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import streamlit as st
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)
# app.py
import os
from flask import Flask, request, jsonify, render_template
import librosa
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
import joblib
app = Flask(__name__)
# Load the trained model
model = tf.keras.models.load_model('model.h5')
# Load the scaler - you'll need to save this during training
# Add this after your training code:
# joblib.dump(scaler, 'scaler.pkl')
scaler = joblib.load('scaler.pkl')
def extract_features(audio_file):
y, sr = librosa.load(audio_file)
mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr)
spectral_rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
zero_crossing_rate = librosa.feature.zero_crossing_rate(y)
features = np.concatenate([
np.mean(mfccs, axis=1),
[np.mean(spectral_centroid)],
[np.mean(spectral_bandwidth)],
[np.mean(spectral_rolloff)],
[np.mean(zero_crossing_rate)]
])
return features.reshape(1, -1)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
try:
if 'file' not in request.files:
return jsonify({'error': 'No file provided'}), 400
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'No file selected'}), 400
if not file.filename.endswith('.wav'):
return jsonify({'error': 'Please upload a WAV file'}), 400
# Extract features
features = extract_features(file)
# Scale features
scaled_features = scaler.transform(features)
# Make prediction
prediction = model.predict(scaled_features)
gender = "Female" if prediction[0][0] < 0.5 else "Male"
confidence = float(prediction[0][0] if prediction[0][0] > 0.5 else 1 - prediction[0][0])
return jsonify({
'prediction': gender,
'confidence': f"{confidence * 100:.2f}%"
})
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
app.run(debug=True) |