Create app.py

app.py

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+import gradio as gr
+import librosa
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+# Load trained model
+model = keras.models.load_model("engine_sound_model.h5")
+
+# Class labels
+labels = ["normal", "faulty", "background_noise", "unknown"]
+
+def predict_engine_sound(audio_file):
+    y, sr = librosa.load(audio_file, sr=22050)
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    features = np.mean(mfccs.T, axis=0)
+    features = np.expand_dims(features, axis=0)
+
+    prediction = model.predict(features)
+    return labels[np.argmax(prediction)]
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict_engine_sound,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="Engine Sound Fault Detector",
+    description="Upload an engine sound and the model will classify it as normal, faulty, or background noise."
+)
+
+# Launch the Gradio app
+iface.launch()