from fastapi import FastAPI, Request, Depends, HTTPException, Header, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Optional
from helpmate_ai import get_system_msg, retreive_results, rerank_with_cross_encoder, generate_response, intro_message
import google.generativeai as genai
import os
from dotenv import load_dotenv
import re

import speech_recognition as sr
from io import BytesIO
import wave

import google.generativeai as genai


# Load environment variables
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=gemini_api_key)

# Define a secret API key (use environment variables in production)
API_KEY = os.getenv("API_KEY")

# Initialize FastAPI app
app = FastAPI()

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

# Pydantic models for request/response validation
class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    message: str

class ChatResponse(BaseModel):
    response: str
    conversation: List[Message]

class Report(BaseModel):
    response: str
    message: str
    timestamp: str

# Initialize conversation and model
conversation_bot = []
conversation = get_system_msg()
model = genai.GenerativeModel("gemini-1.5-flash", system_instruction=conversation)

# Initialize speech recognizer
recognizer = sr.Recognizer()

# Dependency to check the API key
async def verify_api_key(x_api_key: str = Header(...)):
    if x_api_key != API_KEY:
        raise HTTPException(status_code=403, detail="Unauthorized")
    
def get_gemini_completions(conversation: str) -> str:
    response = model.generate_content(conversation)
    return response.text

# @app.get("/secure-endpoint", dependencies=[Depends(verify_api_key)])
# async def secure_endpoint():
#     return {"message": "Access granted!"}

# Initialize conversation endpoint
@app.get("/init", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
async def initialize_chat():
    global conversation_bot

    # conversation = "Hi" 
    # introduction = get_gemini_completions(conversation)
    conversation_bot = [Message(role="bot", content=intro_message)]
    return ChatResponse(
        response=intro_message,
        conversation=conversation_bot
    )

# Chat endpoint
@app.post("/chat", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
async def chat(request: ChatRequest):
    global conversation_bot
    
    # Add user message to conversation
    user_message = Message(role="user", content=request.message)
    conversation_bot.append(user_message)
    
    # Generate response
    results_df = retreive_results(request.message)
    top_docs = rerank_with_cross_encoder(request.message, results_df)
    messages = generate_response(request.message, top_docs)
    response_assistant = get_gemini_completions(messages)
    # formatted_response = format_rag_response(response_assistant)
    
    # Add bot response to conversation
    bot_message = Message(role="bot", content=response_assistant)
    conversation_bot.append(bot_message)
    
    return ChatResponse(
        response=response_assistant,
        conversation=conversation_bot
    )

# Voice processing endpoint
@app.post("/process-voice")
async def process_voice(audio_file: UploadFile = File(...), dependencies=[Depends(verify_api_key)]):
# async def process_voice(name: str):

    try:
        # Read the audio file
        contents = await audio_file.read()
        audio_data = BytesIO(contents)

    
        # Convert audio to wav format for speech recognition
        with sr.AudioFile(audio_data) as source:
            audio = recognizer.record(source)
       
        # Perform speech recognition
        text = recognizer.recognize_google(audio)
        # print(text)
        
        # Process the text through the chat pipeline
        results_df = retreive_results(text)
        top_docs = rerank_with_cross_encoder(text, results_df)
        messages = generate_response(text, top_docs)
        response_assistant = get_gemini_completions(messages)
        
        return {
            "transcribed_text": text,
            "response": response_assistant
        }
    
    except Exception as e:
        return {"error": f"Error processing voice input: {str(e)}"}

@app.post("/report")
async def handle_feedback(
    request: Report,
    dependencies=[Depends(verify_api_key)]
):
    # if x_api_key != VALID_API_KEY:
    #     raise HTTPException(status_code=403, detail="Invalid API key")
        
    # Here you can store the feedback in your database
    # For example:
    # await db.store_feedback(message, is_positive)
    
    return {"status": "success"}
    
# Reset conversation endpoint
@app.post("/reset", dependencies=[Depends(verify_api_key)])
async def reset_conversation():
    global conversation_bot, conversation
    conversation_bot = []
    # conversation = "Hi"
    # introduction = get_gemini_completions(conversation)
    conversation_bot.append(Message(role="bot", content=intro_message))
    return {"status": "success", "message": "Conversation reset"}

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