main.py

import os
import json
import httpx
import asyncio
import logging
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import edge_tts
import uvicorn
import base64

# إعدادات اللوج
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI()

# تفعيل CORS للاتصال مع Next.js
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

OLLAMA_URL = "http://localhost:11434/api/chat"

class ChatRequest(BaseModel):
    messages: list
    voice: str = "ar-SA-HamedNeural"
    rate: str = "+0%"

async def stream_text_and_voice(payload,messages, voice, rate):
    full_response_text = ""
    sentence_buffer = ""
    
    async with httpx.AsyncClient(timeout=None) as client:
        try:
            async with client.stream("POST", OLLAMA_URL, json=payload) as response:
                async for line in response.aiter_lines():
                    if not line: continue
                    chunk = json.loads(line)
                    token = chunk.get("message", {}).get("content", "")
                    
                    sentence_buffer += token
                    full_response_text += token

                    # Check for sentence end
                    if any(punct in token for punct in [".", "!", "?", "؟", "\n"]):
                        clean_text = sentence_buffer.strip()
                        print(clean_text)
                        if clean_text:
                            # 1. إنشاء كائن التواصل مع edge-tts
                            communicate = edge_tts.Communicate(clean_text, voice, rate=rate)
                            
                            # 2. تجميع البيانات الصوتية في الذاكرة (Memory)
                            audio_data = b""
                            async for chunk in communicate.stream():
                                if chunk["type"] == "audio":
                                    audio_data += chunk["data"]
                            
                            # 3. تحويل البيانات الصوتية الكاملة للجملة إلى Base64
                            if audio_data:
                                audio_base64 = base64.b64encode(audio_data).decode('utf-8')
                                
                                # 4. إرسالها كسطر JSON واحد للمتصفح
                                yield f'{{ "type": "audio", "data": "{audio_base64}" }}\n'
                                
                            sentence_buffer = "" # تصغير البفر لبدء جملة جديدة

                # Handle remaining text in buffer
                if sentence_buffer.strip():
                    communicate = edge_tts.Communicate(sentence_buffer.strip(), voice, rate=rate)
                    async for audio_chunk in communicate.stream():
                        if audio_chunk["type"] == "audio":
                            b64_data = base64.b64encode(audio_chunk["data"]).decode('utf-8')
                            yield json.dumps({"type": "audio", "data": b64_data}) + "\n"

            # THE IMPORTANT PART: Send the text message at the end
            yield json.dumps({
                "type": "final_text", 
                "content": full_response_text
            }) + "\n"

        except Exception as e:
            logger.error(f"Error: {e}")

@app.post("/stream-voice")
async def voice_engine(data: ChatRequest):
    payload = {
        "model": "qwen2.5:3b", # Or whatever model you are using
        "messages": data.messages,
        "stream": True,  # Crucial for streaming
        "options": {
            "temperature": 0.3,
            "top_p": 0.9,
        }
    }
    return StreamingResponse(
        stream_text_and_voice(payload,data.messages, data.voice, data.rate),
        media_type="audio/mpeg",
        headers={"Cache-Control":"no-cache"}
    )
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)