# -*- coding: utf-8 -*-
import os
import tempfile
from flask import Flask, request, jsonify
from faster_whisper import WhisperModel

# ==========================================================
# Configuration
# ==========================================================
MODEL_PATH = os.environ.get("WHISPER_MODEL", "/models/whisper")
DEVICE = os.environ.get("WHISPER_DEVICE", "cuda")
COMPUTE_TYPE = "float16" if DEVICE == "cuda" else "int8"
PORT = int(os.environ.get("PORT", 5001))

app = Flask(__name__)

# ==========================================================
# Load Whisper Model
# ==========================================================
print(f"[stt] Loading model from {MODEL_PATH} on {DEVICE} ({COMPUTE_TYPE})")
try:
    model = WhisperModel(MODEL_PATH, device=DEVICE, compute_type=COMPUTE_TYPE)
    print("[stt] Whisper model loaded successfully")
except Exception as e:
    print(f"[stt] Fatal error loading model: {e}")
    exit(1)

# ==========================================================
# Speech To Text Endpoint
# ==========================================================
@app.route("/transcribe", methods=["POST"])
def transcribe():
    if "audio" not in request.files:
        return jsonify({"error": "audio file missing"}), 400

    audio_file = request.files["audio"]
    
    # استفاده از NamedTemporaryFile با مدیریت بهتر برای محیط داکر
    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
    audio_path = tmp_file.name
    tmp_file.close()

    try:
        audio_file.save(audio_path)

        # اجرای اینفرنس با تنظیمات بهینه شده
        segments, info = model.transcribe(
            audio_path,
            language="fa",
            beam_size=10,
            vad_filter=True,
            vad_parameters=dict(min_silence_duration_ms=500) # بهبود تشخیص سکوت
        )

        text = " ".join([segment.text.strip() for segment in segments])
        
        return jsonify({
            "text": text.strip(),
            "language": info.language
        })

    except Exception as e:
        print(f"[stt] Error during transcription: {e}")
        return jsonify({"error": str(e)}), 500

    finally:
        if os.path.exists(audio_path):
            os.remove(audio_path)

# ==========================================================
# Health Check
# ==========================================================
@app.route("/health", methods=["GET"])
def health():
    return jsonify({"status": "ok"})

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