app.py

from flask import Flask, request, jsonify, send_from_directory
from transformers import AutoProcessor, SeamlessM4Tv2Model
import numpy as np
import wave
import os
from huggingface_hub import InferenceClient, login
import logging
import torchaudio

# Configurer les logs de debug
logging.basicConfig(level=logging.INFO)

# Initialisation des variables pour le modèle et le processeur
model = None
processor = None

UPLOAD_FOLDER = "audio_files"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 
app = Flask(__name__, static_folder="front/dist", static_url_path="/")

@app.route("/")
def serve_react_app():
    return send_from_directory("front/dist", "index.html")

@app.route("/<path:path>")
def serve_static_files(path):
    return send_from_directory("dist", path)

@app.route("/load_model", methods=["POST"])
def load_model():
    global model, processor
    load = request.json.get("load", False)
    
    if load:
        processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")  # Remplacez "model_name" par le nom de votre modèle
        model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")  # Remplacez "model_name" par le nom de votre modèle
        return jsonify({"message": "Modèle chargé avec succès."}), 200
    else:
        return jsonify({"message": "Le modèle n'est pas chargé."}), 200

@app.route("/record", methods=["POST"])
def record_audio():
    if model is None or processor is None:
        return jsonify({"error": "Le modèle n'est pas chargé."}), 400
    
    file = request.files['audio']
    filename = os.path.join(UPLOAD_FOLDER, file.filename)
    file.save(filename)
    
    # Charger et traiter l'audio
    audio_data, orig_freq = torchaudio.load(filename)
    audio_inputs = processor(audios=audio_data, return_tensors="pt")
    output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
    translated_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
    
    return jsonify({"translated_text": translated_text})

@app.route("/text_to_speech", methods=["POST"])
def text_to_speech():
    data = request.get_json()
    text = data.get("text")
    src_lang = data.get("src_lang")
    tgt_lang = data.get("tgt_lang")
    
    text_inputs = processor(text=text, src_lang=src_lang, return_tensors="pt")
    audio_array = model.generate(**text_inputs, tgt_lang=tgt_lang)[0].cpu().numpy().squeeze()
    
    output_filename = os.path.join(UPLOAD_FOLDER, "output.wav")
    with wave.open(output_filename, "wb") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(16000)
        wf.writeframes((audio_array * 32767).astype(np.int16).tobytes())
    
    return jsonify({"audio_url": output_filename})