from typing import Optional from fastapi import APIRouter, HTTPException, Depends, BackgroundTasks from pydantic import BaseModel, Field import logging import time # For timing logs from src.services.rag_service import get_service_instance from src.core.config import settings logger = logging.getLogger("RERA_API") # Professional Greetings Fast-Path GREETINGS_MAP = { "hi": { "english": "Hi! I am FINRERA Agent. How can I help you today?", "hindi": "नमस्ते! मैं FINRERA एजेंट हूँ। आज मैं आपकी क्या मदद कर सकता हूँ?", "gujarati": "નમસ્તે! હું FINRERA એજન્ટ છું. આજે હું તમને શું મદદ કરી શકું?" }, "hello": { "english": "Hello! I am your FINRERA AI Assistant. What RERA information can I assist you with?", "hindi": "नमस्ते! मैं आपका FINRERA AI असिस्टेंट हूँ। मैं RERA की कौन सी जानकारी में आपकी सहायता कर सकता हूँ?", "gujarati": "નમસ્તે! હું તમારો FINRERA AI આસિસ્ટન્ટ છું. RERA ની કઈ માહિતીમાં હું તમને મદદ કરી શકું?" } } def detect_basic_greeting(msg: str): clean = msg.lower().strip().replace("?", "").replace("!", "").replace(".", "") # Check for simple greetings if clean in ["hi", "hello", "hey", "hola", "namaste"]: return "hi" # Check for "how are you" variants if clean in ["how are you", "how r u", "how are you doing"]: return "how_are_you" return None GREETINGS_MAP["how_are_you"] = { "english": "I am doing great! I am your FINRERA AI Assistant. How can I help you today?", "hindi": "मैं ठीक हूँ! मैं आपका FINRERA AI असिस्टेंट हूँ। आज मैं आपकी क्या मदद कर सकता हूँ?", "gujarati": "હું મજામાં છું! હું તમારો FINRERA AI આસિસ્ટન્ટ છું. આજે હું તમને શું મદદ કરી શકું?" } router = APIRouter(prefix="/ai", tags=["Legal Services"]) # Helper for background history saving async def save_chat_history(user_identifier, user_msg, ai_response, full_name, phone_number, source="website"): from src.services.history_service import history_service try: await history_service.add_message(user_identifier, role="user", message=user_msg, full_name=full_name, phone_number=phone_number, source=source) await history_service.add_message(user_identifier, role="model", message=ai_response, full_name=full_name, phone_number=phone_number, source=source) except Exception as e: logger.error(f"Background history save failed: {e}") _assistant_service = None def get_assistant_service(): global _assistant_service if _assistant_service is None: _assistant_service = get_service_instance() return _assistant_service class LegalQueryRequest(BaseModel): user_message: str = Field(..., min_length=1) user_id: Optional[str] = None phone_number: Optional[str] = None # Backward compatibility contact_id: Optional[str] = None # Backward compatibility full_name: Optional[str] = None # Backward compatibility class LegalQueryResponse(BaseModel): message: str status: str = "True" @router.post("/chat", response_model=LegalQueryResponse) async def chat_assessment(request: LegalQueryRequest, background_tasks: BackgroundTasks): try: from src.services.history_service import history_service # Determine source and identifier if request.phone_number: # WhatsApp flow source = "whatsapp" identifier = request.phone_number else: # Website flow source = "website" identifier = request.user_id or "guest_user" # --- FAST PATH: Simple Greetings --- greeting_key = detect_basic_greeting(request.user_message) if greeting_key: # Simple word-based language detection for greetings msg_lower = request.user_message.lower() lang = "english" if any(word in msg_lower for word in ["કેમ", "નમસ્તે", "છો"]): lang = "gujarati" elif any(word in msg_lower for word in ["नमस्ते", "कैसे", "हो"]): lang = "hindi" msg = GREETINGS_MAP[greeting_key][lang] background_tasks.add_task(save_chat_history, identifier, request.user_message, msg, request.full_name, request.phone_number, source) return LegalQueryResponse(message=msg) # 1. Fetch Last 10 Messages for Context past_msgs = await history_service.get_recent_history(identifier, limit=10, source=source) # 2. Format history for AI (Gemini format) formatted_history = "No prior history." if past_msgs: formatted_history = "\n".join([f"{m['role'].upper()}: {m['parts']}" for m in past_msgs]) # 3. Get AI Assessment service = get_assistant_service() response = await service.get_legal_assessment(request.user_message, chat_history=formatted_history) # 4. Save to History as Background Task (Saves time for the user) background_tasks.add_task( save_chat_history, identifier, request.user_message, response, request.full_name, request.phone_number, source ) return LegalQueryResponse(message=response) except Exception as error: logger.error(f"API Error: {error}") raise HTTPException(status_code=500, detail=str(error)) @router.get("/health") async def health(): return {"status": "healthy"} @router.get("/test-chat") async def test_chat(message: str = "What is RERA?"): """Browser-friendly GET endpoint for testing""" try: service = get_assistant_service() response = await service.get_legal_assessment(message) return {"message": response, "status": "True"} except Exception as e: return {"error": str(e), "status": "False"} @router.get("/history/{identifier}") async def get_user_history(identifier: str, source: str = "website"): """ Fetch complete chat history for a user (for frontend display). Query params: - source: 'whatsapp' or 'website' (default: website) For WhatsApp: identifier is contact_id For Website: identifier is user_id """ try: from src.services.history_service import history_service history_data = await history_service.get_full_history(identifier, source=source) return history_data except Exception as e: logger.error(f"History fetch error: {e}") raise HTTPException(status_code=500, detail=str(e)) @router.get("/all-user-chats") async def get_all_user_chats(page: int = 1, limit: int = 20): """ Get all user chat histories with user details from admins collection. Uses MongoDB $lookup to join collections. Supports pagination. Query params: - page: Page number (default: 1) - limit: Items per page (default: 20) """ try: from src.services.history_service import history_service result = await history_service.get_all_chats_with_user_details(page=page, limit=limit) return result except Exception as e: logger.error(f"All chats fetch error: {e}") raise HTTPException(status_code=500, detail=str(e))