0:
+                first_forecast = forecasts[0]["value"]
+                last_actual = float(series.values[-1])
+                change = ((first_forecast - last_actual) / abs(last_actual)) * 100 if last_actual != 0 else 0
+                summary += f" (next period: {first_forecast:.1f}, {change:+.1f}% vs current)"
+            
+            return {
+                "summary": summary,
+                "method": "Exponential Smoothing (Holt)",
+                "forecasts": forecasts,
+                "model_params": {
+                    "alpha": float(fitted.params.get('smoothing_level', 0)),
+                    "beta": float(fitted.params.get('smoothing_trend', 0)) if fitted.params.get('smoothing_trend') else None,
+                }
+            }
+            
+    except Exception as e:
+        return {
+            "summary": f"Forecasting failed: {str(e)[:100]}",
+            "forecasts": [],
+        }
+
+
+def _run_statistical_tests(series: pd.Series) -> Optional[Dict[str, object]]:
+    """Run statistical comparison tests on time series data."""
+    if series is None or len(series) < 6:
+        return None
+    
+    results = {}
+    
+    # Split into two halves for comparison (e.g., first half vs second half)
+    mid = len(series) // 2
+    first_half = series.values[:mid]
+    second_half = series.values[mid:]
+    
+    # T-test: Are the two periods significantly different?
+    try:
+        t_stat, p_value = stats.ttest_ind(first_half, second_half)
+        results["period_comparison"] = {
+            "test": "Independent t-test",
+            "comparison": "First half vs Second half",
+            "t_statistic": float(t_stat),
+            "p_value": float(p_value),
+            "significant": p_value < 0.05,
+            "summary": f"{'Significant' if p_value < 0.05 else 'No significant'} difference between periods (p={p_value:.4f})"
+        }
+    except Exception:
+        pass
+    
+    # Test for stationarity (Augmented Dickey-Fuller test)
+    if HAS_STATSMODELS:
+        try:
+            adf_result = adfuller(series.values, autolag='AIC')
+            results["stationarity"] = {
+                "test": "Augmented Dickey-Fuller",
+                "adf_statistic": float(adf_result[0]),
+                "p_value": float(adf_result[1]),
+                "is_stationary": adf_result[1] < 0.05,
+                "summary": f"Series is {'stationary' if adf_result[1] < 0.05 else 'non-stationary'} (p={adf_result[1]:.4f})"
+            }
+        except Exception:
+            pass
+    
+    # Quartile-based comparison (ANOVA-like for segments)
+    try:
+        quartiles = pd.qcut(series.index.to_series(), q=4, labels=False, duplicates='drop')
+        groups = [series.values[quartiles == i] for i in range(4) if len(series.values[quartiles == i]) > 0]
+        
+        if len(groups) >= 2:
+            f_stat, p_value = stats.f_oneway(*groups)
+            results["quarterly_variance"] = {
+                "test": "One-way ANOVA",
+                "comparison": "Across quarters",
+                "f_statistic": float(f_stat),
+                "p_value": float(p_value),
+                "significant": p_value < 0.05,
+                "summary": f"{'Significant' if p_value < 0.05 else 'No significant'} variance across quarters (p={p_value:.4f})"
+            }
+    except Exception:
+        pass
+    
+    if not results:
+        return None
+    
+    # Overall summary
+    sig_tests = [v["summary"] for v in results.values() if "significant" in v.get("summary", "").lower()]
+    overall = f"{len(sig_tests)} significant finding(s): " + "; ".join(sig_tests[:2]) if sig_tests else "No significant patterns detected"
+    
+    return {
+        "summary": overall,
+        "tests": results,
+    }
+

backend/app/services/csv_loader.py

@@ -0,0 +1,91 @@
+import io
+import re
+from typing import Any, Dict, List
+
+import pandas as pd
+from fastapi import UploadFile
+from sqlalchemy import inspect, text
+
+from app.db.database import engine
+
+SUPPORTED_ENCODINGS = ("utf-8", "latin1", "cp1252")
+
+SAFE_TABLE_PATTERN = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
+
+
+def _validate_table_name(name: str) -> str:
+    if not SAFE_TABLE_PATTERN.match(name):
+        raise ValueError("Table name must start with a letter/underscore and contain only alphanumerics or underscores.")
+    return name
+
+
+def ingest_csv_dataset(
+    upload_file: UploadFile,
+    table_name: str = "sales",
+) -> Dict[str, Any]:
+    """Load an uploaded CSV file into the configured database table."""
+    raw_bytes = upload_file.file.read()
+    upload_file.file.seek(0)
+
+    dataframe = None
+    chosen_encoding = None
+    errors = []
+
+    for encoding in SUPPORTED_ENCODINGS:
+        buffer = io.BytesIO(raw_bytes)
+        try:
+            dataframe = pd.read_csv(buffer, encoding=encoding)
+            chosen_encoding = encoding
+            break
+        except UnicodeDecodeError as exc:
+            errors.append(f"{encoding}: {exc}")
+            continue
+        except Exception as exc:
+            raise ValueError(f"Unable to parse CSV: {exc}")
+
+    if dataframe is None:
+        raise ValueError(
+            "Unable to decode CSV. Please upload UTF-8 or Latin-1 encoded files." +
+            (f" Details: {'; '.join(errors)}" if errors else "")
+        )
+
+    if dataframe.empty:
+        raise ValueError("Uploaded CSV is empty.")
+
+    safe_table_name = _validate_table_name(table_name.strip() or "sales")
+    dataframe.to_sql(safe_table_name, engine, if_exists="replace", index=False)
+
+    # Convert first 5 rows to dict for preview
+    preview_rows = dataframe.head(5).to_dict(orient="records")
+
+    return {
+        "table": safe_table_name,
+        "rows": len(dataframe),
+        "columns": list(dataframe.columns),
+        "encoding": chosen_encoding,
+        "preview": preview_rows
+    }
+
+
+def list_dataset_tables(limit: int = 15) -> List[Dict[str, Any]]:
+    inspector = inspect(engine)
+    tables = [t for t in inspector.get_table_names() if not t.startswith("sqlite_")][:limit]
+    catalog: List[Dict[str, Any]] = []
+
+    with engine.connect() as conn:
+        for table_name in tables:
+            column_info = inspector.get_columns(table_name)
+            try:
+                result = conn.execute(text(f"SELECT COUNT(*) as cnt FROM {table_name}"))
+                row_count = result.scalar() or 0
+            except Exception:
+                row_count = None
+
+            catalog.append(
+                {
+                    "table": table_name,
+                    "rows": int(row_count) if row_count is not None else None,
+                    "columns": [col.get("name") for col in column_info],
+                }
+            )
+    return catalog

backend/app/services/pdf_generator.py

@@ -0,0 +1,451 @@
+import os
+from datetime import datetime
+from typing import Optional
+
+from reportlab.lib import colors
+from reportlab.lib.pagesizes import letter
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.units import inch
+from reportlab.platypus import (
+    SimpleDocTemplate,
+    Paragraph,
+    Spacer,
+    Table,
+    TableStyle,
+    Image,
+    Preformatted,
+    HRFlowable,
+    PageBreak,
+)
+
+
+def _build_styles():
+    styles = getSampleStyleSheet()
+    styles.add(
+        ParagraphStyle(
+            name="HeroTitle",
+            parent=styles["Title"],
+            fontName="Helvetica-Bold",
+            fontSize=20,
+            leading=26,
+            textColor=colors.HexColor("#111827"),
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="SectionHeader",
+            parent=styles["Heading2"],
+            fontName="Helvetica-Bold",
+            fontSize=14,
+            spaceAfter=6,
+            textColor=colors.HexColor("#111827"),
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="ReportBody",
+            parent=styles["BodyText"],
+            fontSize=11,
+            leading=16,
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="MetaLabel",
+            parent=styles["BodyText"],
+            fontSize=8,
+            textColor=colors.HexColor("#6b7280"),
+            leading=10,
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="QueryText",
+            parent=styles["BodyText"],
+            fontSize=12,
+            leading=16,
+            textColor=colors.HexColor("#111827"),
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="MetricValue",
+            parent=styles["BodyText"],
+            fontName="Helvetica-Bold",
+            fontSize=13,
+            textColor=colors.HexColor("#111827"),
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="MetricLabel",
+            parent=styles["BodyText"],
+            fontSize=9,
+            textColor=colors.HexColor("#6b7280"),
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="CaptionSmall",
+            parent=styles["BodyText"],
+            fontSize=9,
+            textColor=colors.HexColor("#6b7280"),
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="InsightBullet",
+            parent=styles["BodyText"],
+            leftIndent=14,
+            bulletIndent=6,
+            bulletFontName="Helvetica",
+            bulletFontSize=10,
+            leading=16,
+        )
+    )
+    styles.add(
+        ParagraphStyle(
+            name="CodeBlock",
+            parent=styles["BodyText"],
+            fontName="Courier",
+            fontSize=9,
+            leading=14,
+            backColor=colors.whitesmoke,
+        )
+    )
+    return styles
+
+
+def _format_insights(insights: str, styles) -> list:
+    blocks = []
+    lines = [line.strip() for line in (insights or "").splitlines() if line.strip()]
+    if not lines:
+        return [Paragraph("No insights provided.", styles["ReportBody"])]
+
+    for line in lines:
+        if line[0] in {"-", "*", "•"}:
+            text = line.lstrip("-•* ").strip()
+            blocks.append(Paragraph(text, styles["InsightBullet"], bulletText="•"))
+        else:
+            blocks.append(Paragraph(line, styles["ReportBody"]))
+        blocks.append(Spacer(1, 4))
+    return blocks
+
+
+def _build_data_table(data_sample):
+    if not data_sample:
+        return None
+
+    columns = list(data_sample[0].keys())
+    rows = [columns]
+    for row in data_sample[:10]:
+        rows.append([str(row.get(col, "")) for col in columns])
+
+    table = Table(rows, repeatRows=1)
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#111827")),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("FONTSIZE", (0, 0), (-1, 0), 10),
+                ("BOTTOMPADDING", (0, 0), (-1, 0), 6),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.HexColor("#f9fafb")),
+                ("TEXTCOLOR", (0, 1), (-1, -1), colors.HexColor("#111827")),
+                ("GRID", (0, 0), (-1, -1), 0.25, colors.HexColor("#e5e7eb")),
+            ]
+        )
+    )
+    return table
+
+
+def _build_query_callout(query: str, styles):
+    display = query or "No question provided"
+    content = [
+        [Paragraph("BUSINESS QUESTION", styles["MetaLabel"])],
+        [Paragraph(display, styles["QueryText"])]
+    ]
+    table = Table(content, colWidths=[6.5 * inch])
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, -1), colors.HexColor("#f3f4ff")),
+                ("BOX", (0, 0), (-1, -1), 0.6, colors.HexColor("#c7d2fe")),
+                ("INNERPADDING", (0, 0), (-1, -1), 8),
+            ]
+        )
+    )
+    return table
+
+
+def _build_metric_cards(stats: dict, styles):
+    if not stats:
+        return None
+
+    data = []
+    row = []
+    for label, value in stats.items():
+        cell = [
+            Paragraph(label, styles["MetricLabel"]),
+            Paragraph(value, styles["MetricValue"]),
+        ]
+        row.append(cell)
+
+    data.append(row)
+
+    col_width = (6.5 * inch) / len(stats)
+    table = Table(data, colWidths=[col_width] * len(stats))
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, -1), colors.HexColor("#f9fafb")),
+                ("BOX", (0, 0), (-1, -1), 0.5, colors.HexColor("#e5e7eb")),
+                ("INNERPADDING", (0, 0), (-1, -1), 8),
+                ("VALIGN", (0, 0), (-1, -1), "TOP"),
+            ]
+        )
+    )
+    return table
+
+
+def _format_value(value):
+    if value is None:
+        return "—"
+    if isinstance(value, float):
+        return f"{value:,.2f}"
+    if isinstance(value, int):
+        return f"{value:,}"
+    return str(value)
+
+
+def _render_trend_section(trend_analysis, styles):
+    blocks = [Paragraph("Trend Diagnostics", styles["SectionHeader"])]
+    if not trend_analysis:
+        blocks.append(Paragraph("Not enough chronological data to compute a trend.", styles["ReportBody"]))
+        return blocks
+
+    blocks.append(Paragraph(trend_analysis.get("summary", ""), styles["ReportBody"]))
+    stats = [
+        ["Starting value", _format_value(trend_analysis.get("start"))],
+        ["Ending value", _format_value(trend_analysis.get("end"))],
+        ["Slope", _format_value(trend_analysis.get("slope"))],
+    ]
+    if trend_analysis.get("change_pct") is not None:
+        stats.append(["% Change", f"{trend_analysis['change_pct']:+.1f}%"])
+
+    table = Table(stats, colWidths=[2.3 * inch, 4 * inch])
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, -1), colors.HexColor("#f9fafb")),
+                ("BOX", (0, 0), (-1, -1), 0.4, colors.HexColor("#e5e7eb")),
+                ("INNERPADDING", (0, 0), (-1, -1), 6),
+                ("FONTNAME", (0, 0), (-1, -1), "Helvetica"),
+            ]
+        )
+    )
+    blocks.extend([Spacer(1, 6), table])
+    return blocks
+
+
+def _render_anomaly_section(anomaly_analysis, styles):
+    blocks = [Paragraph("Anomaly Highlights", styles["SectionHeader"])]
+    if not anomaly_analysis:
+        blocks.append(Paragraph("No statistical anomalies detected across the observed period.", styles["ReportBody"]))
+        return blocks
+
+    blocks.append(Paragraph(anomaly_analysis.get("summary", ""), styles["ReportBody"]))
+    table_rows = [["Period", "Value", "z-score"]]
+    for entry in anomaly_analysis.get("anomalies", [])[:8]:
+        table_rows.append([
+            entry.get("period", "?"),
+            _format_value(entry.get("value")),
+            f"{entry.get('z_score', 0):+.2f}",
+        ])
+    table = Table(table_rows, repeatRows=1, colWidths=[2 * inch, 2.5 * inch, 1.5 * inch])
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#111827")),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.HexColor("#f3f4f6")),
+                ("GRID", (0, 0), (-1, -1), 0.25, colors.HexColor("#d1d5db")),
+            ]
+        )
+    )
+    blocks.extend([Spacer(1, 6), table])
+    return blocks
+
+
+def _render_forecast_section(forecast_analysis, styles):
+    blocks = [Paragraph("Forecast", styles["SectionHeader"])]
+    if not forecast_analysis or not forecast_analysis.get("forecasts"):
+        blocks.append(Paragraph("Forecasting unavailable for this dataset.", styles["ReportBody"]))
+        return blocks
+
+    blocks.append(Paragraph(forecast_analysis.get("summary", ""), styles["ReportBody"]))
+    
+    method = forecast_analysis.get("method", "N/A")
+    blocks.append(Spacer(1, 4))
+    blocks.append(Paragraph(f"<i>Method: {method}</i>", styles["CaptionSmall"]))
+    
+    table_rows = [["Period", "Forecast", "Lower Bound", "Upper Bound"]]
+    for entry in forecast_analysis.get("forecasts", []):
+        table_rows.append([
+            entry.get("period", "?"),
+            _format_value(entry.get("value")),
+            _format_value(entry.get("lower_bound")),
+            _format_value(entry.get("upper_bound")),
+        ])
+    
+    table = Table(table_rows, repeatRows=1, colWidths=[1.5 * inch, 1.5 * inch, 1.5 * inch, 1.5 * inch])
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#111827")),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("BACKGROUND", (0, 1), (-1, -1), colors.HexColor("#f3f4f6")),
+                ("GRID", (0, 0), (-1, -1), 0.25, colors.HexColor("#d1d5db")),
+            ]
+        )
+    )
+    blocks.extend([Spacer(1, 6), table])
+    return blocks
+
+
+def _render_statistical_tests_section(statistical_tests, styles):
+    blocks = [Paragraph("Statistical Tests", styles["SectionHeader"])]
+    if not statistical_tests or not statistical_tests.get("tests"):
+        blocks.append(Paragraph("No statistical tests performed.", styles["ReportBody"]))
+        return blocks
+
+    blocks.append(Paragraph(statistical_tests.get("summary", ""), styles["ReportBody"]))
+    blocks.append(Spacer(1, 8))
+    
+    for test_name, test_data in statistical_tests.get("tests", {}).items():
+        test_title = test_data.get("test", test_name)
+        blocks.append(Paragraph(f"<b>{test_title}</b>", styles["ReportBody"]))
+        blocks.append(Spacer(1, 2))
+        
+        details = []
+        if "comparison" in test_data:
+            details.append(f"Comparison: {test_data['comparison']}")
+        if "p_value" in test_data:
+            p_val = test_data["p_value"]
+            sig = "Yes" if test_data.get("significant", False) else "No"
+            details.append(f"p-value: {p_val:.4f} (Significant: {sig})")
+        if "summary" in test_data:
+            details.append(test_data["summary"])
+        
+        for detail in details:
+            blocks.append(Paragraph(f"• {detail}", styles["ReportBody"]))
+        
+        blocks.append(Spacer(1, 6))
+    
+    return blocks
+
+
+def generate_pdf_report(
+    report_path: str,
+    title: str,
+    query: str,
+    sql_query: str,
+    insights: str,
+    chart_image_path: str = None,
+    chart_summary: str = None,
+    trend_analysis: Optional[dict] = None,
+    anomaly_analysis: Optional[dict] = None,
+    forecast_analysis: Optional[dict] = None,
+    statistical_tests: Optional[dict] = None,
+    data_sample=None,
+):
+    styles = _build_styles()
+    doc = SimpleDocTemplate(
+        report_path,
+        pagesize=letter,
+        leftMargin=0.75 * inch,
+        rightMargin=0.75 * inch,
+        topMargin=0.75 * inch,
+        bottomMargin=0.75 * inch,
+    )
+
+    story = []
+
+    generated_at = datetime.utcnow().strftime("%B %d, %Y %H:%M UTC")
+    row_count = len(data_sample or [])
+    column_count = len(data_sample[0]) if data_sample else 0
+
+    story.append(Paragraph(title, styles["HeroTitle"]))
+    story.append(Paragraph("Autonomous insight report", styles["CaptionSmall"]))
+    story.append(Spacer(1, 10))
+
+    story.append(_build_query_callout(query, styles))
+    story.append(Spacer(1, 12))
+
+    metrics = {
+        "Rows Returned": f"{row_count:,}" if row_count else "0",
+        "Columns": str(column_count),
+        "Generated": generated_at,
+    }
+    metric_table = _build_metric_cards(metrics, styles)
+    if metric_table:
+        story.append(metric_table)
+        story.append(Spacer(1, 18))
+
+    story.append(Paragraph("This auto-generated briefing captures the freshest SQL results, advanced analytics, and executive visuals from InsightPilot.", styles["ReportBody"]))
+    story.append(Spacer(1, 14))
+    story.append(PageBreak())
+
+    story.append(HRFlowable(width="100%", thickness=0.6, color=colors.HexColor("#e5e7eb")))
+    story.append(Spacer(1, 14))
+
+    story.append(Paragraph("SQL Used", styles["SectionHeader"]))
+    story.append(
+        Preformatted(sql_query.strip() or "No SQL generated", styles["CodeBlock"], maxLineLength=80)
+    )
+    story.append(Spacer(1, 12))
+
+    story.append(Paragraph("Insights", styles["SectionHeader"]))
+    for block in _format_insights(insights, styles):
+        story.append(block)
+    story.append(Spacer(1, 10))
+
+    table = _build_data_table(data_sample or [])
+    if table:
+        story.append(Paragraph("Data Preview", styles["SectionHeader"]))
+        story.append(table)
+        story.append(Spacer(1, 12))
+
+    for block in _render_trend_section(trend_analysis, styles):
+        story.append(block)
+    story.append(Spacer(1, 12))
+
+    for block in _render_anomaly_section(anomaly_analysis, styles):
+        story.append(block)
+    story.append(Spacer(1, 12))
+
+    for block in _render_forecast_section(forecast_analysis, styles):
+        story.append(block)
+    story.append(Spacer(1, 12))
+
+    for block in _render_statistical_tests_section(statistical_tests, styles):
+        story.append(block)
+    story.append(Spacer(1, 12))
+
+    if chart_image_path and os.path.exists(chart_image_path):
+        story.append(Paragraph("Visualization", styles["SectionHeader"]))
+        img = Image(chart_image_path)
+        img._restrictSize(6.5 * inch, 4.5 * inch)
+        img.hAlign = "CENTER"
+        story.append(img)
+        if chart_summary:
+            story.append(Spacer(1, 6))
+            story.append(Paragraph(chart_summary, styles["CaptionSmall"]))
+    elif chart_summary:
+        story.append(Paragraph("Visualization Summary", styles["SectionHeader"]))
+        story.append(Paragraph(chart_summary, styles["ReportBody"]))
+
+    doc.build(story)
+    return report_path

backend/create_db.py

@@ -0,0 +1,57 @@
+import sqlite3
+import pandas as pd
+import random
+from datetime import datetime, timedelta
+
+def create_dummy_db():
+    conn = sqlite3.connect('test.db')
+    cursor = conn.cursor()
+    
+    # Create Sales Table
+    cursor.execute('''
+    CREATE TABLE IF NOT EXISTS sales (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        date DATE,
+        product_category TEXT,
+        product_name TEXT,
+        quantity INTEGER,
+        unit_price REAL,
+        total_amount REAL,
+        region TEXT
+    )
+    ''')
+    
+    # Generate sample data
+    categories = ['Electronics', 'Clothing', 'Home', 'Books']
+    products = {
+        'Electronics': ['Laptop', 'Smartphone', 'Headphones', 'Monitor'],
+        'Clothing': ['T-Shirt', 'Jeans', 'Jacket', 'Sneakers'],
+        'Home': ['Sofa', 'Table', 'Lamp', 'Rug'],
+        'Books': ['Fiction', 'Non-Fiction', 'Sci-Fi', 'Biography']
+    }
+    regions = ['North', 'South', 'East', 'West']
+    
+    data = []
+    start_date = datetime(2023, 1, 1)
+    for i in range(100):
+        date = start_date + timedelta(days=random.randint(0, 365))
+        category = random.choice(categories)
+        product = random.choice(products[category])
+        quantity = random.randint(1, 5)
+        unit_price = round(random.uniform(10.0, 1000.0), 2)
+        total_amount = round(quantity * unit_price, 2)
+        region = random.choice(regions)
+        
+        data.append((date.strftime('%Y-%m-%d'), category, product, quantity, unit_price, total_amount, region))
+        
+    cursor.executemany('''
+    INSERT INTO sales (date, product_category, product_name, quantity, unit_price, total_amount, region)
+    VALUES (?, ?, ?, ?, ?, ?, ?)
+    ''', data)
+    
+    conn.commit()
+    conn.close()
+    print("Dummy database created successfully.")
+
+if __name__ == "__main__":
+    create_dummy_db()

backend/requirements.txt

@@ -0,0 +1,17 @@
+fastapi
+uvicorn
+langgraph
+langchain
+langchain-groq
+langchain-community
+sqlalchemy
+pandas
+numpy
+scipy
+statsmodels
+matplotlib
+reportlab
+python-dotenv
+pydantic
+requests
+python-multipart

build_frontend.bat

@@ -0,0 +1,19 @@
+@echo off
+REM Build script for React frontend (Windows) - run this before deploying to HF Spaces
+
+echo Building React frontend for production...
+
+cd frontend
+
+REM Install dependencies
+echo Installing dependencies...
+call npm install
+
+REM Build for production
+echo Building production bundle...
+call npm run build
+
+echo Frontend build complete! Output in frontend/dist/
+echo Files ready to be served statically by the Gradio app
+
+cd ..

build_frontend.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Build script for React frontend - run this before deploying to HF Spaces
+
+echo "🔨 Building React frontend for production..."
+
+cd frontend
+
+# Install dependencies
+echo "📦 Installing dependencies..."
+npm install
+
+# Build for production
+echo "🏗️  Building production bundle..."
+npm run build
+
+echo "✅ Frontend build complete! Output in frontend/dist/"
+echo "📁 Files ready to be served statically by the Gradio app"
+
+cd ..

data/archive.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06179cdd0ddf1e4df27d19b80c31bb4ad873453f41a26f00d332d3c720c8e2fd
+size 79402

data/report_f86d7e18-bf33-4d6f-a4fc-e57eb10590f7.pdf

@@ -0,0 +1,97 @@
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docs/agent-workflow.drawio

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+<mxfile host="app.diagrams.net" modified="2025-11-22T00:00:00.000Z" agent="5.0 (Windows)" version="22.0.7" editor="diagramly">
+  <diagram id="agentFlow" name="Agent Workflow">
+    <mxGraphModel dx="1180" dy="740" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1100" pageHeight="850" math="0" shadow="0">
+      <root>
+        <mxCell id="0"/>
+        <mxCell id="1" parent="0"/>
+        <mxCell id="user" value="User Prompt" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#E3F2FD;strokeColor=#1E88E5;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="60" y="180" width="160" height="60" as="geometry"/>
+        </mxCell>
+        <mxCell id="orchestrator" value="LangGraph Orchestrator" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#E8F5E9;strokeColor=#2E7D32;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="260" y="180" width="190" height="70" as="geometry"/>
+        </mxCell>
+        <mxCell id="nl2sql" value="NL2SQL + DB Runner" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#FFF3E0;strokeColor=#FB8C00;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="500" y="120" width="200" height="70" as="geometry"/>
+        </mxCell>
+        <mxCell id="analytics" value="Trend &amp; Anomaly Modules" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#F3E5F5;strokeColor=#8E24AA;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="500" y="240" width="200" height="70" as="geometry"/>
+        </mxCell>
+        <mxCell id="reports" value="Visualization + PDF Builder" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#E0F7FA;strokeColor=#00838F;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="750" y="180" width="210" height="80" as="geometry"/>
+        </mxCell>
+        <mxCell id="frontend" value="React Analyst Dashboard" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#FCE4EC;strokeColor=#AD1457;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="1010" y="180" width="210" height="80" as="geometry"/>
+        </mxCell>
+        <mxCell id="datasets" value="Dataset Upload &amp; Catalog" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#FFFDE7;strokeColor=#FDD835;fontSize=14;" vertex="1" parent="1">
+          <mxGeometry x="260" y="60" width="210" height="70" as="geometry"/>
+        </mxCell>
+        <mxCell id="edge1" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=1;strokeColor=#1E88E5;" edge="1" parent="1" source="user" target="orchestrator">
+          <mxGeometry relative="1" as="geometry"/>
+        </mxCell>
+        <mxCell id="edge2" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=1;strokeColor=#2E7D32;" edge="1" parent="1" source="orchestrator" target="nl2sql">
+          <mxGeometry relative="1" as="geometry"/>
+        </mxCell>
+        <mxCell id="edge3" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=1;strokeColor=#2E7D32;" edge="1" parent="1" source="orchestrator" target="analytics">
+          <mxGeometry relative="1" as="geometry"/>
+        </mxCell>
+        <mxCell id="edge4" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=1;strokeColor=#00838F;" edge="1" parent="1" source="nl2sql" target="reports">
+          <mxGeometry relative="1" as="geometry"/>
+        </mxCell>
+        <mxCell id="edge5" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=1;strokeColor=#8E24AA;" edge="1" parent="1" source="analytics" target="reports">
+          <mxGeometry relative="1" as="geometry"/>
+        </mxCell>
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+          <mxGeometry relative="1" as="geometry"/>
+        </mxCell>
+      </root>
+    </mxGraphModel>
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+</mxfile>

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/README.md

@@ -0,0 +1,16 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.

frontend/eslint.config.js

@@ -0,0 +1,29 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{js,jsx}'],
+    extends: [
+      js.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+      parserOptions: {
+        ecmaVersion: 'latest',
+        ecmaFeatures: { jsx: true },
+        sourceType: 'module',
+      },
+    },
+    rules: {
+      'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }],
+    },
+  },
+])

frontend/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>frontend</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>

frontend/package-lock.json

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+  "name": "frontend",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "frontend",
+      "version": "0.0.0",
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+        "react-dom": "^19.2.0"
+      },
+      "devDependencies": {
+        "@eslint/js": "^9.39.1",
+        "@types/react": "^19.2.5",
+        "@types/react-dom": "^19.2.3",
+        "@vitejs/plugin-react": "^5.1.1",
+        "eslint": "^9.39.1",
+        "eslint-plugin-react-hooks": "^7.0.1",
+        "eslint-plugin-react-refresh": "^0.4.24",
+        "globals": "^16.5.0",
+        "vite": "npm:rolldown-vite@7.2.5"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz",
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+        "picocolors": "^1.1.1"
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+}

frontend/package.json

@@ -0,0 +1,30 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^19.2.0",
+    "react-dom": "^19.2.0"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.1",
+    "@types/react": "^19.2.5",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^5.1.1",
+    "eslint": "^9.39.1",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.4.24",
+    "globals": "^16.5.0",
+    "vite": "npm:rolldown-vite@7.2.5"
+  },
+  "overrides": {
+    "vite": "npm:rolldown-vite@7.2.5"
+  }
+}

frontend/src/App.css

@@ -0,0 +1,885 @@
+:root {
+  --bg-app: #0f172a;
+  --bg-sidebar: #1e293b;
+  --bg-panel: #1e293b;
+  --bg-input: #334155;
+  --border-color: #334155;
+  --primary: #3b82f6;
+  --primary-hover: #2563eb;
+  --text-main: #f8fafc;
+  --text-muted: #94a3b8;
+  --accent-gradient: linear-gradient(135deg, #3b82f6, #8b5cf6);
+  
+  --radius-lg: 12px;
+  --radius-md: 8px;
+  --radius-sm: 4px;
+  
+  --shadow-lg: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
+}
+
+@keyframes glow-rotate {
+  0% {
+    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.4),
+                0 0 25px rgba(59, 130, 246, 0.6),
+                0 0 50px rgba(59, 130, 246, 0.3),
+                -10px 0 30px rgba(139, 92, 246, 0.4),
+                var(--shadow-lg);
+  }
+  25% {
+    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.4),
+                0 0 25px rgba(59, 130, 246, 0.6),
+                0 0 50px rgba(59, 130, 246, 0.3),
+                0 -10px 30px rgba(139, 92, 246, 0.4),
+                var(--shadow-lg);
+  }
+  50% {
+    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.4),
+                0 0 25px rgba(59, 130, 246, 0.6),
+                0 0 50px rgba(59, 130, 246, 0.3),
+                10px 0 30px rgba(139, 92, 246, 0.4),
+                var(--shadow-lg);
+  }
+  75% {
+    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.4),
+                0 0 25px rgba(59, 130, 246, 0.6),
+                0 0 50px rgba(59, 130, 246, 0.3),
+                0 10px 30px rgba(139, 92, 246, 0.4),
+                var(--shadow-lg);
+  }
+  100% {
+    box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.4),
+                0 0 25px rgba(59, 130, 246, 0.6),
+                0 0 50px rgba(59, 130, 246, 0.3),
+                -10px 0 30px rgba(139, 92, 246, 0.4),
+                var(--shadow-lg);
+  }
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  font-family: 'Inter', system-ui, -apple-system, sans-serif;
+  background-color: var(--bg-app);
+  color: var(--text-main);
+  -webkit-font-smoothing: antialiased;
+}
+
+#root {
+  height: 100vh;
+  display: flex;
+  overflow: hidden;
+}
+
+/* Layout */
+.app-shell {
+  display: grid;
+  grid-template-columns: 280px 1fr;
+  width: 100%;
+  height: 100vh;
+}
+
+/* Sidebar */
+.sidebar {
+  background-color: var(--bg-sidebar);
+  border-right: 1px solid var(--border-color);
+  padding: 1.5rem;
+  display: flex;
+  flex-direction: column;
+  gap: 2rem;
+  overflow-y: auto;
+  height: 100%;
+  min-height: 0;
+}
+
+.brand {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+}
+
+.brand h1 {
+  font-size: 1.25rem;
+  font-weight: 700;
+  margin: 0;
+  background: var(--accent-gradient);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+}
+
+.sidebar-section h3 {
+  font-size: 0.75rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--text-muted);
+  margin-bottom: 1rem;
+}
+
+/* Dataset Controls */
+.dataset-control {
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+}
+
+.input-group {
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+
+.input-group label {
+  font-size: 0.875rem;
+  color: var(--text-muted);
+}
+
+.input-group input {
+  background: var(--bg-app);
+  border: 1px solid var(--border-color);
+  color: var(--text-main);
+  padding: 0.5rem;
+  border-radius: var(--radius-md);
+  font-size: 0.875rem;
+}
+
+.btn-secondary {
+  background: transparent;
+  border: 1px solid var(--border-color);
+  color: var(--text-main);
+  padding: 0.5rem;
+  border-radius: var(--radius-md);
+  cursor: pointer;
+  font-size: 0.875rem;
+  transition: all 0.2s;
+}
+
+.btn-secondary:hover:not(:disabled) {
+  background: var(--bg-input);
+}
+
+.btn-primary {
+  background: var(--primary);
+  border: none;
+  color: white;
+  padding: 0.5rem 1rem;
+  border-radius: var(--radius-md);
+  cursor: pointer;
+  font-weight: 500;
+  transition: background 0.2s;
+}
+
+.btn-primary:hover:not(:disabled) {
+  background: var(--primary-hover);
+}
+
+.upload-label {
+  display: block;
+  text-align: center;
+  background: var(--bg-input);
+  border: 1px dashed var(--text-muted);
+  padding: 1rem;
+  border-radius: var(--radius-md);
+  cursor: pointer;
+  font-size: 0.875rem;
+  transition: all 0.2s;
+}
+
+.upload-label:hover {
+  border-color: var(--primary);
+  color: var(--primary);
+}
+
+.upload-label input {
+  display: none;
+}
+
+.dataset-list {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+
+.dataset-item {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 0.5rem;
+  background: var(--bg-app);
+  border-radius: var(--radius-md);
+  font-size: 0.8rem;
+}
+
+.dataset-item strong {
+  color: var(--primary);
+}
+
+/* Main Content */
+.main-content {
+  display: grid;
+  grid-template-columns: 1fr 400px;
+  overflow: hidden;
+  height: 100%;
+  min-height: 0;
+}
+
+/* Chat Area */
+.chat-section {
+  display: flex;
+  flex-direction: column;
+  height: 100%;
+  min-height: 0;
+  position: relative;
+  overflow: hidden;
+}
+
+.chat-header {
+  padding: 1rem 2rem;
+  border-bottom: 1px solid var(--border-color);
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  background: rgba(15, 23, 42, 0.8);
+  backdrop-filter: blur(8px);
+}
+
+.status-indicator {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  font-size: 0.875rem;
+  color: var(--text-muted);
+}
+
+.dot {
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+  background-color: #22c55e;
+}
+
+.dot.busy {
+  background-color: #eab308;
+  animation: pulse 1.5s infinite;
+}
+
+@keyframes pulse {
+  0% { opacity: 1; }
+  50% { opacity: 0.5; }
+  100% { opacity: 1; }
+}
+
+.messages-container {
+  flex: 1;
+  overflow-y: auto;
+  padding: 2rem;
+  display: flex;
+  flex-direction: column;
+  gap: 1.5rem;
+  min-height: 0;
+  scroll-behavior: smooth;
+}
+
+.welcome-screen {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  text-align: center;
+  color: var(--text-muted);
+  max-width: 600px;
+  margin: 0 auto;
+}
+
+.welcome-screen h2 {
+  color: var(--text-main);
+  font-size: 2rem;
+  margin-bottom: 1rem;
+}
+
+.quick-prompts {
+  display: grid;
+  grid-template-columns: repeat(2, 1fr);
+  gap: 1rem;
+  margin-top: 2rem;
+  width: 100%;
+}
+
+.prompt-card {
+  background: var(--bg-panel);
+  border: 1px solid var(--border-color);
+  padding: 1rem;
+  border-radius: var(--radius-lg);
+  text-align: left;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+
+.prompt-card:hover {
+  border-color: var(--primary);
+  transform: translateY(-2px);
+}
+
+/* Messages */
+.message {
+  display: flex;
+  gap: 1rem;
+  max-width: 800px;
+}
+
+.message.user {
+  flex-direction: row-reverse;
+  align-self: flex-end;
+}
+
+.avatar {
+  width: 32px;
+  height: 32px;
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 1.2rem;
+  flex-shrink: 0;
+}
+
+.message.agent .avatar {
+  background: var(--primary);
+  color: white;
+}
+
+.message.user .avatar {
+  background: var(--text-muted);
+  color: var(--bg-app);
+}
+
+.message-content {
+  background: var(--bg-panel);
+  padding: 1rem 1.5rem;
+  border-radius: var(--radius-lg);
+  border-top-left-radius: 2px;
+  line-height: 1.6;
+  font-size: 0.95rem;
+}
+
+.message.user .message-content {
+  background: var(--primary);
+  color: white;
+  border-top-left-radius: var(--radius-lg);
+  border-top-right-radius: 2px;
+}
+
+.input-area {
+  padding: 2rem;
+  background: linear-gradient(to top, var(--bg-app) 80%, transparent);
+}
+
+.input-wrapper {
+  max-width: 1500px;
+  margin: 0 auto;
+  position: relative;
+}
+.input-shell {
+  display: flex;
+  align-items: flex-end;
+  gap: 1rem;
+  background: var(--bg-panel);
+  border: 2px solid var(--border-color);
+  padding: 1.5rem 1.5rem 1.5rem 2rem;
+  border-radius: 999px;
+  box-shadow: var(--shadow-lg);
+  transition: border-color 0.2s, box-shadow 0.2s, transform 0.2s;
+}
+
+.input-shell:focus-within {
+  border-color: var(--primary);
+  animation: glow-rotate 3s ease-in-out infinite;
+}
+
+.input-wrapper textarea {
+  flex: 1;
+  width: 100%;
+  background: transparent;
+  border: none;
+  padding: 0;
+  border-radius: 0;
+  color: var(--text-main);
+  font-size: 1.05rem;
+  resize: none;
+  min-height: 50px;
+  line-height: 1.6;
+  overflow: hidden;
+  transition: border-color 0.2s, box-shadow 0.2s;
+}
+
+.input-wrapper textarea:focus {
+  outline: none;
+}
+
+.send-btn {
+  flex-shrink: 0;
+  border: none;
+  background: linear-gradient(135deg, #60a5fa, #8b5cf6);
+  width: 80px;
+  height: 56px;
+  border-radius: 999px;
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  color: #fff;
+  box-shadow: 0 10px 25px rgba(99, 102, 241, 0.35);
+  transition: transform 0.2s, box-shadow 0.2s, opacity 0.2s;
+}
+
+.send-btn svg {
+  width: 26px;
+  height: 26px;
+  stroke: currentColor;
+  fill: none;
+}
+
+.send-btn:hover:not(:disabled) {
+  transform: translateX(2px) scale(1.02);
+  box-shadow: 0 12px 28px rgba(99, 102, 241, 0.5);
+}
+
+.send-btn:disabled {
+  opacity: 0.6;
+  cursor: not-allowed;
+}
+
+/* Results Panel */
+.results-section {
+  background: var(--bg-sidebar);
+  border-left: 1px solid var(--border-color);
+  display: flex;
+  flex-direction: column;
+  height: 100%;
+  min-height: 0;
+  overflow: hidden;
+}
+
+.results-header {
+  padding: 1rem;
+  border-bottom: 1px solid var(--border-color);
+}
+
+.results-header h2 {
+  margin: 0;
+  font-size: 1rem;
+  font-weight: 600;
+}
+
+.results-content {
+  flex: 1;
+  overflow-y: auto;
+  padding: 1rem;
+  display: flex;
+  flex-direction: column;
+  gap: 1rem;
+  min-height: 0;
+}
+
+.result-card {
+  background: var(--bg-app);
+  border: 1px solid var(--border-color);
+  border-radius: var(--radius-md);
+  padding: 1rem;
+}
+
+.card-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 0.75rem;
+  font-size: 0.8rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--text-muted);
+}
+
+.badge {
+  background: rgba(59, 130, 246, 0.1);
+  color: var(--primary);
+  padding: 0.2rem 0.5rem;
+  border-radius: 4px;
+  font-size: 0.7rem;
+}
+
+.trend-grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 0.5rem;
+  text-align: center;
+}
+
+.trend-grid div {
+  display: flex;
+  flex-direction: column;
+  gap: 0.25rem;
+}
+
+.trend-grid span {
+  font-size: 0.75rem;
+  color: var(--text-muted);
+}
+
+.trend-grid strong {
+  font-size: 1rem;
+}
+
+.anomaly-list {
+  list-style: none;
+  padding: 0;
+  margin: 0;
+}
+
+.anomaly-list li {
+  display: flex;
+  justify-content: space-between;
+  padding: 0.5rem 0;
+  border-bottom: 1px solid var(--border-color);
+  font-size: 0.875rem;
+}
+
+.anomaly-list li:last-child {
+  border-bottom: none;
+}
+
+code {
+  display: block;
+  background: #000;
+  padding: 0.75rem;
+  border-radius: var(--radius-sm);
+  font-family: 'Fira Code', monospace;
+  font-size: 0.8rem;
+  overflow-x: auto;
+  color: #a5b4fc;
+}
+
+.viz-container img {
+  width: 100%;
+  border-radius: var(--radius-sm);
+  border: 1px solid var(--border-color);
+}
+
+.table-container {
+  overflow-x: auto;
+}
+
+table {
+  width: 100%;
+  border-collapse: collapse;
+  font-size: 0.8rem;
+}
+
+th, td {
+  padding: 0.5rem;
+  text-align: left;
+  border-bottom: 1px solid var(--border-color);
+}
+
+th {
+  color: var(--text-muted);
+  font-weight: 500;
+}
+
+.download-btn {
+  display: block;
+  width: 100%;
+  text-align: center;
+  background: var(--bg-input);
+  color: var(--text-main);
+  text-decoration: none;
+  padding: 0.75rem;
+  border-radius: var(--radius-md);
+  font-size: 0.875rem;
+  transition: background 0.2s;
+}
+
+.download-btn:hover {
+  background: var(--border-color);
+}
+
+.empty-state-results {
+  text-align: center;
+  color: var(--text-muted);
+  padding: 2rem;
+  font-size: 0.9rem;
+}
+
+/* Responsive */
+@media (max-width: 1024px) {
+  .main-content {
+    grid-template-columns: 1fr;
+  }
+  .results-section {
+    display: none; /* Or make it a modal/drawer */
+  }
+  .results-section.active {
+    display: flex;
+    position: fixed;
+    inset: 0;
+    z-index: 50;
+  }
+}
+
+.viz-preview img {
+  width: 100%;
+  display: block;
+}
+
+.chart-summary {
+  margin-top: 0.75rem;
+  font-size: 0.9rem;
+  color: rgba(255, 255, 255, 0.78);
+}
+
+.report-chip {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.4rem;
+  margin-top: 0.75rem;
+  padding: 0.4rem 0.9rem;
+  border-radius: 999px;
+  text-decoration: none;
+  color: #1b1d34;
+  background: #9ef4c5;
+  font-weight: 600;
+}
+
+.diagnostic-chips {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.4rem;
+  margin-top: 0.6rem;
+}
+
+.diagnostic-chip {
+  font-size: 0.75rem;
+  border-radius: 999px;
+  padding: 0.25rem 0.7rem;
+  border: 1px solid rgba(255, 255, 255, 0.3);
+  background: rgba(255, 255, 255, 0.08);
+}
+
+.input-area {
+  margin-top: 1.5rem;
+  display: flex;
+  gap: 0.75rem;
+}
+
+.input-area input {
+  flex: 1;
+  border-radius: 999px;
+  border: 1px solid rgba(255, 255, 255, 0.15);
+  background: rgba(255, 255, 255, 0.04);
+  color: inherit;
+  padding: 0.95rem 1.4rem;
+  font-size: 1.05rem;
+}
+
+.input-area input:focus {
+  outline: none;
+  border-color: rgba(122, 131, 255, 0.6);
+}
+
+.input-area button {
+  border: none;
+  border-radius: 999px;
+  padding: 0.95rem 1.8rem;
+  background: linear-gradient(135deg, #7a83ff, #5cd4f4);
+  color: #05070f;
+  font-weight: 600;
+  cursor: pointer;
+  transition: transform 0.2s ease;
+}
+
+.input-area button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.input-area button:not(:disabled):hover {
+  transform: translateY(-1px);
+}
+
+.error-text {
+  margin-top: 0.75rem;
+  color: #ffb4b4;
+  font-size: 0.9rem;
+}
+
+.result-stack {
+  display: flex;
+  flex-direction: column;
+  gap: 1.25rem;
+}
+
+.insight-card {
+  background: rgba(158, 244, 197, 0.08);
+  border: 1px solid rgba(158, 244, 197, 0.4);
+  border-radius: 16px;
+  padding: 1rem 1.2rem;
+  color: #d4ffe7;
+}
+
+.meta-card {
+  border-radius: 16px;
+  padding: 1rem 1.2rem;
+  border: 1px solid rgba(255, 255, 255, 0.08);
+  background: rgba(255, 255, 255, 0.03);
+}
+
+.trend-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(100px, 1fr));
+  gap: 0.75rem;
+  margin-top: 0.75rem;
+}
+
+.trend-grid span {
+  display: block;
+  font-size: 0.75rem;
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+  color: rgba(255, 255, 255, 0.5);
+}
+
+.trend-grid strong {
+  display: block;
+  font-size: 1.1rem;
+}
+
+.anomaly-list {
+  list-style: none;
+  margin: 0.75rem 0 0;
+  padding: 0;
+  display: flex;
+  flex-direction: column;
+  gap: 0.35rem;
+}
+
+.anomaly-list li {
+  display: flex;
+  gap: 0.5rem;
+  flex-wrap: wrap;
+  font-size: 0.85rem;
+  color: rgba(255, 255, 255, 0.85);
+}
+
+.anomaly-list li strong {
+  color: #ffffff;
+}
+
+.meta-head {
+  display: flex;
+  justify-content: space-between;
+  font-size: 0.85rem;
+  color: rgba(255, 255, 255, 0.65);
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+}
+
+.badge {
+  padding: 0.1rem 0.6rem;
+  border-radius: 999px;
+  border: 1px solid rgba(255, 255, 255, 0.2);
+}
+
+.meta-card code {
+  display: block;
+  margin-top: 0.5rem;
+  font-family: 'JetBrains Mono', 'Fira Code', monospace;
+  color: #9bd0ff;
+  white-space: pre-wrap;
+}
+
+.data-preview {
+  border-radius: 18px;
+  border: 1px solid rgba(255, 255, 255, 0.09);
+  background: rgba(1, 2, 15, 0.4);
+  padding: 1rem;
+}
+
+.table-wrapper {
+  overflow-x: auto;
+  margin-top: 0.75rem;
+}
+
+table {
+  width: 100%;
+  border-collapse: collapse;
+  font-size: 0.9rem;
+}
+
+th,
+td {
+  padding: 0.55rem 0.75rem;
+  border-bottom: 1px solid rgba(255, 255, 255, 0.08);
+  text-align: left;
+}
+
+th {
+  font-weight: 600;
+  color: rgba(255, 255, 255, 0.7);
+}
+
+.viz-card {
+  border-radius: 18px;
+  border: 1px solid rgba(255, 255, 255, 0.09);
+  background: rgba(255, 255, 255, 0.03);
+  padding: 1rem;
+}
+
+.viz-card img {
+  width: 100%;
+  border-radius: 12px;
+}
+
+.report-button {
+  display: inline-flex;
+  justify-content: center;
+  padding: 0.9rem 1.5rem;
+  border-radius: 12px;
+  text-decoration: none;
+  font-weight: 600;
+  background: linear-gradient(135deg, #4ade80, #22d3ee);
+  color: #041019;
+}
+
+.empty-state {
+  padding: 2rem;
+  text-align: center;
+  color: rgba(255, 255, 255, 0.6);
+}
+
+@media (max-width: 1100px) {
+  .dashboard {
+    grid-template-columns: 1fr;
+  }
+
+  .messages-area {
+    max-height: 45vh;
+  }
+}
+
+@media (max-width: 600px) {
+  .app-shell {
+    padding: 2rem 1rem 3rem;
+  }
+
+  .panel {
+    padding: 1.25rem;
+  }
+
+  .bubble {
+    max-width: 100%;
+  }
+}

frontend/src/App.jsx

@@ -0,0 +1,546 @@
+import { useState, useRef, useEffect } from 'react'
+import './App.css'
+
+const QUICK_PROMPTS = [
+  'What were total sales by category?',
+  'Compare revenue by region last quarter',
+  'Show monthly sales trend for 2023',
+  'Top products by revenue'
+]
+
+const DEFAULT_AGENT_MESSAGE = {
+  type: 'agent',
+  content: 'Hi! Ask me anything about your business data and I will generate SQL, visuals, and insights for you.'
+}
+
+function App() {
+  const sessionIdRef = useRef(null)
+  if (!sessionIdRef.current) {
+    sessionIdRef.current = crypto?.randomUUID ? crypto.randomUUID() : `session-${Date.now()}`
+  }
+  const sessionId = sessionIdRef.current
+
+  const [messages, setMessages] = useState([DEFAULT_AGENT_MESSAGE])
+  const [input, setInput] = useState('')
+  const [loading, setLoading] = useState(false)
+  const [activeResult, setActiveResult] = useState(null)
+  const [error, setError] = useState(null)
+  const [datasetCatalog, setDatasetCatalog] = useState([])
+  const [catalogLoading, setCatalogLoading] = useState(false)
+  const [tableName, setTableName] = useState('sales')
+  const [uploading, setUploading] = useState(false)
+  const [uploadError, setUploadError] = useState(null)
+  const [clearing, setClearing] = useState(false)
+  const messagesEndRef = useRef(null)
+  const inputRef = useRef(null)
+
+  const [datasetPreview, setDatasetPreview] = useState(null)
+
+  const dataPreview = activeResult?.data ?? []
+  const previewColumns = dataPreview.length ? Object.keys(dataPreview[0]) : []
+  const previewRows = dataPreview.slice(0, 5)
+
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
+  }
+
+  useEffect(() => {
+    scrollToBottom()
+  }, [messages])
+
+  useEffect(() => {
+    if (!inputRef.current) return
+    const el = inputRef.current
+    el.style.height = 'auto'
+    const nextHeight = Math.min(el.scrollHeight, 160)
+    el.style.height = `${nextHeight}px`
+  }, [input])
+
+  const fetchDatasetCatalog = async () => {
+    setCatalogLoading(true)
+    try {
+      const res = await fetch('http://localhost:8000/api/datasets')
+      const payload = await res.json()
+      setDatasetCatalog(payload.tables || [])
+    } catch (err) {
+      console.error('Failed to fetch dataset catalog', err)
+    } finally {
+      setCatalogLoading(false)
+    }
+  }
+
+  useEffect(() => {
+    fetchDatasetCatalog()
+  }, [])
+
+  const sendQuery = async (question) => {
+    const trimmed = question.trim()
+    if (!trimmed) return
+
+    const userMessage = { type: 'user', content: trimmed }
+    setMessages((prev) => [...prev, userMessage])
+    setInput('')
+    setLoading(true)
+    setError(null)
+
+    try {
+      const response = await fetch('http://localhost:8000/api/analyze', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json'
+        },
+        body: JSON.stringify({ query: trimmed, session_id: sessionId })
+      })
+
+      const data = await response.json()
+
+      if (!response.ok || data.error) {
+        const message = data.error || 'Something went wrong.'
+        setError(message)
+        setMessages((prev) => [...prev, { type: 'agent', content: `⚠️ ${message}` }])
+        return
+      }
+
+      const agentPayload = {
+        type: 'agent',
+        content: data.insights || 'Analysis complete.',
+        sql: data.sql_query,
+        visualization: data.visualization_url,
+        chartSummary: data.visualization_summary,
+        trendSummary: data.trend_analysis?.summary,
+        anomalySummary: data.anomaly_analysis?.summary,
+        report: data.report_url
+      }
+
+      setMessages((prev) => [...prev, agentPayload])
+      setActiveResult(data)
+    } catch (err) {
+      const fallback = `Network error: ${err.message}`
+      setError(fallback)
+      setMessages((prev) => [...prev, { type: 'agent', content: fallback }])
+    } finally {
+      setLoading(false)
+    }
+  }
+
+  const handleSubmit = (e) => {
+    e.preventDefault()
+    if (loading) return
+    sendQuery(input)
+  }
+
+  const handlePromptClick = (prompt) => {
+    if (loading) return
+    setInput(prompt)
+    sendQuery(prompt)
+  }
+
+  const handleDatasetUpload = async (event) => {
+    const file = event.target.files?.[0]
+    if (!file || uploading) return
+
+    const formData = new FormData()
+    formData.append('file', file)
+    formData.append('table_name', tableName || 'sales')
+
+    setUploading(true)
+    setUploadError(null)
+
+    try {
+      const response = await fetch('http://localhost:8000/api/upload-csv', {
+        method: 'POST',
+        body: formData
+      })
+
+      const data = await response.json()
+
+      if (!response.ok || data.status !== 'success') {
+        throw new Error(data.detail || 'Upload failed')
+      }
+
+      if (data.preview) {
+        setDatasetPreview({
+          table: data.table,
+          columns: data.columns,
+          rows: data.preview
+        })
+        setActiveResult(null)
+      }
+
+      fetchDatasetCatalog()
+    } catch (err) {
+      setUploadError(err.message)
+    } finally {
+      setUploading(false)
+      event.target.value = ''
+    }
+  }
+
+  const handleClearChat = async () => {
+    if (loading || clearing) return
+
+    setClearing(true)
+    setMessages([DEFAULT_AGENT_MESSAGE])
+    setActiveResult(null)
+    setError(null)
+
+    try {
+      await fetch('http://localhost:8000/api/session/reset', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ session_id: sessionId })
+      })
+    } catch (err) {
+      console.error('Failed to reset session', err)
+    } finally {
+      setClearing(false)
+    }
+  }
+
+  return (
+    <div className="app-shell">
+      <aside className="sidebar">
+        <div className="brand">
+          <h1>InsightPilot</h1>
+        </div>
+
+        <div className="sidebar-section">
+          <h3>Dataset Control</h3>
+          <div className="dataset-control">
+            <div className="input-group">
+              <label>Target Table</label>
+              <input
+                type="text"
+                value={tableName}
+                onChange={(e) => setTableName(e.target.value)}
+                placeholder="sales"
+              />
+            </div>
+            
+            <label className="upload-label">
+              {uploading ? 'Uploading...' : 'Click to Upload CSV'}
+              <input type="file" accept=".csv" onChange={handleDatasetUpload} disabled={uploading} />
+            </label>
+            {uploadError && <p className="error-text">{uploadError}</p>}
+
+            <button className="btn-secondary" onClick={fetchDatasetCatalog} disabled={catalogLoading}>
+              {catalogLoading ? 'Refreshing...' : 'Refresh Catalog'}
+            </button>
+          </div>
+        </div>
+
+        <div className="sidebar-section">
+          <h3>Available Tables</h3>
+          <ul className="dataset-list">
+            {datasetCatalog.length === 0 ? (
+              <li className="dataset-item">No tables found</li>
+            ) : (
+              datasetCatalog.map((table) => (
+                <li key={table.table} className="dataset-item">
+                  <strong>{table.table}</strong>
+                  <span>{table.rows?.toLocaleString()} rows</span>
+                </li>
+              ))
+            )}
+          </ul>
+        </div>
+      </aside>
+
+      <main className="main-content">
+        <section className="chat-section">
+          <header className="chat-header">
+            <div className="status-indicator">
+              <span className={`dot ${loading ? 'busy' : ''}`}></span>
+              {loading ? 'Agent is thinking...' : 'Agent is ready'}
+            </div>
+            <button 
+              className="btn-secondary" 
+              onClick={handleClearChat}
+              disabled={loading || clearing || messages.length <= 1}
+            >
+              {clearing ? 'Clearing...' : 'Clear Chat'}
+            </button>
+          </header>
+
+          <div className="messages-container">
+            {messages.length === 1 ? (
+              <div className="welcome-screen">
+                <h2>Autonomous Data Analyst</h2>
+                <p>Ask natural-language questions about your warehouse. The agent will plan the query, validate results, draw charts, and package everything in a PDF.</p>
+                <div className="quick-prompts">
+                  {QUICK_PROMPTS.map((prompt) => (
+                    <button key={prompt} className="prompt-card" onClick={() => handlePromptClick(prompt)}>
+                      {prompt}
+                    </button>
+                  ))}
+                </div>
+              </div>
+            ) : (
+              messages.map((msg, index) => (
+                <div key={index} className={`message ${msg.type}`}>
+                  <div className="avatar">
+                    {msg.type === 'agent' ? '🤖' : '👤'}
+                  </div>
+                  <div className="message-content">
+                    <p>{msg.content}</p>
+                    {msg.sql && (
+                      <div className="sql-snippet">
+                        <code>{msg.sql}</code>
+                      </div>
+                    )}
+                    {(msg.trendSummary || msg.anomalySummary) && (
+                      <div className="diagnostic-chips">
+                        {msg.trendSummary && <span className="badge">Trend: {msg.trendSummary}</span>}
+                        {msg.anomalySummary && <span className="badge">Anomaly: {msg.anomalySummary}</span>}
+                      </div>
+                    )}
+                  </div>
+                </div>
+              ))
+            )}
+            <div ref={messagesEndRef} />
+          </div>
+
+          <div className="input-area">
+            <form className="input-wrapper" onSubmit={handleSubmit}>
+              <div className="input-shell">
+                <textarea
+                  ref={inputRef}
+                  rows={1}
+                  value={input}
+                  onChange={(e) => setInput(e.target.value)}
+                  onKeyDown={(e) => {
+                    if (e.key === 'Enter' && !e.shiftKey) {
+                      e.preventDefault()
+                      if (!loading) {
+                        sendQuery(input)
+                      }
+                    }
+                  }}
+                  placeholder="Ask anything about your data..."
+                  disabled={loading}
+                />
+                <button type="submit" className="send-btn" disabled={loading}>
+                  <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2.5" strokeLinecap="round" strokeLinejoin="round">
+                    <path d="M22 2L11 13"></path>
+                    <path d="M22 2L15 22L11 13L2 9L22 2Z"></path>
+                  </svg>
+                </button>
+              </div>
+            </form>
+          </div>
+        </section>
+
+        <section className="results-section">
+          <div className="results-header">
+            <h2>Analysis Results</h2>
+          </div>
+          <div className="results-content">
+            {datasetPreview && !activeResult && (
+              <div className="result-card">
+                <div className="card-header">
+                  <span>Uploaded Data Preview: {datasetPreview.table}</span>
+                </div>
+                <div className="table-container">
+                  <table>
+                    <thead>
+                      <tr>
+                        {datasetPreview.columns.map(col => <th key={col}>{col}</th>)}
+                      </tr>
+                    </thead>
+                    <tbody>
+                      {datasetPreview.rows.map((row, i) => (
+                        <tr key={i}>
+                          {datasetPreview.columns.map(col => <td key={col}>{row[col]}</td>)}
+                        </tr>
+                      ))}
+                    </tbody>
+                  </table>
+                </div>
+              </div>
+            )}
+
+            {activeResult ? (
+              <>
+                <div className="result-card">
+                  <div className="card-header">
+                    <span>Insight</span>
+                  </div>
+                  <p>{activeResult.insights}</p>
+                </div>
+
+                {activeResult.visualization_url && (
+                  <div className="result-card">
+                    <div className="card-header">
+                      <span>Visualization</span>
+                    </div>
+                    <div className="viz-container">
+                      <img src={`http://localhost:8000${activeResult.visualization_url}`} alt="Visualization" />
+                    </div>
+                  </div>
+                )}
+
+                {activeResult.trend_analysis && (
+                  <div className="result-card">
+                    <div className="card-header">
+                      <span>Trend Analysis</span>
+                      <span className="badge">Diagnostic</span>
+                    </div>
+                    <p>{activeResult.trend_analysis.summary}</p>
+                    <div className="trend-grid">
+                      <div>
+                        <span>Start</span>
+                        <strong>{Number.isFinite(activeResult.trend_analysis.start) ? activeResult.trend_analysis.start.toFixed(2) : '-'}</strong>
+                      </div>
+                      <div>
+                        <span>End</span>
+                        <strong>{Number.isFinite(activeResult.trend_analysis.end) ? activeResult.trend_analysis.end.toFixed(2) : '-'}</strong>
+                      </div>
+                      <div>
+                        <span>Change</span>
+                        <strong>{activeResult.trend_analysis.change_pct?.toFixed(1)}%</strong>
+                      </div>
+                    </div>
+                  </div>
+                )}
+
+                {activeResult.anomaly_analysis && (
+                  <div className="result-card">
+                    <div className="card-header">
+                      <span>Anomalies</span>
+                      <span className="badge">Diagnostic</span>
+                    </div>
+                    <p>{activeResult.anomaly_analysis.summary}</p>
+                    <ul className="anomaly-list">
+                      {activeResult.anomaly_analysis.anomalies?.slice(0, 3).map((a, i) => (
+                        <li key={i}>
+                          <span>{a.period}</span>
+                          <strong>z={a.z_score?.toFixed(2)}</strong>
+                        </li>
+                      ))}
+                    </ul>
+                  </div>
+                )}
+
+                {activeResult.forecast_analysis && (
+                  <div className="result-card">
+                    <div className="card-header">
+                      <span>Forecast</span>
+                      <span className="badge">Predictive</span>
+                    </div>
+                    <p>{activeResult.forecast_analysis.summary}</p>
+                    {activeResult.forecast_analysis.method && (
+                      <div style={{marginTop: '0.5rem', fontSize: '0.85rem', color: '#8899aa'}}>
+                        Method: {activeResult.forecast_analysis.method}
+                      </div>
+                    )}
+                    {activeResult.forecast_analysis.forecasts && (
+                      <div className="table-container" style={{marginTop: '1rem'}}>
+                        <table>
+                          <thead>
+                            <tr>
+                              <th>Period</th>
+                              <th>Forecast</th>
+                              <th>Lower Bound</th>
+                              <th>Upper Bound</th>
+                            </tr>
+                          </thead>
+                          <tbody>
+                            {activeResult.forecast_analysis.forecasts.map((f, i) => (
+                              <tr key={i}>
+                                <td>{f.period}</td>
+                                <td>{Number.isFinite(f.value) ? f.value.toFixed(2) : '-'}</td>
+                                <td>{Number.isFinite(f.lower_bound) ? f.lower_bound.toFixed(2) : '-'}</td>
+                                <td>{Number.isFinite(f.upper_bound) ? f.upper_bound.toFixed(2) : '-'}</td>
+                              </tr>
+                            ))}
+                          </tbody>
+                        </table>
+                      </div>
+                    )}
+                  </div>
+                )}
+
+                {activeResult.statistical_tests && (
+                  <div className="result-card">
+                    <div className="card-header">
+                      <span>Statistical Tests</span>
+                      <span className="badge">Diagnostic</span>
+                    </div>
+                    <p>{activeResult.statistical_tests.summary}</p>
+                    <div style={{marginTop: '1rem'}}>
+                      {activeResult.statistical_tests.tests && Object.entries(activeResult.statistical_tests.tests).map(([testName, testData]) => (
+                        <div key={testName} style={{marginBottom: '1rem', padding: '0.75rem', background: '#1e2430', borderRadius: '6px'}}>
+                          <div style={{fontWeight: '600', marginBottom: '0.5rem', textTransform: 'capitalize'}}>
+                            {testName.replace(/_/g, ' ')}
+                          </div>
+                          <div style={{fontSize: '0.9rem', color: '#8899aa'}}>
+                            {testData.summary || testData.test || '-'}
+                          </div>
+                          {testData.p_value !== undefined && (
+                            <div style={{marginTop: '0.5rem', fontSize: '0.85rem'}}>
+                              <span style={{color: '#8899aa'}}>p-value: </span>
+                              <span style={{fontFamily: 'monospace', color: testData.significant || testData.is_stationary === false ? '#ff6b6b' : '#51cf66'}}>
+                                {Number.isFinite(testData.p_value) ? testData.p_value.toFixed(4) : '-'}
+                              </span>
+                              {testData.significant !== undefined && (
+                                <span style={{marginLeft: '0.75rem', color: testData.significant ? '#ff6b6b' : '#51cf66'}}>
+                                  {testData.significant ? '✓ Significant' : '○ Not Significant'}
+                                </span>
+                              )}
+                              {testData.is_stationary !== undefined && (
+                                <span style={{marginLeft: '0.75rem', color: testData.is_stationary ? '#51cf66' : '#ff6b6b'}}>
+                                  {testData.is_stationary ? '✓ Stationary' : '○ Non-Stationary'}
+                                </span>
+                              )}
+                            </div>
+                          )}
+                        </div>
+                      ))}
+                    </div>
+                  </div>
+                )}
+
+                {previewColumns.length > 0 && (
+                  <div className="result-card">
+                    <div className="card-header">
+                      <span>Data Preview</span>
+                    </div>
+                    <div className="table-container">
+                      <table>
+                        <thead>
+                          <tr>
+                            {previewColumns.map(col => <th key={col}>{col}</th>)}
+                          </tr>
+                        </thead>
+                        <tbody>
+                          {previewRows.map((row, i) => (
+                            <tr key={i}>
+                              {previewColumns.map(col => <td key={col}>{row[col]}</td>)}
+                            </tr>
+                          ))}
+                        </tbody>
+                      </table>
+                    </div>
+                  </div>
+                )}
+
+                {activeResult.report_url && (
+                  <a href={`http://localhost:8000${activeResult.report_url}`} target="_blank" rel="noreferrer" className="download-btn">
+                    Download PDF Report
+                  </a>
+                )}
+              </>
+            ) : (
+              <div className="empty-state-results">
+                <p>Run a query to see detailed analysis, charts, and data previews here.</p>
+              </div>
+            )}
+          </div>
+        </section>
+      </main>
+    </div>
+  )
+}
+
+export default App

frontend/src/index.css

@@ -0,0 +1,13 @@
+:root {
+  font-family: 'Inter', system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+  line-height: 1.5;
+  font-weight: 400;
+  color-scheme: dark;
+}
+
+body {
+  margin: 0;
+  padding: 0;
+  min-height: 100vh;
+  width: 100%;
+}

frontend/src/main.jsx

@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)

frontend/vite.config.js

@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+})

requirements.txt

@@ -0,0 +1,24 @@
+# Python dependencies from backend
+fastapi==0.109.0
+uvicorn[standard]==0.27.0
+langgraph==0.0.20
+langchain==0.1.0
+langchain-groq==0.0.1
+langchain-community==0.0.13
+sqlalchemy==2.0.25
+pandas==2.1.4
+numpy==1.26.3
+scipy==1.11.4
+statsmodels==0.14.1
+matplotlib==3.8.2
+reportlab==4.0.8
+python-dotenv==1.0.0
+pydantic==2.5.3
+requests==2.31.0
+python-multipart==0.0.6
+
+# Gradio for HF Spaces UI
+gradio==4.16.0
+
+# Additional helpful packages
+aiofiles==23.2.1

setup_hf_deployment.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+# Quick setup script for HF Spaces deployment
+
+echo "🚀 Setting up InsightPilot for Hugging Face Spaces deployment"
+echo ""
+
+# Check if we should build frontend
+read -p "Build React frontend? (y/n - takes a few minutes): " build_frontend
+
+if [ "$build_frontend" = "y" ] || [ "$build_frontend" = "Y" ]; then
+    echo "📦 Building frontend..."
+    cd frontend
+    npm install
+    npm run build
+    cd ..
+    echo "✅ Frontend built successfully!"
+else
+    echo "⏭️  Skipping frontend build (app will work without it)"
+fi
+
+echo ""
+echo "📝 Next steps:"
+echo ""
+echo "1. Create a new Space on Hugging Face:"
+echo "   → https://huggingface.co/new-space"
+echo "   → Choose SDK: Gradio"
+echo "   → Choose Hardware: CPU basic (free)"
+echo ""
+echo "2. Clone your new Space:"
+echo "   git clone https://huggingface.co/spaces/YOUR_USERNAME/SPACE_NAME"
+echo ""
+echo "3. Copy files to your Space:"
+echo "   cp app.py YOUR_SPACE/"
+echo "   cp requirements.txt YOUR_SPACE/"
+echo "   cp README_HF.md YOUR_SPACE/README.md"
+echo "   cp -r backend YOUR_SPACE/"
+echo "   cp -r data YOUR_SPACE/"
+if [ "$build_frontend" = "y" ] || [ "$build_frontend" = "Y" ]; then
+    echo "   cp -r frontend/dist YOUR_SPACE/frontend/dist"
+fi
+echo ""
+echo "4. Push to Hugging Face:"
+echo "   cd YOUR_SPACE"
+echo "   git add ."
+echo "   git commit -m 'Initial deployment'"
+echo "   git push"
+echo ""
+echo "5. Add your GROQ_API_KEY in Space Settings → Repository secrets"
+echo ""
+echo "📖 Full deployment guide: See DEPLOYMENT.md"
+echo ""
+echo "✨ Done! Ready to deploy to Hugging Face Spaces!"