app.py

import os
import io
import uuid
import time
import json
import logging
import tempfile
import threading

from flask import Flask, request, jsonify, send_file
from transformers import pipeline
from gtts import gTTS
from pydub import AudioSegment

# ================= CONFIG =================

TEMP_AUDIO_DIR = "/tmp/audio"
os.makedirs(TEMP_AUDIO_DIR, exist_ok=True)

STT_MODEL = "openai/whisper-tiny"
LLM_MODEL = "google/flan-t5-base"

MAX_AUDIO_SECONDS = 10
MAX_TEXT_LEN = 200

CLEANUP_INTERVAL = 300      # seconds
FILE_EXPIRE_TIME = 600     # seconds

# ================= LOG =================

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s | %(levelname)s | %(message)s"
)
logger = logging.getLogger(__name__)

# ================= APP =================

app = Flask(__name__)
app.config["TEMP_AUDIO_DIR"] = TEMP_AUDIO_DIR

# ================= LOAD MODELS =================

logger.info("Loading STT model...")
stt_pipeline = pipeline(
    "automatic-speech-recognition",
    model=STT_MODEL,
    device="cpu"
)

logger.info("Loading LLM model...")
llm_pipeline = pipeline(
    "text2text-generation",
    model=LLM_MODEL,
    device="cpu"
)

logger.info("Models loaded successfully")

# ================= UTILS =================

def generate_tts_audio(text: str) -> bytes:
    """
    Generate WAV 16kHz mono audio from text
    """
    try:
        text = text.replace("\n", " ").strip()
        if not text:
            text = "I understand."

        text = text[:MAX_TEXT_LEN]
        logger.info(f"TTS: {text}")

        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as wav_file:
            mp3_path = wav_file.name.replace(".wav", ".mp3")

            tts = gTTS(text=text, lang="en")
            tts.save(mp3_path)

            audio = AudioSegment.from_file(mp3_path)
            audio = audio.set_frame_rate(16000).set_channels(1)
            audio.export(wav_file.name, format="wav")

            with open(wav_file.name, "rb") as f:
                wav_data = f.read()

        os.remove(mp3_path)
        os.remove(wav_file.name)

        return wav_data

    except Exception as e:
        logger.error(f"TTS error: {e}", exc_info=True)
        return b""


def cleanup_temp_files():
    while True:
        try:
            now = time.time()
            for filename in os.listdir(TEMP_AUDIO_DIR):
                path = os.path.join(TEMP_AUDIO_DIR, filename)
                if os.path.isfile(path):
                    if now - os.path.getmtime(path) > FILE_EXPIRE_TIME:
                        os.remove(path)
        except Exception as e:
            logger.warning(f"Cleanup error: {e}")

        time.sleep(CLEANUP_INTERVAL)


# ================= ROUTES =================

@app.route("/health", methods=["GET"])
def health():
    return jsonify({
        "status": "ok",
        "stt": STT_MODEL,
        "llm": LLM_MODEL
    })


@app.route("/process_audio", methods=["POST"])
def process_audio():
    try:
        if "audio" not in request.files:
            return jsonify({"error": "No audio file"}), 400

        audio_file = request.files["audio"]
        raw_audio = audio_file.read()

        if len(raw_audio) < 1000:
            return jsonify({"error": "Audio too short"}), 400

        # ================= STT =================
        logger.info("Running STT...")
        stt_result = stt_pipeline(
            raw_audio,
            sampling_rate=16000
        )

        user_text = stt_result.get("text", "").strip()
        logger.info(f"User said: {user_text}")

        if not user_text:
            user_text = "Hello"

        # ================= LLM =================
        logger.info("Running LLM...")
        llm_result = llm_pipeline(
            user_text,
            max_new_tokens=64,
            do_sample=False
        )

        answer = llm_result[0]["generated_text"]
        logger.info(f"Answer: {answer}")

        # ================= TTS =================
        audio_response = generate_tts_audio(answer)

        if not audio_response:
            return jsonify({"error": "TTS failed"}), 500

        file_id = str(uuid.uuid4())
        filepath = os.path.join(TEMP_AUDIO_DIR, f"{file_id}.wav")

        with open(filepath, "wb") as f:
            f.write(audio_response)

        return send_file(
            filepath,
            mimetype="audio/wav",
            as_attachment=False,
            download_name="response.wav"
        )

    except Exception as e:
        logger.error(f"Processing error: {e}", exc_info=True)
        return jsonify({"error": "Internal error"}), 500


# ================= STARTUP =================

if __name__ == "__main__":
    threading.Thread(target=cleanup_temp_files, daemon=True).start()

    app.run(
        host="0.0.0.0",
        port=7860,
        threaded=True
    )