Delete app.py

app.py

@@ -1,192 +0,0 @@
-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
-    )