app.py

from fastapi import FastAPI, Depends, HTTPException, Header, File, UploadFile
from pydantic import BaseModel
from typing import List, Dict, Optional
from email_ai import initialize_conversation, intro_message, get_chat_model_completions

import google.generativeai as genai
import os
from dotenv import load_dotenv

import speech_recognition as sr
from io import BytesIO
import wave

# 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")

app = FastAPI()

# Initialize speech recognizer
recognizer = sr.Recognizer()

# 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

# 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")
    
@app.get("/init", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
async def get_conversation():
    global llm, chroma_retriever, conversation_bot

    conversation_bot = [Message(role="bot", content=intro_message)]
    llm, chroma_retriever = initialize_conversation()

    return ChatResponse(
        response=intro_message,
        conversation=conversation_bot
    )

@app.post("/chat", response_model=ChatResponse, dependencies=[Depends(verify_api_key)])
async def send_message(request: ChatRequest):
    global conversation_bot
    
    conversation_bot.append(Message(role="user", content=request.message))
    response_assistant = get_chat_model_completions(llm, chroma_retriever, request.message)
    conversation_bot.append(Message(role="bot", content=response_assistant.content))
    
    return ChatResponse(
        response=response_assistant.content,
        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):

    global conversation_bot

    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)
        
        conversation_bot.append(Message(role="user", content=text))
        response_assistant = get_chat_model_completions(llm, chroma_retriever, text)
        conversation_bot.append(Message(role="bot", content=response_assistant.content))
        # print('response_assistant.content')
        return {
            "transcribed_text": text,
            "response": response_assistant.content
        }
    
    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"}

@app.post("/reset")
async def reset_conversation():
    global conversation_bot, llm, chroma_retriever
    conversation_bot = [{'bot': intro_message}]
    llm, chroma_retriever = initialize_conversation()
    return {"status": "conversation reset"}

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