Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import librosa
+import tensorflow as tf
+import random
+import warnings
+import joblib
+warnings.filterwarnings("ignore")
+
+# Load model and label encoder
+model = tf.keras.models.load_model("final_model.h5")
+label_encoder = joblib.load("le.pkl")
+
+# Your feature extractor
+def extract_features(y, sr):
+    try:
+        stft = np.abs(librosa.stft(y))
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)
+        mfcc_mean = np.mean(mfcc.T, axis=0)
+        chroma = librosa.feature.chroma_stft(S=stft, sr=sr)
+        chroma_mean = np.mean(chroma.T, axis=0)
+        contrast = librosa.feature.spectral_contrast(S=stft, sr=sr)
+        contrast_mean = np.mean(contrast.T, axis=0)
+        zcr = librosa.feature.zero_crossing_rate(y)
+        zcr_mean = np.mean(zcr)
+        rmse = librosa.feature.rms(y=y)
+        rmse_mean = np.mean(rmse)
+
+        return np.hstack([mfcc_mean, chroma_mean, contrast_mean, zcr_mean, rmse_mean])
+    
+    except Exception as e:
+        print(f"Error extracting features: {e}")
+        return np.zeros(61)
+
+# Prediction function
+def predict_emotion(audio):
+    y, sr = librosa.load(audio, sr=None)
+    features = extract_features(y, sr)
+    features = features.reshape(1, -1)  # Make it 2D
+    prediction = model.predict(features)
+    predicted_label = label_encoder.inverse_transform([np.argmax(prediction)])[0]
+    return predicted_label
+
+examples = [["happy.wav"], ["sad.wav"], ["angry.wav"]]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Audio(type="filepath"),
+    outputs="label",
+    title="🎙️ Emotion Recognition from Audio",
+    description="Upload or record your voice to predict the emotion using a TensorFlow model trained on audio features.",
+    examples=examples
+)
+
+interface.launch()