que pesner vosu de ce code javascript

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const express = require('express');
const cors = require('cors');
const multer = require('multer');
const sqlite3 = require('sqlite3');
const AudioFile = require('./models/AudioFile');

const app = express();
const port = 3000;

app.use(cors());
app.use(express.json());

const storage = multer.diskStorage({
  destination: './uploads/',
  filename: (req, file, cb) => {
    cb(null, Date.now() + '-' + file.originalname);
  }
});

const upload = multer({ storage: storage });

const db = new sqlite3.Database('./database.sqlite');

app.post('/upload', upload.single('audio'), async (req, res) => {
  try {
    const file = req.file;
    const audioFile = new AudioFile(file.filename);
    await audioFile.save();
    res.json({ success: true, fileId: audioFile.id });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.post('/analyze', async (req, res) => {
  try {
    const { fileId } = req.body;
    const audioFile = await AudioFile.findById(fileId);
    const results = await audioFile.analyze();
    res.json(results);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.listen(port, () => {
  console.log(`Serveur démarré sur le port ${port}`);
});
backend/models/AudioFile.js:
javascript

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const sqlite3 = require('sqlite3');
const db = new sqlite3.Database('../database.sqlite');

class AudioFile {
  constructor(filename) {
    this.filename = filename;
  }

  async save() {
    return new Promise((resolve, reject) => {
      db.run(
        'INSERT INTO audio_files (filename, created_at) VALUES (?, CURRENT_TIMESTAMP)',
        [this.filename],
        function(err) {
          if (err) reject(err);
          this.id = this.lastID;
          resolve(this);
        }
      );
    });
  }

  static async findById(id) {
    return new Promise((resolve, reject) => {
      db.get(
        'SELECT * FROM audio_files WHERE id = ?',
        [id],
        (err, row) => {
          if (err) reject(err);
          if (!row) reject(new Error('File not found'));
          resolve(new AudioFile(row.filename));
        }
      );
    });
  }

  async analyze() {
    // Intégration avec audio_service.py
    // Simulé pour l'exemple
    return {
      deepfake_probability: Math.random(),
      authenticity_score: Math.random(),
      emotional_analysis: {
        primary_emotion: ['joie', 'colère', 'tristesse', 'peur'][Math.floor(Math.random() * 4)],
        confidence: Math.random()
      }
    };
  }
}

module.exports = AudioFile;
audio_service/audio_service.py:
python

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import tensorflow as tf
import numpy as np
import librosa
from pathlib import Path

class AudioAnalyzer:
    def __init__(self):
        self.model = tf.keras.models.load_model('models/deepfake_detection_model.h5')
        self.emotion_analyzer = EmotionalAnalyzer()
        
    def analyze_file(self, file_path):
        audio_features = self._extract_features(file_path)
        deepfake_score = self._detect_deepfake(audio_features)
        emotional_analysis = self.emotion_analyzer.analyze(audio_features)
        
        return {
            'deepfake_probability': float(deepfake_score),
            'emotional_analysis': emotional_analysis
        }
        
    def _extract_features(self, file_path):
        y, sr = librosa.load(file_path)
        mfcc = librosa.feature.mfcc(y=y, sr=sr)
        return mfcc
        
    def _detect_deepfake(self, features):
        prediction = self.model.predict(features[np.newaxis, ..., np.newaxis])
        return float(prediction[0][0])

class EmotionalAnalyzer:
    def analyze(self, features):
        # Simulation d'analyse émotionnelle
        emotions = ['joie', 'colère', 'tristesse', 'peur']
        scores = np.random.random(len(emotions))
        scores = scores / np.sum(scores)
        
        return {
            'primary_emotion': emotions[np.argmax(scores)],
            'confidence': float(np.max(scores)),
            'scores': dict(zip(emotions, scores.tolist()))
        }
audio_service/train_model.py:
python

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import tensorflow as tf
from pathlib import Path
import numpy as np

def create_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(20, 87, 1)),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Conv2D(64, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    
    model.compile(
        optimizer='adam',
        loss='binary_crossentropy',
        metrics=['accuracy']
    )
    
    return model

def train_model():
    model = create_model()
    # Chargement et prétraitement des données
    # Entraînement du modèle
    model.save('models/deepfake_detection_model.h5')

if __name__ == "__main__":
    train_model()