app.py

import gradio as gr
import requests
import json
import os
from pathlib import Path
from collections import Counter
from datetime import datetime

try:
    import plotly.graph_objects as go
    PLOTLY_AVAILABLE = True
except ImportError:
    PLOTLY_AVAILABLE = False
    go = None

BACKEND_BASE_URL = os.getenv("BACKEND_BASE_URL", "http://localhost:8000")


def chat_with_agent(message, tenant_id, history):
    """
    Send a message to the backend MCP agent and return the response.
    Uses streaming for real-time word-by-word updates.
    
    Args:
        message: User's message text
        tenant_id: Tenant ID for multi-tenant isolation
        history: Chat history (Gradio messages format)
    
    Yields:
        Updated chat history with agent response (streaming)
    """
    if not message or not message.strip():
        yield history
        return
    
    if not tenant_id or not tenant_id.strip():
        error_msg = "Please enter a Tenant ID before sending a message."
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": error_msg})
        yield history
        return
    
    # Add user message to history
    history.append({"role": "user", "content": message})
    
    # Backend streaming endpoint
    backend_url = f"{BACKEND_BASE_URL}/agent/message/stream"
    
    # Prepare request payload
    payload = {
        "tenant_id": tenant_id.strip(),
        "message": message,
        "user_id": None,
        "conversation_history": [],
        "temperature": 0.0
    }
    
    try:
        # Make streaming request
        response = requests.post(
            backend_url,
            json=payload,
            headers={"Content-Type": "application/json"},
            stream=True,
            timeout=120
        )
        
        if response.status_code == 200:
            # Initialize assistant message
            assistant_message = ""
            history.append({"role": "assistant", "content": assistant_message})
            yield history  # Yield initial empty message
            
            # Stream tokens - use iter_lines for SSE format
            for line_bytes in response.iter_lines():
                if line_bytes:
                    try:
                        line = line_bytes.decode('utf-8').strip()
                        if not line:
                            continue
                            
                        if line.startswith('data: '):
                            data_str = line[6:]  # Remove 'data: ' prefix
                            try:
                                data = json.loads(data_str)
                                
                                # Handle status messages
                                if 'status' in data:
                                    status_msg = data.get('message', '')
                                    if status_msg:
                                        # Show status in the message temporarily
                                        history[-1] = {"role": "assistant", "content": f"⏳ {status_msg}"}
                                        yield history
                                    continue
                                
                                # Handle tokens
                                token = data.get('token', '')
                                if token:
                                    assistant_message += token
                                    # Update the last message in history
                                    history[-1] = {"role": "assistant", "content": assistant_message}
                                    yield history  # Yield updated history immediately
                                
                                if data.get('done', False):
                                    break
                            except json.JSONDecodeError:
                                continue
                        elif line.startswith('error:'):
                            try:
                                error_data = json.loads(line[6:])
                                error_msg = error_data.get('error', 'Unknown error')
                                history[-1] = {"role": "assistant", "content": f"❌ Error: {error_msg}"}
                                yield history
                                break
                            except:
                                pass
                    except UnicodeDecodeError:
                        continue
        else:
            error_msg = f"Error {response.status_code}: {response.text}"
            history.append({"role": "assistant", "content": error_msg})
            yield history
    
    except requests.exceptions.ConnectionError:
        error_msg = "❌ Connection Error: Could not connect to backend. Please ensure the FastAPI server is running at http://localhost:8000"
        history.append({"role": "assistant", "content": error_msg})
        yield history
    
    except requests.exceptions.Timeout:
        error_msg = "⏱️ Request Timeout: The backend took longer than 2 minutes to respond. This may happen if:\n- The LLM is processing a complex query\n- Multiple tools (RAG, Web Search) are being used\n- The backend is under heavy load\n\nPlease try again with a simpler query, or check if the backend services (Ollama, MCP servers) are running properly."
        history.append({"role": "assistant", "content": error_msg})
        yield history
    
    except requests.exceptions.RequestException as e:
        error_msg = f"❌ Request Error: {str(e)}"
        history.append({"role": "assistant", "content": error_msg})
        yield history
    
    except Exception as e:
        error_msg = f"❌ Unexpected Error: {str(e)}"
        history.append({"role": "assistant", "content": error_msg})
        yield history


def ingest_document(
    tenant_id: str,
    source_type: str,
    content: str,
    document_url: str,
    filename: str,
    doc_id: str,
    metadata_json: str
):
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required to ingest documents."

    tenant_id = tenant_id.strip()

    payload_content = content or ""
    if source_type == "url" and document_url:
        payload_content = document_url.strip()

    metadata = {}
    if filename:
        metadata["filename"] = filename.strip()
    if document_url:
        metadata["url"] = document_url.strip()
    if doc_id:
        metadata["doc_id"] = doc_id.strip()

    if metadata_json:
        try:
            extra_metadata = json.loads(metadata_json)
            if isinstance(extra_metadata, dict):
                metadata.update(extra_metadata)
            else:
                return "❗ Metadata JSON must represent an object (key/value pairs)."
        except json.JSONDecodeError as exc:
            return f"❗ Invalid metadata JSON: {exc}"

    payload = {
        "action": "ingest_document",
        "tenant_id": tenant_id,
        "source_type": source_type,
        "content": payload_content,
        "metadata": metadata
    }

    try:
        response = requests.post(
            f"{BACKEND_BASE_URL}/rag/ingest-document",
            json=payload,
            headers={"Content-Type": "application/json"},
            timeout=60
        )
        if response.status_code == 200:
            data = response.json()
            return f"✅ Document ingested successfully.\n\n{data.get('message', '')}"
        return f"❌ Ingestion failed ({response.status_code}): {response.text}"
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach the backend. Make sure the FastAPI server is running."
    except requests.exceptions.Timeout:
        return "⏱️ The ingestion request timed out. Please try again."
    except Exception as exc:
        return f"❌ Unexpected error during ingestion: {exc}"


def ingest_file(tenant_id: str, file_obj):
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required to ingest files."
    if file_obj is None:
        return "❗ Please select a file to upload."

    tenant_id = tenant_id.strip()

    try:
        file_path = Path(file_obj.name)
        with open(file_path, "rb") as f:
            file_bytes = f.read()

        files = {
            "file": (file_path.name, file_bytes, "application/octet-stream")
        }
        response = requests.post(
            f"{BACKEND_BASE_URL}/rag/ingest-file",
            files=files,
            headers={"x-tenant-id": tenant_id},
            timeout=120
        )
        if response.status_code == 200:
            data = response.json()
            return f"✅ File ingested successfully.\n\n{data.get('message', '')}"
        return f"❌ File ingestion failed ({response.status_code}): {response.text}"
    except FileNotFoundError:
        return "❌ Could not read the uploaded file."
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach the backend. Make sure the FastAPI server is running."
    except requests.exceptions.Timeout:
        return "⏱️ File ingestion timed out. Please try again."
    except Exception as exc:
        return f"❌ Unexpected error during file ingestion: {exc}"


def _format_rules_table(rules: list[str]) -> list[list]:
    return [[idx + 1, rule] for idx, rule in enumerate(rules)]


def fetch_admin_rules(tenant_id: str) -> tuple[str, list[list]]:
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required.", []

    tenant_id = tenant_id.strip()
    try:
        response = requests.get(
            f"{BACKEND_BASE_URL}/admin/rules",
            headers={"x-tenant-id": tenant_id},
            timeout=30
        )
        if response.status_code == 200:
            rules = response.json().get("rules", [])
            if not rules:
                return "✅ No admin rules have been configured yet.", []
            summary = f"### Current Rules ({len(rules)})"
            return summary, _format_rules_table(rules)
        return f"❌ Error {response.status_code}: {response.text}", []
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach backend. Ensure the FastAPI server is running.", []
    except requests.exceptions.Timeout:
        return "⏱️ Request timed out. Please try again.", []
    except Exception as exc:
        return f"❌ Unexpected error: {exc}", []


def extract_rules_from_file(file_path) -> str:
    """
    Extract rules from uploaded file (TXT, PDF, DOC, DOCX).
    Returns the extracted text content.
    """
    if file_path is None:
        return ""
    
    try:
        # Gradio File component returns file path as string
        if isinstance(file_path, str):
            file_path = Path(file_path)
        else:
            # Sometimes it's a file object with .name attribute
            file_path = Path(file_path.name if hasattr(file_path, 'name') else file_path)
        
        if not file_path.exists():
            return f"❌ File not found: {file_path}"
        
        file_ext = file_path.suffix.lower()
        
        # Read file based on type
        if file_ext == '.txt' or file_ext == '.md':
            # Plain text files
            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                content = f.read()
            return content
        
        elif file_ext == '.pdf':
            # PDF files - use PyPDF2
            try:
                import PyPDF2
                with open(file_path, 'rb') as f:
                    pdf_reader = PyPDF2.PdfReader(f)
                    content = []
                    for page in pdf_reader.pages:
                        content.append(page.extract_text())
                    return '\n'.join(content)
            except ImportError:
                return "❌ PDF extraction requires PyPDF2. Install with: pip install PyPDF2"
            except Exception as e:
                return f"❌ Failed to extract text from PDF: {str(e)}"
        
        elif file_ext in ['.doc', '.docx']:
            # DOC/DOCX files - use python-docx
            try:
                from docx import Document
                doc = Document(file_path)
                content = []
                for paragraph in doc.paragraphs:
                    content.append(paragraph.text)
                return '\n'.join(content)
            except ImportError:
                return "❌ DOCX extraction requires python-docx. Install with: pip install python-docx"
            except Exception as e:
                return f"❌ Failed to extract text from DOCX: {str(e)}"
        
        else:
            return f"❌ Unsupported file type: {file_ext}. Supported: .txt, .pdf, .doc, .docx"
    
    except Exception as e:
        return f"❌ Error reading file: {str(e)}"


def add_admin_rules(tenant_id: str, rules_text: str) -> str:
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required."
    if not rules_text or not rules_text.strip():
        return "❗ Provide at least one rule to upload."

    tenant_id = tenant_id.strip()
    # Filter out comment lines (starting with #) and empty lines
    rules = [
        rule.strip() 
        for rule in rules_text.splitlines() 
        if rule.strip() and not rule.strip().startswith("#")
    ]
    if not rules:
        return "❗ No valid rules detected. (Comment lines starting with # are ignored)"

    added = []
    enhanced = []
    errors = []
    
    # Process rules in chunks to avoid timeout
    CHUNK_SIZE = 5  # Process 5 rules at a time
    total_rules = len(rules)
    
    if total_rules == 1:
        # Single rule - use regular endpoint
        try:
            resp = requests.post(
                f"{BACKEND_BASE_URL}/admin/rules",
                params={"rule": rules[0], "enhance": "true"},
                headers={"x-tenant-id": tenant_id},
                timeout=30
            )
            if resp.status_code == 200:
                data = resp.json()
                added.append(data.get("added_rule", rules[0]))
                if data.get("enhanced"):
                    edge_cases = data.get("edge_cases", [])
                    improvements = data.get("improvements", [])
                    if edge_cases or improvements:
                        enhanced.append(f"**{data.get('added_rule', rules[0])}**:")
                        if improvements:
                            enhanced.append(f"  • Improvements: {', '.join(improvements[:3])}")
                        if edge_cases:
                            enhanced.append(f"  • Edge cases identified: {len(edge_cases)}")
            else:
                errors.append(f"{rules[0]} -> {resp.status_code}: {resp.text}")
        except Exception as exc:
            errors.append(f"{rules[0]} -> {exc}")
    else:
        # Multiple rules - process in chunks
        for i in range(0, total_rules, CHUNK_SIZE):
            chunk = rules[i:i + CHUNK_SIZE]
            chunk_num = (i // CHUNK_SIZE) + 1
            total_chunks = (total_rules + CHUNK_SIZE - 1) // CHUNK_SIZE
            
            try:
                resp = requests.post(
                    f"{BACKEND_BASE_URL}/admin/rules/bulk",
                    json={"rules": chunk},
                    headers={"x-tenant-id": tenant_id},
                    params={"enhance": "true"},
                    timeout=45  # Timeout per chunk (5 rules)
                )
                if resp.status_code == 200:
                    data = resp.json()
                    chunk_added = data.get("added_rules", [])
                    added.extend(chunk_added)
                    if data.get("enhanced"):
                        chunk_enhanced = data.get("enhancement_summary", [])
                        enhanced.extend([f"[Chunk {chunk_num}/{total_chunks}] {e}" for e in chunk_enhanced])
                else:
                    errors.append(f"Chunk {chunk_num}/{total_chunks} failed: {resp.status_code}: {resp.text}")
            except requests.exceptions.Timeout:
                errors.append(f"Chunk {chunk_num}/{total_chunks} timed out after 45s")
            except Exception as exc:
                errors.append(f"Chunk {chunk_num}/{total_chunks} error: {exc}")

    summary = []
    if added:
        summary.append(f"✅ Added {len(added)}/{total_rules} rule(s):\n" + "\n".join([f"- {r}" for r in added[:10]]))
        if len(added) > 10:
            summary.append(f"... and {len(added) - 10} more")
    if enhanced:
        summary.append(f"\n🤖 LLM Enhancement Applied:\n" + "\n".join(enhanced[:5]))
        if len(enhanced) > 5:
            summary.append(f"... and {len(enhanced) - 5} more enhancements")
    if errors:
        summary.append("\n⚠️ Errors:\n" + "\n".join(errors))

    return "\n\n".join(summary) if summary else "No rules were added."


def delete_admin_rule(tenant_id: str, rule: str) -> str:
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required."
    if not rule or not rule.strip():
        return "❗ Provide the exact rule text to delete."

    tenant_id = tenant_id.strip()
    rule = rule.strip()

    try:
        resp = requests.delete(
            f"{BACKEND_BASE_URL}/admin/rules/{rule}",
            headers={"x-tenant-id": tenant_id},
            timeout=15
        )
        if resp.status_code == 200:
            return f"🗑️ Deleted rule: {rule}"
        return f"❌ Error {resp.status_code}: {resp.text}"
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach backend. Ensure the FastAPI server is running."
    except requests.exceptions.Timeout:
        return "⏱️ Delete request timed out. Please try again."
    except Exception as exc:
        return f"❌ Unexpected error: {exc}"


def add_rules_from_file(tenant_id: str, file_path):
    """
    Extract rules from uploaded file and add them.
    """
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required.", "👉 Click **Refresh Rules** to see existing entries.", []
    
    if file_path is None:
        return "❗ Please select a file to upload.", "👉 Click **Refresh Rules** to see existing entries.", []
    
    # Extract text from file
    extracted_text = extract_rules_from_file(file_path)
    
    if extracted_text.startswith("❌"):
        # Error occurred during extraction
        summary, rows = fetch_admin_rules(tenant_id)
        return extracted_text, summary, rows
    
    if not extracted_text or not extracted_text.strip():
        summary, rows = fetch_admin_rules(tenant_id)
        return "❗ No text could be extracted from the file.", summary, rows
    
    # Add rules from extracted text
    status = add_admin_rules(tenant_id, extracted_text)
    summary, rows = fetch_admin_rules(tenant_id)
    return status, summary, rows


def add_rules_and_refresh(tenant_id: str, rules_text: str):
    status = add_admin_rules(tenant_id, rules_text)
    summary, rows = fetch_admin_rules(tenant_id)
    return status, summary, rows


def delete_rule_and_refresh(tenant_id: str, rule: str):
    status = delete_admin_rule(tenant_id, rule)
    summary, rows = fetch_admin_rules(tenant_id)
    return status, summary, rows


def fetch_admin_analytics(tenant_id: str):
    """Fetch analytics data and return formatted results with visualizations."""
    if not tenant_id or not tenant_id.strip():
        error_msg = "❗ Tenant ID is required to view analytics."
        return error_msg, {}, None, None, None, None

    tenant_id = tenant_id.strip()
    headers = {"x-tenant-id": tenant_id}
    
    overview_data = {}
    tool_usage_data = {}
    redflags_data = {}
    activity_data = {}
    error_msg = None
    
    # Fetch Overview
    try:
        resp = requests.get(
            f"{BACKEND_BASE_URL}/analytics/overview",
            headers=headers,
            timeout=30
        )
        if resp.status_code == 200:
            overview_data = resp.json()
        else:
            error_msg = f"❌ Error fetching overview: {resp.status_code}"
    except Exception as e:
        error_msg = f"❌ Error: {str(e)}"
    
    # Fetch Tool Usage
    try:
        resp = requests.get(
            f"{BACKEND_BASE_URL}/analytics/tool-usage",
            headers=headers,
            timeout=30
        )
        if resp.status_code == 200:
            tool_usage_data = resp.json()
    except Exception:
        pass
    
    # Fetch Red Flags
    try:
        resp = requests.get(
            f"{BACKEND_BASE_URL}/analytics/redflags",
            headers=headers,
            timeout=30
        )
        if resp.status_code == 200:
            redflags_data = resp.json()
    except Exception:
        pass
    
    # Fetch Activity
    try:
        resp = requests.get(
            f"{BACKEND_BASE_URL}/analytics/activity",
            headers=headers,
            timeout=30
        )
        if resp.status_code == 200:
            activity_data = resp.json()
    except Exception:
        pass
    
    # Extract data for visualizations
    overview = overview_data.get("overview", {})
    tool_usage = overview.get("tool_usage", tool_usage_data.get("tool_usage", {}))
    rag_quality = overview.get("rag_quality", {})
    
    # Create tool usage bar chart
    tool_chart = None
    if tool_usage and PLOTLY_AVAILABLE:
        try:
            tools = []
            counts = []
            latencies = []
            colors_list = []
            
            color_map = {
                "rag": "#3b82f6",
                "rag.search": "#2563eb",
                "rag.ingest": "#1d4ed8",
                "rag.list": "#1e40af",
                "web.search": "#06b6d4",
                "admin": "#a855f7",
                "llm": "#10b981"
            }
            
            for tool_name, stats in tool_usage.items():
                tools.append(tool_name.replace(".", " ").title())
                counts.append(stats.get("count", 0))
                latencies.append(stats.get("avg_latency_ms", 0))
                colors_list.append(color_map.get(tool_name, "#6b7280"))
            
            if tools:
                fig = go.Figure()
                fig.add_trace(go.Bar(
                    x=tools,
                    y=counts,
                    name="Usage Count",
                    marker_color=colors_list,
                    text=counts,
                    textposition='outside',
                    hovertemplate='<b>%{x}</b><br>Count: %{y}<br><extra></extra>'
                ))
                fig.update_layout(
                    title={
                        "text": "Tool Usage Count",
                        "x": 0.5,
                        "xanchor": "center",
                        "font": {"size": 16, "color": "#1f2937"}
                    },
                    xaxis_title="Tool",
                    yaxis_title="Count",
                    height=380,
                    showlegend=False,
                    margin=dict(l=50, r=20, t=60, b=50),
                    plot_bgcolor="rgba(0,0,0,0)",
                    paper_bgcolor="rgba(0,0,0,0)",
                    font=dict(color="#374151", size=12),
                    xaxis=dict(gridcolor="rgba(0,0,0,0.1)"),
                    yaxis=dict(gridcolor="rgba(0,0,0,0.1)")
                )
                tool_chart = fig
        except Exception:
            tool_chart = None
    
    # Create latency chart
    latency_chart = None
    if tool_usage and PLOTLY_AVAILABLE:
        try:
            tools = []
            latencies = []
            colors_list = []
            
            color_map = {
                "rag": "#3b82f6",
                "rag.search": "#2563eb",
                "rag.ingest": "#1d4ed8",
                "rag.list": "#1e40af",
                "web.search": "#06b6d4",
                "admin": "#a855f7",
                "llm": "#10b981"
            }
            
            for tool_name, stats in tool_usage.items():
                avg_latency = stats.get("avg_latency_ms", 0)
                if avg_latency > 0:
                    tools.append(tool_name.replace(".", " ").title())
                    latencies.append(round(avg_latency, 2))
                    colors_list.append(color_map.get(tool_name, "#6b7280"))
            
            if tools:
                fig = go.Figure()
                fig.add_trace(go.Bar(
                    x=tools,
                    y=latencies,
                    name="Avg Latency (ms)",
                    marker_color=colors_list,
                    text=[f"{l}ms" for l in latencies],
                    textposition='outside',
                    hovertemplate='<b>%{x}</b><br>Avg Latency: %{y}ms<extra></extra>'
                ))
                fig.update_layout(
                    title={
                        "text": "Average Tool Latency",
                        "x": 0.5,
                        "xanchor": "center",
                        "font": {"size": 16, "color": "#1f2937"}
                    },
                    xaxis_title="Tool",
                    yaxis_title="Latency (ms)",
                    height=380,
                    showlegend=False,
                    margin=dict(l=50, r=20, t=60, b=50),
                    plot_bgcolor="rgba(0,0,0,0)",
                    paper_bgcolor="rgba(0,0,0,0)",
                    font=dict(color="#374151", size=12),
                    xaxis=dict(gridcolor="rgba(0,0,0,0.1)"),
                    yaxis=dict(gridcolor="rgba(0,0,0,0.1)")
                )
                latency_chart = fig
        except Exception:
            latency_chart = None
    
    # Create RAG quality metrics visualization
    rag_chart = None
    if rag_quality and PLOTLY_AVAILABLE:
        try:
            metrics = ["Avg Hits", "Avg Score", "Avg Top Score"]
            values = [
                rag_quality.get("avg_hits_per_search", 0),
                rag_quality.get("avg_score", 0) * 100,  # Convert to percentage
                rag_quality.get("avg_top_score", 0) * 100
            ]
            
            fig = go.Figure(data=[go.Bar(
                x=metrics,
                y=values,
                marker_color=["#3b82f6", "#10b981", "#f59e0b"],
                text=[f"{v:.2f}" for v in values],
                textposition='outside',
                hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'
            )])
            fig.update_layout(
                title={
                    "text": "RAG Quality Metrics",
                    "x": 0.5,
                    "xanchor": "center",
                    "font": {"size": 16, "color": "#1f2937"}
                },
                xaxis_title="Metric",
                yaxis_title="Value",
                height=350,
                showlegend=False,
                margin=dict(l=50, r=20, t=60, b=50),
                plot_bgcolor="rgba(0,0,0,0)",
                paper_bgcolor="rgba(0,0,0,0)",
                font=dict(color="#374151", size=12),
                xaxis=dict(gridcolor="rgba(0,0,0,0.1)"),
                yaxis=dict(gridcolor="rgba(0,0,0,0.1)")
            )
            rag_chart = fig
        except Exception:
            rag_chart = None
    
    # Format summary text
    total_queries = overview.get("total_queries", activity_data.get("activity", {}).get("total_queries", 0))
    active_users = overview.get("active_users", activity_data.get("activity", {}).get("active_users", 0))
    redflag_count = overview.get("redflag_count", redflags_data.get("count", 0))
    last_query = overview.get("last_query", activity_data.get("activity", {}).get("last_query"))
    
    # Calculate total tool usage
    total_tool_calls = sum(stats.get("count", 0) for stats in tool_usage.values())
    total_success = sum(stats.get("success_count", 0) for stats in tool_usage.values())
    total_errors = sum(stats.get("error_count", 0) for stats in tool_usage.values())
    
    success_rate = (total_success / total_tool_calls * 100) if total_tool_calls > 0 else 0
    
    summary_text = f"""
#### 📈 Activity Metrics
- **Total Queries:** `{total_queries}`
- **Active Users:** `{active_users}`
- **Red Flags:** `{redflag_count}`
- **Last Query:** `{last_query if last_query else "N/A"}`

---

#### 🔧 Tool Usage Overview
- **Total Tool Calls:** `{total_tool_calls}`
- **Successful Calls:** `{total_success}` ✅
- **Failed Calls:** `{total_errors}` {'⚠️' if total_errors > 0 else ''}
- **Success Rate:** `{success_rate:.1f}%` {'🟢' if success_rate >= 95 else '🟡' if success_rate >= 80 else '🔴'}

---

#### 🔍 RAG Quality Metrics
- **Total Searches:** `{rag_quality.get("total_searches", 0)}`
- **Avg Hits per Search:** `{rag_quality.get("avg_hits_per_search", 0):.2f}`
- **Avg Relevance Score:** `{rag_quality.get("avg_score", 0):.3f}`
- **Avg Top Score:** `{rag_quality.get("avg_top_score", 0):.3f}`
- **Avg Search Latency:** `{rag_quality.get("avg_latency_ms", 0):.2f}ms`

---

#### 📊 Tool Breakdown
"""
    
    # Add individual tool stats to summary
    for tool_name, stats in sorted(tool_usage.items(), key=lambda x: x[1].get("count", 0), reverse=True):
        tool_display = tool_name.replace(".", " ").title()
        count = stats.get("count", 0)
        latency = stats.get("avg_latency_ms", 0)
        success = stats.get("success_count", 0)
        errors = stats.get("error_count", 0)
        status_icon = "✅" if errors == 0 else "⚠️"
        summary_text += f"- **{tool_display}** {status_icon}<br>  └ {count} calls • {latency:.1f}ms avg • {success} success • {errors} errors\n"
    
    return summary_text, tool_usage, tool_chart, latency_chart, rag_chart, error_msg


def list_documents(tenant_id: str, limit: int = 1000, offset: int = 0):
    """
    List all documents for a tenant.
    Returns a tuple of (status_message, documents_list, total_count, stats_dict, chart_fig).
    """
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required.", [], 0, {}, None
    
    tenant_id = tenant_id.strip()
    try:
        response = requests.get(
            f"{BACKEND_BASE_URL}/rag/list",
            params={"tenant_id": tenant_id, "limit": limit, "offset": offset},
            headers={"x-tenant-id": tenant_id},
            timeout=30
        )
        
        if response.status_code == 200:
            data = response.json()
            documents = data.get("documents", [])
            total = data.get("total", 0)
            
            # Format documents for display and collect stats
            formatted_docs = []
            type_counts = Counter()
            total_length = 0
            
            for doc in documents:
                doc_id = doc.get("id", "N/A")
                text = doc.get("text", "")
                created_at = doc.get("created_at", "")
                preview = text[:200] + "..." if len(text) > 200 else text
                
                # Detect document type
                text_lower = text.lower()
                if "http://" in text_lower or "https://" in text_lower or "www." in text_lower:
                    doc_type = "link"
                elif any(x in text_lower for x in ["q:", "question:", "faq", "frequently asked"]):
                    doc_type = "faq"
                elif ".pdf" in text_lower or "pdf document" in text_lower:
                    doc_type = "pdf"
                else:
                    doc_type = "text"
                
                type_counts[doc_type] += 1
                total_length += len(text)
                
                formatted_docs.append({
                    "ID": doc_id,
                    "Type": doc_type,
                    "Preview": preview,
                    "Length": len(text),
                    "Created": created_at[:10] if created_at else "N/A"
                })
            
            # Create statistics dictionary
            stats = {
                "total": total,
                "types": dict(type_counts),
                "avg_length": total_length // total if total > 0 else 0,
                "total_chars": total_length
            }
            
            # Create pie chart for document types
            chart_fig = None
            if type_counts and PLOTLY_AVAILABLE:
                try:
                    labels = list(type_counts.keys())
                    values = list(type_counts.values())
                    colors = {
                        "text": "#3b82f6",  # blue
                        "pdf": "#ef4444",    # red
                        "faq": "#a855f7",    # purple
                        "link": "#06b6d4"    # cyan
                    }
                    chart_colors = [colors.get(label, "#6b7280") for label in labels]
                    
                    fig = go.Figure(data=[go.Pie(
                        labels=labels,
                        values=values,
                        hole=0.4,
                        marker=dict(colors=chart_colors),
                        textinfo='label+percent+value',
                        textfont=dict(size=12),
                        hovertemplate='<b>%{label}</b><br>Count: %{value}<br>Percentage: %{percent}<extra></extra>'
                    )])
                    fig.update_layout(
                        title={
                            "text": "Document Type Distribution",
                            "x": 0.5,
                            "xanchor": "center",
                            "font": {"size": 16}
                        },
                        height=400,
                        showlegend=True,
                        margin=dict(l=20, r=20, t=50, b=20)
                    )
                    chart_fig = fig
                except Exception:
                    chart_fig = None
            
            status = f"✅ Found {total} document(s)"
            return status, formatted_docs, total, stats, chart_fig
        else:
            error_msg = f"❌ Error {response.status_code}: {response.text}"
            return error_msg, [], 0, {}, None
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach backend. Ensure the FastAPI server is running.", [], 0, {}, None
    except requests.exceptions.Timeout:
        return "⏱️ Request timed out. Please try again.", [], 0, {}, None
    except Exception as exc:
        return f"❌ Unexpected error: {exc}", [], 0, {}, None


def delete_document(tenant_id: str, document_id: int):
    """Delete a specific document by ID."""
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required."
    
    if not document_id or document_id <= 0:
        return "❗ Invalid document ID."
    
    tenant_id = tenant_id.strip()
    try:
        response = requests.delete(
            f"{BACKEND_BASE_URL}/rag/delete/{document_id}",
            params={"tenant_id": tenant_id},
            headers={"x-tenant-id": tenant_id},
            timeout=30
        )
        
        if response.status_code == 200:
            return f"✅ Document {document_id} deleted successfully."
        elif response.status_code == 404:
            return f"❌ Document {document_id} not found or access denied."
        else:
            error_data = response.json() if response.headers.get("content-type", "").startswith("application/json") else {}
            error_msg = error_data.get("detail", error_data.get("error", response.text))
            return f"❌ Error {response.status_code}: {error_msg}"
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach backend. Ensure the FastAPI server is running."
    except requests.exceptions.Timeout:
        return "⏱️ Request timed out. Please try again."
    except Exception as exc:
        return f"❌ Unexpected error: {exc}"


def delete_all_documents(tenant_id: str):
    """Delete all documents for a tenant."""
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required."
    
    tenant_id = tenant_id.strip()
    try:
        response = requests.delete(
            f"{BACKEND_BASE_URL}/rag/delete-all",
            params={"tenant_id": tenant_id},
            headers={"x-tenant-id": tenant_id},
            timeout=60
        )
        
        if response.status_code == 200:
            data = response.json()
            deleted_count = data.get("deleted_count", 0)
            return f"✅ Deleted {deleted_count} document(s) successfully."
        else:
            error_data = response.json() if response.headers.get("content-type", "").startswith("application/json") else {}
            error_msg = error_data.get("detail", error_data.get("error", response.text))
            return f"❌ Error {response.status_code}: {error_msg}"
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach backend. Ensure the FastAPI server is running."
    except requests.exceptions.Timeout:
        return "⏱️ Request timed out. Please try again."
    except Exception as exc:
        return f"❌ Unexpected error: {exc}"


def search_knowledge_base(tenant_id: str, query: str):
    """Search the knowledge base using RAG semantic search."""
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required.", []
    
    if not query or not query.strip():
        return "❗ Please enter a search query.", []
    
    tenant_id = tenant_id.strip()
    query = query.strip()
    
    try:
        response = requests.post(
            f"{BACKEND_BASE_URL}/rag/search",
            json={"tenant_id": tenant_id, "query": query, "threshold": 0.3},
            headers={"x-tenant-id": tenant_id, "Content-Type": "application/json"},
            timeout=30
        )
        
        if response.status_code == 200:
            data = response.json()
            results = data.get("results", [])
            
            formatted_results = []
            for idx, result in enumerate(results, 1):
                text = result.get("text", "")
                relevance = result.get("relevance", result.get("score", 0.0))
                formatted_results.append({
                    "Rank": idx,
                    "Text": text[:300] + "..." if len(text) > 300 else text,
                    "Relevance": f"{relevance:.3f}" if relevance else "N/A"
                })
            
            status = f"✅ Found {len(results)} result(s) for '{query}'"
            return status, formatted_results
        else:
            error_msg = f"❌ Error {response.status_code}: {response.text}"
            return error_msg, []
    except requests.exceptions.ConnectionError:
        return "❌ Could not reach backend. Ensure the FastAPI server is running.", []
    except requests.exceptions.Timeout:
        return "⏱️ Request timed out. Please try again.", []
    except Exception as exc:
        return f"❌ Unexpected error: {exc}", []


# Create Gradio interface
with gr.Blocks(
    title="IntegraChat — MCP Autonomous Agent", 
    theme=gr.themes.Soft(),
    css="""
    .stat-card {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        padding: 20px;
        border-radius: 12px;
        color: white;
        text-align: center;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        transition: transform 0.2s;
    }
    .stat-card:hover {
        transform: translateY(-2px);
        box-shadow: 0 6px 12px rgba(0, 0, 0, 0.15);
    }
    .stat-card h3 {
        margin: 0 0 10px 0;
        font-size: 14px;
        opacity: 0.9;
    }
    .stat-card strong {
        font-size: 24px;
        font-weight: bold;
    }
    .summary-box {
        background: linear-gradient(135deg, #1f2937 0%, #111827 100%);
        padding: 24px;
        border-radius: 12px;
        border: 2px solid #374151;
        max-height: 500px;
        overflow-y: auto;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.3);
        color: #f9fafb;
    }
    .summary-box h3, .summary-box h4 {
        margin-top: 0;
        margin-bottom: 12px;
        color: #ffffff;
        font-weight: 600;
    }
    .summary-box h4 {
        color: #e5e7eb;
        font-size: 16px;
        margin-top: 20px;
        margin-bottom: 10px;
    }
    .summary-box p {
        color: #f3f4f6;
        margin: 8px 0;
        line-height: 1.6;
    }
    .summary-box ul {
        margin: 10px 0;
        padding-left: 24px;
        color: #f3f4f6;
    }
    .summary-box li {
        margin: 8px 0;
        color: #f3f4f6;
        line-height: 1.6;
    }
    .summary-box code {
        background-color: #000000;
        color: #00ff00;
        padding: 2px 6px;
        border-radius: 4px;
        font-family: 'Courier New', monospace;
        font-size: 13px;
        border: 1px solid #374151;
    }
    .summary-box hr {
        border: none;
        border-top: 1px solid #4b5563;
        margin: 16px 0;
    }
    .summary-box strong {
        color: #ffffff;
    }
    .chart-title {
        margin-bottom: 8px;
        margin-top: 0;
        font-weight: 600;
        color: #1f2937;
        text-align: center;
    }
    """
) as demo:
    gr.Markdown(
        """
        # 🤖 IntegraChat — MCP Autonomous Agent
        
        **Enterprise-grade AI with autonomous agents, secure multi-tenant RAG, real-time web search, and governance.**
        
        Enter your Tenant ID to chat with the MCP-powered agent or ingest documents into the enterprise knowledge base.
        """
    )
    
    tenant_id_input = gr.Textbox(
        label="Tenant ID",
        placeholder="Enter your tenant ID (e.g., tenant123)",
        value="",
        interactive=True
    )
    
    with gr.Tabs():
        with gr.Tab("Chat"):
            with gr.Row():
                with gr.Column(scale=2):
                    chatbot = gr.Chatbot(
                        label="Chat with Agent",
                        height=500,
                        show_label=True,
                        container=True,
                        type="messages"
                    )
                    
                    with gr.Row():
                        message_input = gr.Textbox(
                            label="Message",
                            placeholder="Type your message here...",
                            scale=4,
                            show_label=False,
                            container=False
                        )
                        send_button = gr.Button("Send", variant="primary", scale=1)
                
                with gr.Column(scale=1):
                    gr.Markdown(
                        """
                        ### 📝 Chat Instructions
                        1. Enter your **Tenant ID** above
                        2. Ask a question or give a task to the agent
                        3. The MCP agent will automatically select tools (RAG, Web, etc.)
                        
                        ### ⚙️ Backend Configuration
                        The agent connects to the FastAPI backend at `http://localhost:8000/agent/message`
                        """
                    )
            
            # Event handlers for chat tab with streaming
            def send_message(message, tenant_id, history):
                # Clear message input immediately
                message_input_value = ""
                # Use streaming function which yields updates
                # Gradio will automatically handle the generator and update UI in real-time
                try:
                    for updated_history in chat_with_agent(message, tenant_id, history):
                        yield updated_history, message_input_value
                except Exception as e:
                    # Fallback if streaming fails
                    error_msg = f"Streaming error: {str(e)}"
                    history.append({"role": "assistant", "content": error_msg})
                    yield history, message_input_value
            
            send_button.click(
                fn=send_message,
                inputs=[message_input, tenant_id_input, chatbot],
                outputs=[chatbot, message_input]
            )
            
            message_input.submit(
                fn=send_message,
                inputs=[message_input, tenant_id_input, chatbot],
                outputs=[chatbot, message_input]
            )
        
        with gr.Tab("Document Ingestion"):
            gr.Markdown(
                """
                ### 📚 Knowledge Base Ingestion
                Ingest documents so the MCP agent can reference tenant-private knowledge.
                
                - **Raw text / URLs:** Use the fields below.
                - **Files (PDF, DOCX, TXT, MD):** Use the file upload section.
                """
            )
            
            ingestion_mode = gr.Radio(
                ["Raw Text", "URL", "File Upload"],
                value="Raw Text",
                label="Select Ingestion Mode"
            )
            
            with gr.Row():
                doc_filename = gr.Textbox(label="Filename (optional)")
                doc_id = gr.Textbox(label="Document ID (optional)")
            
            document_url = gr.Textbox(
                label="Document URL (for URL ingestion)",
                placeholder="https://example.com/policy",
                visible=False
            )
            
            doc_content = gr.Textbox(
                label="Content / Notes",
                placeholder="Paste the document text here...",
                lines=8,
                visible=True
            )
            
            metadata_json = gr.Textbox(
                label="Additional Metadata (JSON)",
                placeholder='{"department": "HR", "tags": ["policy", "benefits"]}'
            )
            
            ingest_doc_button = gr.Button("Ingest Text / URL Document", variant="primary")
            
            document_status = gr.Markdown("")
            
            def handle_ingest_document(
                tenant_id,
                mode,
                content,
                doc_url,
                filename,
                doc_id_value,
                metadata
            ):
                source_type = "raw_text" if mode == "Raw Text" else "url"
                result = ingest_document(
                    tenant_id=tenant_id,
                    source_type=source_type,
                    content=content,
                    document_url=doc_url,
                    filename=filename,
                    doc_id=doc_id_value,
                    metadata_json=metadata
                )
                # Add note about refreshing Knowledge Base Library
                if "✅" in result:
                    result += "\n\n💡 **Tip:** Go to the 'Knowledge Base Library' tab to view your ingested documents."
                return result
            
            ingest_doc_button.click(
                fn=handle_ingest_document,
                inputs=[
                    tenant_id_input,
                    ingestion_mode,
                    doc_content,
                    document_url,
                    doc_filename,
                    doc_id,
                    metadata_json
                ],
                outputs=document_status
            )
            
            file_section = gr.Markdown("#### 📁 File Upload (PDF, DOCX, TXT, Markdown)", visible=False)
            file_upload = gr.File(
                label="Upload File",
                file_types=[".pdf", ".docx", ".txt", ".md", ".markdown"],
                visible=False
            )
            ingest_file_button = gr.Button("Upload & Ingest File", visible=False)
            
            def handle_file_ingestion(tenant_id, file_obj):
                result = ingest_file(tenant_id, file_obj)
                # Add note about refreshing Knowledge Base Library
                if "✅" in result:
                    result += "\n\n💡 **Tip:** Go to the 'Knowledge Base Library' tab to view your ingested documents."
                return result
            
            ingest_file_button.click(
                fn=handle_file_ingestion,
                inputs=[tenant_id_input, file_upload],
                outputs=document_status
            )

            def toggle_source_fields(mode):
                show_text = mode == "Raw Text"
                show_url = mode == "URL"
                show_file = mode == "File Upload"
                return (
                    gr.update(visible=show_text),
                    gr.update(visible=show_url),
                    gr.update(visible=not show_file),
                    gr.update(visible=not show_file),
                    gr.update(visible=not show_file),
                    gr.update(visible=show_file),
                    gr.update(visible=show_file),
                    gr.update(visible=show_file),
                )

            ingestion_mode.change(
                fn=toggle_source_fields,
                inputs=[ingestion_mode],
                outputs=[
                    doc_content,
                    document_url,
                    doc_filename,
                    doc_id,
                    ingest_doc_button,
                    file_section,
                    file_upload,
                    ingest_file_button,
                ]
            )
        
        with gr.Tab("Knowledge Base Library"):
            gr.Markdown(
                """
                ### 📚 Knowledge Base Library
                View, search, and manage all ingested documents for your tenant with visual analytics.
                
                - **📊 Statistics:** View document counts, types, and distribution
                - **🔍 Search:** Use semantic search to find relevant documents
                - **🔽 Filter:** Filter documents by type (text, PDF, FAQ, link)
                - **🗑️ Delete:** Remove individual documents or delete all at once
                """
            )
            
            # Statistics Section
            with gr.Row():
                kb_total_docs = gr.Markdown("### 📄 Total Documents\n**0**", elem_classes=["stat-card"])
                kb_text_docs = gr.Markdown("### 📝 Text Documents\n**0**", elem_classes=["stat-card"])
                kb_pdf_docs = gr.Markdown("### 📄 PDF Documents\n**0**", elem_classes=["stat-card"])
                kb_faq_docs = gr.Markdown("### ❓ FAQ Documents\n**0**", elem_classes=["stat-card"])
                kb_link_docs = gr.Markdown("### 🔗 Link Documents\n**0**", elem_classes=["stat-card"])
            
            # Chart and Search Section
            with gr.Row():
                with gr.Column(scale=1):
                    kb_chart = gr.Plot(label="Document Type Distribution", show_label=True)
                    kb_refresh_button = gr.Button("🔄 Refresh Documents", variant="primary", size="lg")
                    kb_delete_all_button = gr.Button("🗑️ Delete All Documents", variant="stop")
                
                with gr.Column(scale=1):
                    kb_search_query = gr.Textbox(
                        label="🔍 Search Knowledge Base",
                        placeholder="Enter a search query (e.g., 'admin', 'policy', 'FAQ')...",
                        show_label=True
                    )
                    kb_search_button = gr.Button("Search", variant="primary")
                    kb_search_status = gr.Markdown("")
                    kb_search_results = gr.Dataframe(
                        headers=["Rank", "Text", "Relevance"],
                        datatype=["number", "str", "str"],
                        interactive=False,
                        label="Search Results",
                        wrap=True
                    )
            
            # Status and Filter Section
            kb_status = gr.Markdown("👉 Click **Refresh Documents** to load your knowledge base.")
            
            with gr.Row():
                with gr.Column(scale=2):
                    kb_filter_type = gr.Radio(
                        ["all", "text", "pdf", "faq", "link"],
                        value="all",
                        label="Filter by Type",
                        info="Filter documents by detected type"
                    )
                with gr.Column(scale=1):
                    kb_avg_length = gr.Markdown("**Average Length:** 0 characters")
            
            # Documents Table
            kb_documents_table = gr.Dataframe(
                headers=["ID", "Type", "Preview", "Length", "Created"],
                datatype=["number", "str", "str", "number", "str"],
                interactive=False,
                label="Documents",
                wrap=True
            )
            
            # Delete Section
            with gr.Row():
                kb_delete_id = gr.Number(
                    label="Delete Document by ID",
                    value=None,
                    precision=0,
                    info="Enter document ID to delete",
                    scale=3
                )
                kb_delete_button = gr.Button("Delete Document", variant="stop", scale=1)
            
            kb_delete_status = gr.Markdown("")
            
            def refresh_documents(tenant_id, filter_type="all"):
                status, docs, total, stats, chart_fig = list_documents(tenant_id)
                
                # Filter documents by type if not "all"
                if filter_type != "all" and docs:
                    filtered_docs = [doc for doc in docs if doc.get("Type", "").lower() == filter_type.lower()]
                    docs = filtered_docs
                    status = f"✅ Found {len(docs)} {filter_type} document(s) (out of {total} total)"
                
                # Update statistics cards
                type_counts = stats.get("types", {})
                total_md = f"### 📄 Total Documents\n**{total}**"
                text_md = f"### 📝 Text Documents\n**{type_counts.get('text', 0)}**"
                pdf_md = f"### 📄 PDF Documents\n**{type_counts.get('pdf', 0)}**"
                faq_md = f"### ❓ FAQ Documents\n**{type_counts.get('faq', 0)}**"
                link_md = f"### 🔗 Link Documents\n**{type_counts.get('link', 0)}**"
                avg_length_md = f"**Average Length:** {stats.get('avg_length', 0):,} characters"
                
                status_msg = f"{status}\n\n**Total Documents:** {total} | **Total Characters:** {stats.get('total_chars', 0):,}"
                
                return (
                    status_msg, docs, total_md, text_md, pdf_md, faq_md, link_md, 
                    avg_length_md, chart_fig
                )
            
            def filter_documents(tenant_id, filter_type):
                return refresh_documents(tenant_id, filter_type)
            
            def search_kb(tenant_id, query):
                status, results = search_knowledge_base(tenant_id, query)
                return status, results
            
            def delete_doc(tenant_id, doc_id):
                if doc_id is None or doc_id <= 0:
                    return "❗ Please enter a valid document ID.", "", "", "", "", "", "", "", None
                result = delete_document(tenant_id, int(doc_id))
                # Refresh document list after deletion
                return (result, *refresh_documents(tenant_id, "all"))
            
            def delete_all_docs(tenant_id):
                result = delete_all_documents(tenant_id)
                # Refresh document list after deletion
                return (result, *refresh_documents(tenant_id, "all"))
            
            kb_refresh_button.click(
                fn=refresh_documents,
                inputs=[tenant_id_input, kb_filter_type],
                outputs=[
                    kb_status, kb_documents_table, kb_total_docs, kb_text_docs, 
                    kb_pdf_docs, kb_faq_docs, kb_link_docs, kb_avg_length, kb_chart
                ]
            )
            
            kb_filter_type.change(
                fn=filter_documents,
                inputs=[tenant_id_input, kb_filter_type],
                outputs=[
                    kb_status, kb_documents_table, kb_total_docs, kb_text_docs, 
                    kb_pdf_docs, kb_faq_docs, kb_link_docs, kb_avg_length, kb_chart
                ]
            )
            
            kb_search_button.click(
                fn=search_kb,
                inputs=[tenant_id_input, kb_search_query],
                outputs=[kb_search_status, kb_search_results]
            )
            
            kb_search_query.submit(
                fn=search_kb,
                inputs=[tenant_id_input, kb_search_query],
                outputs=[kb_search_status, kb_search_results]
            )
            
            kb_delete_button.click(
                fn=delete_doc,
                inputs=[tenant_id_input, kb_delete_id],
                outputs=[
                    kb_delete_status, kb_status, kb_documents_table, kb_total_docs, 
                    kb_text_docs, kb_pdf_docs, kb_faq_docs, kb_link_docs, kb_avg_length, kb_chart
                ]
            )
            
            kb_delete_all_button.click(
                fn=delete_all_docs,
                inputs=[tenant_id_input],
                outputs=[
                    kb_delete_status, kb_status, kb_documents_table, kb_total_docs, 
                    kb_text_docs, kb_pdf_docs, kb_faq_docs, kb_link_docs, kb_avg_length, kb_chart
                ]
            )
        
        with gr.Tab("Admin Analytics"):
            gr.Markdown(
                """
                # 📊 Admin Analytics Dashboard
                
                Comprehensive tenant-level analytics with visual insights, performance metrics, and detailed tool usage statistics.
                """
            )
            
            # Refresh Button at Top
            with gr.Row():
                analytics_refresh = gr.Button("🔄 Fetch Analytics Snapshot", variant="primary", size="lg")
                gr.Markdown("")
            
            # Statistics Cards
            gr.Markdown("### 📈 Key Metrics")
            with gr.Row():
                analytics_total_queries = gr.Markdown("### 📊 Total Queries\n**0**", elem_classes=["stat-card"])
                analytics_active_users = gr.Markdown("### 👥 Active Users\n**0**", elem_classes=["stat-card"])
                analytics_redflags = gr.Markdown("### 🚩 Red Flags\n**0**", elem_classes=["stat-card"])
                analytics_rag_searches = gr.Markdown("### 🔍 RAG Searches\n**0**", elem_classes=["stat-card"])
            
            # Charts Section
            gr.Markdown("### 📊 Performance Charts")
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("#### 📈 Tool Usage Count", elem_classes=["chart-title"])
                    analytics_tool_chart = gr.Plot(label="", show_label=False)
                with gr.Column(scale=1):
                    gr.Markdown("#### ⚡ Average Tool Latency", elem_classes=["chart-title"])
                    analytics_latency_chart = gr.Plot(label="", show_label=False)
            
            # RAG Quality and Summary Section
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("#### 🔍 RAG Quality Metrics", elem_classes=["chart-title"])
                    analytics_rag_chart = gr.Plot(label="", show_label=False)
                
                with gr.Column(scale=1):
                    gr.Markdown("### 📋 Analytics Summary")
                    analytics_summary = gr.Markdown(
                        "👉 Click **Fetch Analytics Snapshot** to load data.",
                        elem_classes=["summary-box"]
                    )
            
            # Tool Usage Details Table
            gr.Markdown("### 🔧 Detailed Tool Usage")
            analytics_tool_table = gr.Dataframe(
                headers=["Tool", "Count", "Avg Latency (ms)", "Success", "Errors", "Total Tokens"],
                datatype=["str", "number", "number", "number", "number", "number"],
                interactive=False,
                label="",
                wrap=True
            )
            
            analytics_error = gr.Markdown("", visible=False)
            
            def format_analytics(tenant_id):
                summary, tool_usage, tool_chart, latency_chart, rag_chart, error = fetch_admin_analytics(tenant_id)
                
                if error:
                    return (
                        error, "", "", "", "", None, None, None, []
                    )
                
                # Extract overview data - fetch_admin_analytics already fetched it, but we need it again for cards
                overview_data = {}
                try:
                    resp = requests.get(
                        f"{BACKEND_BASE_URL}/analytics/overview",
                        headers={"x-tenant-id": tenant_id},
                        timeout=30
                    )
                    if resp.status_code == 200:
                        data = resp.json()
                        # The API returns {"overview": {...}} or direct overview object
                        overview_data = data.get("overview", data) if isinstance(data, dict) else {}
                        # Debug: print to see what we're getting
                        print(f"DEBUG: Overview data keys: {overview_data.keys() if isinstance(overview_data, dict) else 'Not a dict'}")
                except Exception as e:
                    print(f"Error fetching overview: {e}")
                    pass
                
                # Extract values with proper fallbacks - handle both nested and flat structures
                if isinstance(overview_data, dict):
                    total_queries = overview_data.get("total_queries", 0)
                    active_users = overview_data.get("active_users", 0)
                    redflag_count = overview_data.get("redflag_count", 0)
                    rag_quality = overview_data.get("rag_quality", {})
                    rag_searches = rag_quality.get("total_searches", 0) if isinstance(rag_quality, dict) else 0
                else:
                    total_queries = 0
                    active_users = 0
                    redflag_count = 0
                    rag_quality = {}
                    rag_searches = 0
                
                # Format statistics cards
                queries_md = f"### 📊 Total Queries\n**{total_queries}**"
                users_md = f"### 👥 Active Users\n**{active_users}**"
                redflags_md = f"### 🚩 Red Flags\n**{redflag_count}**"
                rag_md = f"### 🔍 RAG Searches\n**{rag_searches}**"
                
                # Format tool usage table
                tool_table_data = []
                for tool_name, stats in tool_usage.items():
                    tool_table_data.append({
                        "Tool": tool_name.replace(".", " ").title(),
                        "Count": stats.get("count", 0),
                        "Avg Latency (ms)": round(stats.get("avg_latency_ms", 0), 2),
                        "Success": stats.get("success_count", 0),
                        "Errors": stats.get("error_count", 0),
                        "Total Tokens": stats.get("total_tokens", 0)
                    })
                
                return (
                    summary, queries_md, users_md, redflags_md, rag_md,
                    tool_chart, latency_chart, rag_chart, tool_table_data
                )
            
            analytics_refresh.click(
                fn=format_analytics,
                inputs=[tenant_id_input],
                outputs=[
                    analytics_summary,
                    analytics_total_queries,
                    analytics_active_users,
                    analytics_redflags,
                    analytics_rag_searches,
                    analytics_tool_chart,
                    analytics_latency_chart,
                    analytics_rag_chart,
                    analytics_tool_table
                ]
            )
        
        with gr.Tab("Admin Rules & Compliance"):
            gr.Markdown(
                """
                ### 🛡️ Admin Rules & Regulations
                Upload or manage tenant-specific governance rules (red-flag patterns, compliance policies, etc.).
                
                **Upload Methods:**
                - **Text Input:** Enter one rule per line in the text box
                - **File Upload:** Upload rules from TXT, PDF, DOC, or DOCX files
                
                **Features:**
                - Rules are automatically enhanced by LLM (identifies edge cases, improves patterns)
                - Comment lines (starting with #) are automatically ignored
                - Use the delete box to remove an exact rule
                - Refresh anytime to view the latest rule set
                """
            )
            
            rules_summary = gr.Markdown("👉 Click **Refresh Rules** to see existing entries.")
            rules_table = gr.Dataframe(
                headers=["#", "Rule"],
                datatype=["number", "str"],
                interactive=False,
                value=[]
            )
            rules_status = gr.Markdown("")
            
            with gr.Row():
                refresh_rules_button = gr.Button("Refresh Rules", variant="secondary")
                gr.Markdown("")
            
            with gr.Row():
                with gr.Column(scale=1):
                    rules_input = gr.Textbox(
                        label="Rules / Regulations (Text Input)",
                        placeholder="Enter one rule per line...",
                        lines=6
                    )
                    upload_rules_button = gr.Button("Upload / Append Rules", variant="primary")
                
                with gr.Column(scale=1):
                    gr.Markdown("**OR**")
                    rules_file_upload = gr.File(
                        label="Upload Rules File",
                        file_types=[".txt", ".pdf", ".doc", ".docx"],
                        type="filepath"
                    )
                    upload_file_button = gr.Button("Upload Rules from File", variant="primary")
            
            delete_rule_input = gr.Textbox(
                label="Delete Rule",
                placeholder="Enter the exact rule text to remove..."
            )
            delete_rule_button = gr.Button("Delete Rule", variant="stop")
            
            refresh_rules_button.click(
                fn=fetch_admin_rules,
                inputs=[tenant_id_input],
                outputs=[rules_summary, rules_table]
            )
            
            upload_rules_button.click(
                fn=add_rules_and_refresh,
                inputs=[tenant_id_input, rules_input],
                outputs=[rules_status, rules_summary, rules_table]
            )
            
            upload_file_button.click(
                fn=add_rules_from_file,
                inputs=[tenant_id_input, rules_file_upload],
                outputs=[rules_status, rules_summary, rules_table]
            )
            
            delete_rule_button.click(
                fn=delete_rule_and_refresh,
                inputs=[tenant_id_input, delete_rule_input],
                outputs=[rules_status, rules_summary, rules_table]
            )
    
    gr.Markdown(
        """
        ---
        **Built with [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) for the MCP Hackathon**
        """
    )

if __name__ == "__main__":
    import os
    # For Hugging Face Spaces, bind to 0.0.0.0; for local dev, use 127.0.0.1
    # HF Spaces sets SPACE_ID environment variable
    server_name = "0.0.0.0" if os.getenv("SPACE_ID") else "127.0.0.1"
    
    demo.launch(
        server_name=server_name,
        server_port=7860,
        share=False
    )