app.py

import os
import io
import json
import uuid
import wave
import tempfile
from datetime import datetime
from typing import Optional, Dict, Any
from pathlib import Path

from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import uvicorn
import requests
import numpy as np
from groq import Groq
import dotenv

# Load environment variables
dotenv.load_dotenv()

app = FastAPI(title="Voice AI Backend")

# CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Configure appropriately for production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Configuration
GROQ_MODEL = "whisper-large-v3-turbo"
AI_API_ENDPOINT = "https://nitinbot001-crop-rag-api.hf.space/api/query"
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

# Initialize Groq client
groq_client = Groq(api_key=GROQ_API_KEY) if GROQ_API_KEY else None

# Store conversation history (in production, use a database)
conversation_history = []

class TranscriptionResponse(BaseModel):
    success: bool
    user_query: str
    ai_response: str
    metadata: Dict[str, Any]
    session_id: str
    timestamp: str
    error: Optional[str] = None

class ConversationHistory(BaseModel):
    sessions: list

@app.get("/")
async def root():
    return {"message": "Voice AI Backend API", "status": "online"}

@app.post("/api/process-audio", response_model=TranscriptionResponse)
async def process_audio(audio: UploadFile = File(...)):
    """
    Process audio file: transcribe and get AI response
    """
    session_id = str(uuid.uuid4())
    timestamp = datetime.now().isoformat()
    
    try:
        # Validate file type
        if not audio.filename.endswith(('.wav', '.webm', '.mp3', '.m4a', '.ogg')):
            raise HTTPException(status_code=400, detail="Invalid audio format")
        
        # Read audio data
        audio_data = await audio.read()
        
        # Save temporary file for processing
        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
            # If it's webm (from browser), we need to save it as-is
            # Groq can handle webm directly
            if audio.filename.endswith('.webm'):
                tmp_file.write(audio_data)
                tmp_path = tmp_file.name
            else:
                # For wav files, write directly
                tmp_file.write(audio_data)
                tmp_path = tmp_file.name
        
        # Transcribe with Groq
        user_query = await transcribe_audio(tmp_path, audio.filename)
        
        # Get AI response
        ai_response = await get_ai_response(user_query)
        
        # Create metadata
        metadata = {
            "audio_size": len(audio_data),
            "audio_format": audio.filename.split('.')[-1],
            "transcription_model": GROQ_MODEL,
            "ai_endpoint": AI_API_ENDPOINT,
            "processing_time": datetime.now().isoformat(),
        }
        
        # Store in history
        conversation_history.append({
            "session_id": session_id,
            "timestamp": timestamp,
            "user_query": user_query,
            "ai_response": ai_response,
            "metadata": metadata
        })
        
        # Clean up
        os.unlink(tmp_path)
        
        return TranscriptionResponse(
            success=True,
            user_query=user_query,
            ai_response=ai_response,
            metadata=metadata,
            session_id=session_id,
            timestamp=timestamp
        )
        
    except Exception as e:
        return TranscriptionResponse(
            success=False,
            user_query="",
            ai_response="",
            metadata={},
            session_id=session_id,
            timestamp=timestamp,
            error=str(e)
        )

async def transcribe_audio(file_path: str, original_filename: str) -> str:
    """
    Transcribe audio using Groq Whisper
    """
    if not groq_client:
        raise HTTPException(status_code=500, detail="GROQ_API_KEY not configured")
    
    try:
        with open(file_path, "rb") as audio_file:
            transcription = groq_client.audio.transcriptions.create(
                file=(original_filename, audio_file.read()),
                model=GROQ_MODEL,
                response_format="text"
            )
        
        # Handle different response formats
        if hasattr(transcription, 'text'):
            text = transcription.text
        elif isinstance(transcription, dict):
            text = transcription.get('text', '')
        else:
            text = str(transcription)
        
        return text.strip()
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")

async def get_ai_response(query: str) -> str:
    """
    Get response from AI API
    """
    try:
        headers = {"Content-Type": "application/json"}
        payload = {"query": query}
        
        response = requests.post(
            AI_API_ENDPOINT,
            json=payload,
            headers=headers,
            timeout=30
        )
        response.raise_for_status()
        
        result = response.json()
        
        # Extract text from response (adjust based on actual API response format)
        if isinstance(result, dict):
            # Try different possible response keys
            ai_text = result.get('response', 
                      result.get('answer', 
                      result.get('text', 
                      result.get('message', str(result)))))
        else:
            ai_text = str(result)
        
        return ai_text
    
    except requests.exceptions.Timeout:
        return "I'm sorry, the AI service is taking too long to respond. Please try again."
    except Exception as e:
        return f"I encountered an error while processing your request: {str(e)}"

@app.get("/api/history", response_model=ConversationHistory)
async def get_history():
    """
    Get conversation history
    """
    return ConversationHistory(sessions=conversation_history[-20:])  # Last 20 conversations

@app.delete("/api/history")
async def clear_history():
    """
    Clear conversation history
    """
    global conversation_history
    conversation_history = []
    return {"message": "History cleared"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)