new.py

import re
import json
import requests
import traceback
import time
import os
import asyncio
from typing import Dict, Any, List, Optional, Tuple
from datetime import datetime, timedelta
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor

# Updated imports for pydantic
from pydantic import BaseModel, Field

# Updated imports for LangChain
from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_ollama import OllamaLLM
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_huggingface.embeddings import HuggingFaceEmbeddings

# Enhanced HuggingFace imports for improved functionality
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import numpy as np

# FastAPI and async HTTP client imports
from fastapi import FastAPI, HTTPException, BackgroundTasks, Depends
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import aiohttp
import httpx
from starlette.requests import Request
from starlette.responses import Response

# Import endpoints documentation
from endpoints_documentation import endpoints_documentation

# Set environment variables for HuggingFace
os.environ["HF_HOME"] = "/tmp/huggingface"
os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1"

# Global thread pool for CPU-bound operations
thread_pool = ThreadPoolExecutor(max_workers=4)

# Global HTTP client session for async requests
http_client = None

# Rate limiting settings
RATE_LIMIT_PER_MINUTE = 60
rate_limit_counter = 0
rate_limit_reset_time = time.time()


class ChatMessage(BaseModel):
    """Data model for chat messages"""
    message_id: str = Field(..., description="Unique identifier for the message")
    user_id: str = Field(..., description="User identifier")
    message: str = Field(..., description="The user's message")
    timestamp: datetime = Field(default_factory=datetime.now, description="When the message was sent")
    language: str = Field(default="english", description="Detected language of the message")


class ChatResponse(BaseModel):
    """Data model for chatbot responses"""
    response_id: str = Field(..., description="Unique identifier for the response")
    response_type: str = Field(..., description="Type of response: 'conversation' or 'api_action'")
    message: str = Field(..., description="The chatbot's response message")
    api_call_made: bool = Field(default=False, description="Whether an API call was made")
    api_data: Optional[Dict[str, Any]] = Field(default=None, description="API response data if applicable")
    language: str = Field(default="english", description="Language of the response")
    timestamp: datetime = Field(default_factory=datetime.now, description="When the response was generated")


class RouterResponse(BaseModel):
    """Data model for router chain response"""
    intent: str = Field(..., description="Either 'API_ACTION' or 'CONVERSATION'")
    confidence: float = Field(..., description="Confidence score between 0.0 and 1.0")
    reasoning: str = Field(..., description="Explanation of the decision")
    endpoint: Optional[str] = Field(default=None, description="API endpoint if intent is API_ACTION")
    method: Optional[str] = Field(default=None, description="HTTP method if intent is API_ACTION")
    params: Dict[str, Any] = Field(default_factory=dict, description="Parameters for API call")
    missing_required: List[str] = Field(default_factory=list, description="Missing required parameters")


class HealthcareChatbot:
    def __init__(self):
        self.endpoints_documentation = endpoints_documentation
        self.ollama_base_url = "http://localhost:11434"
        self.model_name = "gemma3"
        self.BASE_URL = 'https://8ac0-197-54-54-66.ngrok-free.app'
        self.headers = {'Content-type': 'application/json'}
        self.user_id = '9e889485-3db4-4f70-a7a2-e219beae6578'
        self.max_retries = 3
        self.retry_delay = 2
        
        # Store conversation history with user-specific sessions
        self.conversation_sessions = {}
        self.max_history_length = 10
        
        # Initialize components
        self._initialize_language_tools()
        self._initialize_llm()
        self._initialize_parsers_and_chains()
        self._initialize_date_parser()
        
        # Initialize async HTTP client
        self._initialize_http_client()
        
        print("Healthcare Chatbot initialized successfully!")
        self._print_welcome_message()

    def _initialize_http_client(self):
        """Initialize async HTTP client with connection pooling"""
        global http_client
        if http_client is None:
            http_client = httpx.AsyncClient(
                timeout=30.0,
                limits=httpx.Limits(max_keepalive_connections=100, max_connections=1000),
                transport=httpx.AsyncHTTPTransport(retries=3)
            )

    async def _close_http_client(self):
        """Close the HTTP client"""
        global http_client
        if http_client:
            await http_client.aclose()
            http_client = None

    def _get_user_session(self, user_id: str) -> List[Dict]:
        """Get or create user conversation session"""
        if user_id not in self.conversation_sessions:
            self.conversation_sessions[user_id] = []
        return self.conversation_sessions[user_id]

    async def _check_rate_limit(self) -> bool:
        """Check and update rate limiting"""
        global rate_limit_counter, rate_limit_reset_time
        current_time = time.time()
        
        # Reset counter if a minute has passed
        if current_time - rate_limit_reset_time >= 60:
            rate_limit_counter = 0
            rate_limit_reset_time = current_time
        
        # Check if we're over the limit
        if rate_limit_counter >= RATE_LIMIT_PER_MINUTE:
            return False
        
        rate_limit_counter += 1
        return True

    def _print_welcome_message(self):
        """Print welcome message in both languages"""
        print("\n" + "="*60)
        print("🏥 HEALTHCARE CHATBOT READY")
        print("="*60)
        print("English: Hello! I'm your healthcare assistant. I can help you with:")
        print("• Booking and managing appointments")
        print("• Finding hospital information")
        print("• Viewing your medical records")
        print("• General healthcare questions")
        print()
        print("Arabic: مرحباً! أنا مساعدك الطبي. يمكنني مساعدتك في:")
        print("• حجز وإدارة المواعيد")
        print("• العثور على معلومات المستشفى")
        print("• عرض سجلاتك الطبية")
        print("• الأسئلة الطبية العامة")
        print("="*60)
        print("Type 'quit' or 'خروج' to exit\n")

    def _initialize_language_tools(self):
        """Initialize language processing tools"""
        try:
            self.embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
            self.language_classifier = pipeline(
                "text-classification", 
                model="papluca/xlm-roberta-base-language-detection",
                top_k=1
            )
            self.sentiment_analyzer = pipeline(
                "sentiment-analysis",
                model="cardiffnlp/twitter-xlm-roberta-base-sentiment"
            )
            print("✓ Language processing models loaded successfully")
        except Exception as e:
            print(f"⚠ Warning: Some language models failed to load: {e}")
            self.language_classifier = None
            self.sentiment_analyzer = None

    def _initialize_date_parser(self):
        """Initialize date parsing model"""
        try:
            self.date_parser = pipeline(
                "token-classification",
                model="Jean-Baptiste/roberta-large-ner-english",
                aggregation_strategy="simple"
            )
        except Exception as e:
            print(f"⚠ Warning: Date parsing model failed to load: {e}")
            self.date_parser = None

    def _initialize_llm(self):
        """Initialize the LLM"""
        callbacks = [StreamingStdOutCallbackHandler()]
        self.llm = OllamaLLM(
            model=self.model_name,
            base_url=self.ollama_base_url,
            callbacks=callbacks,
            temperature=0.7,
            num_ctx=8192,
            top_p=0.9,
            request_timeout=60,
        )

    def _initialize_parsers_and_chains(self):
        """Initialize all prompt templates and chains - REVAMPED to 3 chains only"""
        self.json_parser = JsonOutputParser(pydantic_object=RouterResponse)

        # UNIFIED ROUTER CHAIN - Handles both intent classification AND API routing
        self.router_prompt_template = PromptTemplate(
              template="""
          You are a routing system. Your job is simple:
          1. Understand what the user wants
          2. Handle any dates/times in their request with PRECISE calculations
          3. Check if any endpoint can do what they want
          4. If yes = API_ACTION, if no = CONVERSATION

          ## Available API Endpoints Documentation
          {endpoints_documentation}

          ## User Query to Analyze
          Query: "{user_query}"
          Language: {detected_language}
          Current Context:
          - DateTime: {current_datetime}
          - Timezone: {timezone}
          - Current Day of Week: {current_day_name}

          ## Step-by-Step Analysis

          **STEP 1: What does the user want?**
          - If query is in Arabic, translate it to English first
          - Identify the exact action or information the user is requesting
          - Focus on understanding their underlying need, not just the words

          **STEP 2: Handle Date/Time Processing with PRECISE Calculations**
          IMPORTANT: Use the current datetime ({current_datetime}) and timezone ({timezone}) for ALL calculations.

          ### Current Date Reference Points:
          - Today is: {current_datetime}
          - Current day of week: {current_day_name}
          - Current timezone: {timezone}

          ### Arabic Date/Time Expressions Processing:
          **Basic Relative Dates:**
          - "اليوم" (today) = {current_datetime} date portion
          - "غدا" (tomorrow) = current date + 1 day
          - "أمس" (yesterday) = current date - 1 day
          - "بعد غد" (day after tomorrow) = current date + 2 days

          **Weekly Expressions - CALCULATE PRECISELY:**
          - "الأسبوع القادم" (next week) = current date + 7 days
          - "الأسبوع الماضي" (last week) = current date - 7 days

          **Specific Weekday Calculations - MOST IMPORTANT:**
          For expressions like "يوم [weekday] القادم" (next [weekday]):
          1. Identify the target weekday from Arabic names:
          - الأحد (Sunday) = 0
          - الاثنين (Monday) = 1  
          - الثلاثاء (Tuesday) = 2
          - الأربعاء (Wednesday) = 3
          - الخميس (Thursday) = 4
          - الجمعة (Friday) = 5
          - السبت (Saturday) = 6

          2. Calculate days to add:
          - Get current weekday number (0=Sunday, 1=Monday, etc.)
          - Target weekday number
          - If target > current: days_to_add = target - current
          - If target <= current: days_to_add = 7 - (current - target)
          - Final date = current_date + days_to_add

          **Example Calculation:**
          If today is Sunday (June 1, 2025) and user says "يوم الاربع القادم" (next Wednesday):
          - Current weekday: 0 (Sunday)
          - Target weekday: 3 (Wednesday)  
          - Days to add: 3 - 0 = 3
          - Result: June 1 + 3 days = June 4, 2025

          **Monthly/Yearly Expressions:**
          - "الشهر القادم" (next month) = add 1 month to current date
          - "الشهر الماضي" (last month) = subtract 1 month from current date
          - "السنة القادمة" (next year) = add 1 year to current date

          **Time Expressions:**
          - "صباحًا" (morning/AM) = 09:00 if no specific time given
          - "مساءً" (evening/PM) = 18:00 if no specific time given
          - "ظهرًا" (noon) = 12:00
          - "منتصف الليل" (midnight) = 00:00
          - "بعد ساعتين" (in 2 hours) = current time + 2 hours
          - "قبل ساعة" (1 hour ago) = current time - 1 hour

          **Date Format Output:**
          - Always convert final calculated date to ISO 8601 format: YYYY-MM-DDTHH:MM:SS
          - Include timezone offset if available
          - For date-only expressions, use 00:00:00 as default time

          **STEP 3: Find matching endpoint**
          - Read each endpoint description in the documentation
          - Check if any endpoint's purpose can fulfill what the user wants
          - Match based on functionality, not keywords

          **STEP 4: Decision**
          - Found matching endpoint = "API_ACTION"
          - No matching endpoint = "CONVERSATION"

          **STEP 5: Parameter Extraction (only if API_ACTION)**
          - Extract parameter values from user query
          - Use the CALCULATED dates/times from Step 2
          - Convert all dates/times to ISO 8601 format (YYYY-MM-DDTHH:MM:SS)
          - List any missing required parameters
          - **CRITICAL: All parameters must be in English**
            - Translate any Arabic text to English
            - Convert names to English equivalents (e.g., "دكتور احمد" → "Dr. Ahmed")
            - Use standard English terms for all parameters

          ## Output Format
          {{
              "intent": "CONVERSATION|API_ACTION",
              "confidence": 0.8,
              "reasoning": "User wants: [what user actually needs]. Date/time processing: [show exact calculation: current date + X days = final date]. Found endpoint: [endpoint path and why it matches] OR No endpoint matches this need",
              "endpoint": "/exact/endpoint/path",
              "method": "GET|POST|PUT|DELETE",
              "params": {{
                  // ALL VALUES MUST BE IN ENGLISH
                  // Arabic terms must be translated to English equivalents
              }},
              "missing_required": [],
              "calculated_datetime": "YYYY-MM-DDTHH:MM:SS (if date/time was processed)"
          }}

          ## CRITICAL REMINDERS:
          1. ALWAYS use the provided current_datetime ({current_datetime}) as your base for calculations
          2. For "next weekday" expressions, calculate the exact number of days to add
          3. Show your calculation work in the reasoning field
          4. Double-check weekday numbers: Sunday=0, Monday=1, Tuesday=2, Wednesday=3, Thursday=4, Friday=5, Saturday=6
          5. **ALL PARAMETERS MUST BE IN ENGLISH** - translate any Arabic text before output

          **FINAL CHECK BEFORE OUTPUTTING:**
          🔍 **MANDATORY LANGUAGE CHECK:**
          1. Examine every value in the params object
          2. If ANY value contains Arabic characters (ا-ي), you MUST:
            - Translate it to English
            - Convert names to English equivalents
            - Replace Arabic terms with English counterparts
          3. Only output JSON when ALL parameters are in English

          Now analyze the user query step by step and give me the JSON response.
          """,
              input_variables=["user_query", "detected_language", "extracted_keywords", 
                              "sentiment_analysis", "endpoints_documentation", "current_datetime", 
                              "timezone", "current_day_name"]
          )

        # CONVERSATION CHAIN - Handles conversational responses
        self.conversation_template = PromptTemplate(
            template="""
            You are a friendly and professional healthcare chatbot assistant.

            === RESPONSE GUIDELINES ===
            - Respond ONLY in {detected_language}
            - Be helpful, empathetic, and professional
            - Keep responses concise but informative
            - Use appropriate medical terminology when needed
            - Maintain a caring and supportive tone

            === CONTEXT ===
            User Message: {user_query}
            Language: {detected_language}
            Sentiment: {sentiment_analysis}
            Conversation History: {conversation_history}

            === LANGUAGE-SPECIFIC INSTRUCTIONS ===

            FOR ARABIC RESPONSES:
            - Use Modern Standard Arabic (الفصحى)
            - Be respectful and formal as appropriate in Arabic culture
            - Use proper Arabic medical terminology
            - Keep sentences clear and grammatically correct

            FOR ENGLISH RESPONSES:
            - Use clear, professional English
            - Be warm and approachable
            - Use appropriate medical terminology

            === RESPONSE RULES ===
            1. Address the user's question or comment directly
            2. Provide helpful information when possible
            3. If you cannot help with something specific, explain what you CAN help with
            4. Never provide specific medical advice - always recommend consulting healthcare professionals
            5. Be encouraging and supportive
            6. Do NOT mix languages in your response
            7. End responses naturally without asking multiple questions

            Generate a helpful conversational response:""",
            input_variables=["user_query", "detected_language", "sentiment_analysis", "conversation_history"]
        )

        # API RESPONSE CHAIN - Formats API responses for users
        self.api_response_template = PromptTemplate(
              template="""
          You are a professional healthcare assistant. Generate a natural language response to the user's query using ONLY the provided API data.

          User Query: {user_query}
          User Sentiment: {sentiment_analysis}
          Response Language: {detected_language}

          API Response Data:
          {api_response}

          === CORE INSTRUCTIONS ===

          1. Analyze the API response structure and extract relevant data points
          2. Cross-reference with the user's query to determine what information to include
          3. Respond in {detected_language} using a warm, conversational tone
          4. Convert technical data into natural language appropriate for healthcare communication

          === DATE/TIME HANDLING ===

          1. Identify all date/time fields in the API response (look for ISO 8601 format: YYYY-MM-DDTHH:MM:SS)
          2. For English responses:
            - Format dates as "Month Day, Year at HH:MM AM/PM"
            - Convert times to 12-hour format with proper AM/PM
          3. For Arabic responses:
            - Format dates as "Day Month Year الساعة HH:MM صباحاً/مساءً"
            - Use Arabic numerals (٠١٢٣٤٥٦٧٨٩)
            - Use Arabic month names
          4. Preserve all original date/time values - only change the formatting

          === RESPONSE GUIDELINES ===

          1. Use ONLY data present in the API response
          2. Maintain a professional yet friendly healthcare tone
          3. Adapt to the user's sentiment: 
            - Positive: reinforce with encouraging language
            - Neutral: provide clear, factual information
            - Negative: show empathy and offer assistance
          4. Structure the response to directly answer the user's query
          5. Include relevant details from the API response that address the user's needs

          === CRITICAL RULES ===

          1. Never invent or hallucinate information not present in the API response
          2. If the API response doesn't contain requested information, say so politely
          3. All dates/times must exactly match the API data
          4. Maintain strict language consistency (respond only in {detected_language})
          5. Format all technical data (IDs, codes, etc.) for easy understanding

          Generate a helpful response that addresses the user's query using the API data.
          """,
              input_variables=["user_query", "api_response", "detected_language", "sentiment_analysis"]
          )

        # Create the 3 chains
        self.router_chain = LLMChain(llm=self.llm, prompt=self.router_prompt_template)
        self.conversation_chain = LLMChain(llm=self.llm, prompt=self.conversation_template)
        self.api_response_chain = LLMChain(llm=self.llm, prompt=self.api_response_template)

    def detect_language(self, text):
        """Detect language of the input text"""
        if self.language_classifier and len(text.strip()) > 3:
            try:
                result = self.language_classifier(text)
                detected_lang = result[0][0]['label']
                confidence = result[0][0]['score']
                
                if detected_lang in ['ar', 'arabic']:
                    return "arabic"
                elif detected_lang in ['en', 'english']:
                    return "english"
                elif confidence > 0.8:
                    return "english"  # Default to English for unsupported languages
            except:
                pass
        
        # Fallback: Basic Arabic detection
        arabic_pattern = re.compile(r'[\u0600-\u06FF\u0750-\u077F\u08A0-\u08FF]+')
        if arabic_pattern.search(text):
            return "arabic"
        
        return "english"

    def analyze_sentiment(self, text):
        """Analyze sentiment of the text"""
        if self.sentiment_analyzer and len(text.strip()) > 3:
            try:
                result = self.sentiment_analyzer(text)
                return {
                    "sentiment": result[0]['label'],
                    "score": result[0]['score']
                }
            except:
                pass
        
        return {"sentiment": "NEUTRAL", "score": 0.5}

    def extract_keywords(self, text):
        """Extract keywords from text"""
        # Simple keyword extraction
        words = re.findall(r'\b\w+\b', text.lower())
        # Filter out common words and keep meaningful ones
        stopwords = {'the', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'is', 'are', 'was', 'were'}
        keywords = [w for w in words if len(w) > 3 and w not in stopwords]
        return list(set(keywords))[:5]  # Return top 5 unique keywords

    def get_conversation_context(self, user_id: str) -> str:
        """Get recent conversation history as context"""
        history = self._get_user_session(user_id)
        if not history:
            return "No previous conversation"
        
        context = []
        for item in history[-3:]:  # Last 3 exchanges
            context.append(f"User: {item['user_message']}")
            context.append(f"Bot: {item['bot_response'][:100]}...")  # Truncate long responses
        
        return " | ".join(context)

    def add_to_history(self, user_id: str, user_message: str, bot_response: str, response_type: str):
        """Add exchange to conversation history"""
        history = self._get_user_session(user_id)
        history.append({
            'timestamp': datetime.now(),
            'user_message': user_message,
            'bot_response': bot_response,
            'response_type': response_type
        })
        
        # Keep only recent history
        if len(history) > self.max_history_length:
            self.conversation_sessions[user_id] = history[-self.max_history_length:]

    def parse_relative_date(self, text, detected_language):
        """Parse relative dates from text using a combination of methods"""
        today = datetime.now()
        
        # Handle common relative date patterns in English and Arabic
        tomorrow_patterns = {
            'english': [r'\btomorrow\b', r'\bnext day\b'],
            'arabic': [r'\bغدا\b', r'\bبكرة\b', r'\bغدًا\b', r'\bالغد\b']
        }
        
        next_week_patterns = {
            'english': [r'\bnext week\b'],
            'arabic': [r'\bالأسبوع القادم\b', r'\bالأسبوع المقبل\b', r'\bالاسبوع الجاي\b']
        }
        
        # Check for "tomorrow" patterns
        for pattern in tomorrow_patterns.get(detected_language, []) + tomorrow_patterns.get('english', []):
            if re.search(pattern, text, re.IGNORECASE):
                return (today + timedelta(days=1)).strftime('%Y-%m-%dT%H:%M:%S')
        
        # Check for "next week" patterns
        for pattern in next_week_patterns.get(detected_language, []) + next_week_patterns.get('english', []):
            if re.search(pattern, text, re.IGNORECASE):
                return (today + timedelta(days=7)).strftime('%Y-%m-%dT%H:%M:%S')
        
        # If NER model is available, use it to extract date entities
        if self.date_parser and detected_language == 'english':
            try:
                date_entities = self.date_parser(text)
                for entity in date_entities:
                    if entity['entity_group'] == 'DATE':
                        print(f"Found date entity: {entity['word']}")
                        # Default to tomorrow if we detect any date
                        return (today + timedelta(days=1)).strftime('%Y-%m-%dT%H:%M:%S')
            except Exception as e:
                print(f"Error in date parsing: {e}")
        
        # Default return None if no date pattern is recognized
        return None

    def parse_router_response(self, router_text):
        """Parse the router chain response into structured data"""
        try:
            # Clean the response text
            cleaned_response = router_text
            
            # Remove any comments (both single-line and multi-line)
            cleaned_response = re.sub(r'//.*?$', '', cleaned_response, flags=re.MULTILINE)
            cleaned_response = re.sub(r'/\*.*?\*/', '', cleaned_response, flags=re.DOTALL)
            
            # Remove any trailing commas
            cleaned_response = re.sub(r',(\s*[}\]])', r'\1', cleaned_response)
            
            # Try different methods to parse the JSON response
            try:
                # First attempt: direct JSON parsing of cleaned response
                parsed_response = json.loads(cleaned_response)
            except json.JSONDecodeError:
                try:
                    # Second attempt: extract JSON from markdown code block
                    json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', cleaned_response, re.DOTALL)
                    if json_match:
                        parsed_response = json.loads(json_match.group(1))
                    else:
                        raise ValueError("No JSON found in code block")
                except (json.JSONDecodeError, ValueError):
                    try:
                        # Third attempt: find JSON-like content using regex
                        json_pattern = r'\{\s*"intent"\s*:.*?\}'
                        json_match = re.search(json_pattern, cleaned_response, re.DOTALL)
                        if json_match:
                            json_str = json_match.group(0)
                            # Additional cleaning for the extracted JSON
                            json_str = re.sub(r'//.*?$', '', json_str, flags=re.MULTILINE)
                            json_str = re.sub(r',(\s*[}\]])', r'\1', json_str)
                            parsed_response = json.loads(json_str)
                        else:
                            raise ValueError("Could not extract JSON using regex")
                    except (json.JSONDecodeError, ValueError):
                        print(f"Failed to parse JSON. Raw response: {router_text}")
                        print(f"Cleaned response: {cleaned_response}")
                        # Return default conversation response on parse failure
                        return {
                            "intent": "CONVERSATION",
                            "confidence": 0.5,
                            "reasoning": "Failed to parse router response - defaulting to conversation",
                            "endpoint": None,
                            "method": None,
                            "params": {},
                            "missing_required": []
                        }
            
            # Validate required fields and set defaults
            validated_response = {
                "intent": parsed_response.get("intent", "CONVERSATION"),
                "confidence": parsed_response.get("confidence", 0.5),
                "reasoning": parsed_response.get("reasoning", "Router decision"),
                "endpoint": parsed_response.get("endpoint"),
                "method": parsed_response.get("method"),
                "params": parsed_response.get("params", {}),
                "missing_required": parsed_response.get("missing_required", [])
            }
            
            return validated_response
            
        except Exception as e:
            print(f"Error parsing router response: {e}")
            return {
                "intent": "CONVERSATION",
                "confidence": 0.5,
                "reasoning": f"Parse error: {str(e)}",
                "endpoint": None,
                "method": None,
                "params": {},
                "missing_required": []
            }

    def handle_conversation(self, user_query, detected_language, sentiment_result):
        """Handle conversational responses"""
        try:
            result = self.conversation_chain.invoke({
                "user_query": user_query,
                "detected_language": detected_language,
                "sentiment_analysis": json.dumps(sentiment_result),
                "conversation_history": self.get_conversation_context(self.user_id)
            })
            
            return result["text"].strip()
            
        except Exception as e:
            # Fallback response
            if detected_language == "arabic":
                return "أعتذر، واجهت مشكلة في المعالجة. كيف يمكنني مساعدتك؟"
            else:
                return "I apologize, I encountered a processing issue. How can I help you?"

    async def backend_call(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Make async API call to backend with retry logic"""
        endpoint_url = data.get('endpoint')
        endpoint_method = data.get('method')
        endpoint_params = data.get('params', {}).copy()

        print(f"🔗 Making API call to {endpoint_method} {self.BASE_URL + endpoint_url} with params: {endpoint_params}")
        
        # Inject patient_id if needed
        if 'patient_id' in endpoint_params:
            endpoint_params['patient_id'] = self.user_id
        
        retries = 0
        while retries < self.max_retries:
            try:
                if endpoint_method.upper() == 'GET':
                    response = await http_client.get(
                        self.BASE_URL + endpoint_url,
                        params=endpoint_params,
                        headers=self.headers
                    )
                else:
                    response = await http_client.request(
                        endpoint_method.upper(),
                        self.BASE_URL + endpoint_url,
                        json=endpoint_params,
                        headers=self.headers
                    )
                
                response.raise_for_status()
                return response.json()
                
            except httpx.HTTPError as e:
                retries += 1
                if retries >= self.max_retries:
                    return {
                        "error": "Backend API call failed after multiple retries",
                        "details": str(e),
                        "status_code": getattr(e.response, 'status_code', None) if hasattr(e, 'response') else None
                    }
                
                await asyncio.sleep(self.retry_delay)

    async def handle_api_action(self, user_query: str, detected_language: str, 
                              sentiment_result: Dict, keywords: List[str], 
                              router_data: Dict) -> Dict[str, Any]:
        """Handle API-based actions using router data"""
        try:
            # Inject patient_id if needed
            if 'patient_id' in router_data['params']:
                router_data['params']['patient_id'] = self.user_id
            else:
                router_data['params']['patient_id'] = self.user_id
            
            print(f"🔍 Final API call data: {router_data}")

            # Make backend API call
            api_response = await self.backend_call(router_data)
            
            print("🔗 API response received:", api_response)
            
            # Generate user-friendly response using thread pool for CPU-bound LLM operation
            loop = asyncio.get_event_loop()
            user_response_result = await loop.run_in_executor(
                thread_pool,
                lambda: self.api_response_chain.invoke({
                    "user_query": user_query,
                    "api_response": json.dumps(api_response, indent=2),
                    "detected_language": detected_language,
                    "sentiment_analysis": json.dumps(sentiment_result),
                })
            )

            print("🔗 Final user response:", user_response_result["text"].strip())
            
            return {
                "response": user_response_result["text"].strip(),
                "api_data": api_response,
                "routing_info": router_data
            }
            
        except Exception as e:
            # Fallback error response
            if detected_language == "arabic":
                error_msg = "أعتذر، لم أتمكن من معالجة طلبك. يرجى المحاولة مرة أخرى أو صياغة السؤال بطريقة مختلفة."
            else:
                error_msg = "I apologize, I couldn't process your request. Please try again or rephrase your question."
            
            return {
                "response": error_msg,
                "api_data": {"error": str(e)},
                "routing_info": None
            }

    async def chat(self, user_message: str, user_id: str = None) -> ChatResponse:
        """Main chat method that handles user messages with async support"""
        start_time = time.time()
        
        # Use provided user_id or default
        user_id = user_id or self.user_id
        
        # Check rate limiting
        if not await self._check_rate_limit():
            return ChatResponse(
                response_id=str(time.time()),
                response_type="conversation",
                message="I'm currently processing too many requests. Please try again in a moment.",
                api_call_made=False,
                language="english"
            )
        
        # Check for exit commands
        if user_message.lower().strip() in ['quit', 'exit', 'خروج', 'bye', 'goodbye']:
            if self.detect_language(user_message) == "arabic":
                return ChatResponse(
                    response_id=str(time.time()),
                    response_type="conversation",
                    message="مع السلامة! أتمنى لك يوماً سعيداً. 👋",
                    language="arabic"
                )
            else:
                return ChatResponse(
                    response_id=str(time.time()),
                    response_type="conversation",
                    message="Goodbye! Have a great day! 👋",
                    language="english"
                )
        
        try:
            print(f"\n{'='*50}")
            print(f"🔍 Processing: '{user_message}'")
            print(f"{'='*50}")
            
            # Step 1: Language and sentiment analysis (CPU-bound operations in thread pool)
            loop = asyncio.get_event_loop()
            detected_language = await loop.run_in_executor(
                thread_pool, self.detect_language, user_message
            )
            sentiment_result = await loop.run_in_executor(
                thread_pool, self.analyze_sentiment, user_message
            )
            keywords = await loop.run_in_executor(
                thread_pool, self.extract_keywords, user_message
            )
            
            print(f"🌐 Detected Language: {detected_language}")
            print(f"😊 Sentiment: {sentiment_result}")
            print(f"🔑 Keywords: {keywords}")
            
            # Step 2: Router Chain (CPU-bound LLM operation in thread pool)
            print(f"\n🤖 Running Router Chain...")
            router_result = await loop.run_in_executor(
                thread_pool,
                lambda: self.router_chain.invoke({
                    "user_query": user_message,
                    "detected_language": detected_language,
                    "extracted_keywords": json.dumps(keywords),
                    "sentiment_analysis": json.dumps(sentiment_result),
                    "conversation_history": self.get_conversation_context(user_id),
                    "endpoints_documentation": json.dumps(self.endpoints_documentation, indent=2),
                    "current_datetime": datetime.now().strftime('%Y-%m-%dT%H:%M:%S'),
                    "timezone": "UTC",
                    "current_day_name": datetime.now().strftime('%A'),
                })
            )
            
            # Parse router response
            router_data = await loop.run_in_executor(
                thread_pool, self.parse_router_response, router_result["text"]
            )
            print(f"🎯 Router Decision: {router_data}")
            
            # Step 3: Handle based on intent
            if router_data["intent"] == "CONVERSATION" and router_data['endpoint'] == '':
                print(f"\n💬 Handling as CONVERSATION")
                response_text = await loop.run_in_executor(
                    thread_pool,
                    lambda: self.handle_conversation(user_message, detected_language, sentiment_result)
                )
                
                # Add to conversation history
                self.add_to_history(user_id, user_message, response_text, "conversation")
                
                return ChatResponse(
                    response_id=str(time.time()),
                    response_type="conversation",
                    message=response_text,
                    api_call_made=False,
                    language=detected_language,
                    api_data=None
                )
                
            elif router_data["intent"] == "API_ACTION":
                print(f"\n🔗 Handling as API_ACTION")
                
                # Handle API action
                api_result = await self.handle_api_action(
                    user_message, detected_language, sentiment_result, keywords, router_data
                )
                
                # Add to conversation history
                self.add_to_history(user_id, user_message, api_result["response"], "api_action")
                
                return ChatResponse(
                    response_id=str(time.time()),
                    response_type="api_action",
                    message=api_result["response"],
                    api_call_made=True,
                    language=detected_language
                )
            
            else:
                # Fallback for unknown intent
                print(f"⚠️ Unknown intent: {router_data['intent']}")
                fallback_response = await loop.run_in_executor(
                    thread_pool,
                    lambda: self.handle_conversation(user_message, detected_language, sentiment_result)
                )
                
                return ChatResponse(
                    response_id=str(time.time()),
                    response_type="conversation",
                    message=fallback_response,
                    api_call_made=False,
                    language=detected_language
                )
                
        except Exception as e:
            print(f"❌ Error in chat method: {str(e)}")
            print(f"❌ Traceback: {traceback.format_exc()}")
            
            # Fallback error response
            if self.detect_language(user_message) == "arabic":
                error_message = "أعتذر، حدث خطأ في معالجة رسالتك. يرجى المحاولة مرة أخرى."
            else:
                error_message = "I apologize, there was an error processing your message. Please try again."
            
            return ChatResponse(
                response_id=str(time.time()),
                response_type="conversation",
                message=error_message,
                api_call_made=False,
                language=self.detect_language(user_message)
            )
        
        finally:
            end_time = time.time()
            print(f"⏱️ Processing time: {end_time - start_time:.2f} seconds")

    async def run_interactive_chat(self):
        """Run the interactive chat interface"""
        try:
            while True:
                try:
                    # Get user input
                    user_input = input("\n👤 You: ").strip()
                    
                    if not user_input:
                        continue
                    
                    # Process the message
                    response = await self.chat(user_input)
                    
                    # Display the response
                    print(f"\n🤖 Bot: {response.message}")
                    
                    # Check for exit
                    if user_input.lower() in ['quit', 'exit', 'خروج', 'bye', 'goodbye']:
                        break
                        
                except KeyboardInterrupt:
                    print("\n\n👋 Chat interrupted. Goodbye!")
                    break
                except EOFError:
                    print("\n\n👋 Chat ended. Goodbye!")
                    break
                except Exception as e:
                    print(f"\n❌ Error: {e}")
                    continue
                    
        except Exception as e:
            print(f"❌ Fatal error in chat interface: {e}")

    def clear_history(self):
        """Clear conversation history"""
        self.conversation_history = []
        print("🗑️ Conversation history cleared.")


def main():
    """Main function to run the healthcare chatbot"""
    try:
        print("🚀 Starting Healthcare Chatbot...")
        chatbot = HealthcareChatbot()
        chatbot.run_interactive_chat()
        
    except KeyboardInterrupt:
        print("\n\n👋 Shutting down gracefully...")
    except Exception as e:
        print(f"❌ Fatal error: {e}")
        print(f"❌ Traceback: {traceback.format_exc()}")


if __name__ == "__main__":
    main()


from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Dict, Any, Optional


# FastAPI application setup
app = FastAPI(
    title="Healthcare AI Assistant",
    description="An AI-powered healthcare assistant that handles appointment booking and queries",
    version="1.0.0"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize the AI agent
agent = HealthcareChatbot()

class QueryRequest(BaseModel):
    query: str
    user_id: Optional[str] = None

@app.post("/query")
async def process_query(request: QueryRequest):
    """
    Process a user query and return a response
    """
    try:
        response = await agent.chat(request.query, request.user_id)
        return response.dict()
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health_check():
    """
    Health check endpoint
    """
    return {"status": "healthy", "service": "healthcare-ai-assistant"}

@app.get("/")
async def root():
    return {"message": "Hello World"}

@app.on_event("startup")
async def startup_event():
    """Initialize resources on startup"""
    agent._initialize_http_client()

@app.on_event("shutdown")
async def shutdown_event():
    """Cleanup resources on shutdown"""
    await agent._close_http_client()
    thread_pool.shutdown(wait=True)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000, workers=4)