app.py

import gradio as gr
import requests
import json
import os
from pathlib import Path

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.
    
    Args:
        message: User's message text
        tenant_id: Tenant ID for multi-tenant isolation
        history: Chat history (Gradio messages format)
    
    Returns:
        Updated chat history with agent response
    """
    if not message or not message.strip():
        return history
    
    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})
        return history
    
    # Backend API endpoint
    backend_url = f"{BACKEND_BASE_URL}/agent/message"
    
    # Prepare request payload (matching backend API format)
    payload = {
        "tenant_id": tenant_id.strip(),
        "message": message,
        "user_id": None,
        "conversation_history": [],
        "temperature": 0.0
    }
    
    # Prepare headers
    headers = {
        "Content-Type": "application/json"
    }
    
    try:
        # Send POST request to backend
        # Increased timeout to 120 seconds for complex agent operations
        # (RAG search, web search, LLM calls can take time)
        response = requests.post(
            backend_url,
            json=payload,
            headers=headers,
            timeout=120
        )
        
        # Check if request was successful
        if response.status_code == 200:
            response_data = response.json()
            # Backend returns response in "text" field
            agent_response = response_data.get("text", "No response received from agent.")
            history.append({"role": "user", "content": message})
            history.append({"role": "assistant", "content": agent_response})
        else:
            error_msg = f"Error {response.status_code}: {response.text}"
            history.append({"role": "user", "content": message})
            history.append({"role": "assistant", "content": error_msg})
    
    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": "user", "content": message})
        history.append({"role": "assistant", "content": error_msg})
    
    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": "user", "content": message})
        history.append({"role": "assistant", "content": error_msg})
    
    except requests.exceptions.RequestException as e:
        error_msg = f"❌ Request Error: {str(e)}"
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": error_msg})
    
    except Exception as e:
        error_msg = f"❌ Unexpected Error: {str(e)}"
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": error_msg})
    
    return 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 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()
    rules = [rule.strip() for rule in rules_text.splitlines() if rule.strip()]
    if not rules:
        return "❗ No valid rules detected."

    added = []
    errors = []
    for rule in rules:
        try:
            resp = requests.post(
                f"{BACKEND_BASE_URL}/admin/rules",
                params={"rule": rule},
                headers={"x-tenant-id": tenant_id},
                timeout=15
            )
            if resp.status_code == 200:
                added.append(rule)
            else:
                errors.append(f"{rule} -> {resp.status_code}: {resp.text}")
        except Exception as exc:
            errors.append(f"{rule} -> {exc}")

    summary = []
    if added:
        summary.append(f"✅ Added {len(added)} rule(s):\n" + "\n".join([f"- {r}" for r in added]))
    if errors:
        summary.append("⚠️ 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_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) -> str:
    if not tenant_id or not tenant_id.strip():
        return "❗ Tenant ID is required to view analytics."

    tenant_id = tenant_id.strip()
    headers = {"x-tenant-id": tenant_id}
    sections = []

    endpoints = [
        ("Overview", "/analytics/overview"),
        ("Tool Usage", "/analytics/tool-usage"),
        ("Red Flags", "/analytics/redflags"),
        ("Activity", "/analytics/activity"),
    ]

    for label, path in endpoints:
        try:
            resp = requests.get(
                f"{BACKEND_BASE_URL}{path}",
                headers=headers,
                timeout=30
            )
            if resp.status_code == 200:
                data = resp.json()
                pretty = json.dumps(data, indent=2)
                sections.append(f"### {label}\n```json\n{pretty}\n```")
            else:
                sections.append(f"### {label}\n❌ Error {resp.status_code}: {resp.text}")
        except requests.exceptions.ConnectionError:
            sections.append(f"### {label}\n❌ Could not reach backend. Is the FastAPI server running?")
        except requests.exceptions.Timeout:
            sections.append(f"### {label}\n⏱️ Request timed out. Please try again.")
        except Exception as exc:
            sections.append(f"### {label}\n❌ Unexpected error: {exc}")

    return "\n\n".join(sections) if sections else "No analytics available."


# Create Gradio interface
with gr.Blocks(title="IntegraChat — MCP Autonomous Agent", theme=gr.themes.Soft()) 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
            def send_message(message, tenant_id, history):
                updated_history = chat_with_agent(message, tenant_id, history)
                return updated_history, ""  # Clear message input after sending
            
            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"
                return 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
                )
            
            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):
                return ingest_file(tenant_id, file_obj)
            
            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("Admin Analytics"):
            gr.Markdown(
                """
                ### 📊 Admin Analytics
                Review tenant-level analytics generated by the IntegraChat backend.
                
                - **Overview:** Total queries, active users, red-flag count.
                - **Tool Usage:** How often RAG, Web, and Admin tools are invoked.
                - **Red Flags:** Recent governance events for this tenant.
                - **Activity:** Summary of tenant activity metrics.
                """
            )
            
            analytics_refresh = gr.Button("Fetch Analytics Snapshot", variant="primary")
            analytics_output = gr.Markdown("👉 Click the button to load analytics for the current tenant.")
            
            analytics_refresh.click(
                fn=fetch_admin_analytics,
                inputs=[tenant_id_input],
                outputs=analytics_output
            )
        
        with gr.Tab("Admin Rules & Compliance"):
            gr.Markdown(
                """
                ### 🛡️ Admin Rules & Regulations
                Upload or manage tenant-specific governance rules (red-flag patterns, compliance policies, etc.).
                
                - Enter one rule per line to upload multiple at once.
                - 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("")
            
            rules_input = gr.Textbox(
                label="Rules / Regulations",
                placeholder="Enter one rule per line...",
                lines=6
            )
            upload_rules_button = gr.Button("Upload / Append Rules", 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]
            )
            
            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
    )