Update app.py

app.py

@@ -1,61 +1,65 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-import librosa
-import librosa.util
-
-# Define your predict_class function
-def predict_class(file_path, model, labels):
-    # Extract MFCC features
-    y, sr = librosa.load(file_path, sr=None)
-    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
-    
-    # Pad or truncate to 100 frames along axis 1
-    mfcc = librosa.util.fix_length(mfcc, size=100, axis=1)
-
-    # Ensure mfcc has shape (13, 100)
-    if mfcc.shape[0] != 13:
-        mfcc = librosa.util.fix_length(mfcc, size=13, axis=0)
-
-    # Add batch and channel dimensions
-    mfcc = mfcc[np.newaxis, ..., np.newaxis]  # Shape: (1, 13, 100, 1)
-
-    # Predict using the model
-    prediction = model.predict(mfcc)
-    predicted_class = labels[np.argmax(prediction)]
-    return predicted_class
-
-# Load your pre-trained model
-model = tf.keras.models.load_model("voice_classification_modelm.h5")
-
-# Define the class labels based on your folder names
-labels = [
-    "all_vowels_healthy",
-    "allvowels_functional",
-    "allvowels_laryngitis",
-    "allvowels_lukoplakia",
-    "allvowels_psychogenic",
-    "allvowels_rlnp",
-    "allvowels_sd"
-]
-
-# Define the Gradio function
-def classify_audio(audio_file):
-    try:
-        predicted_class = predict_class(audio_file, model, labels)
-        return f"Predicted Class: {predicted_class}"
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=classify_audio,
-    inputs=gr.Audio(source="upload", type="filepath", label="Upload an Audio File"),
-    outputs=gr.Textbox(label="Predicted Class"),
-    title="Voice Classification",
-    description="Upload an audio file to classify its voice type.",
-    examples=["example_audio.wav"]  # Replace with paths to sample audio files
-)
-
-# Launch the app
-interface.launch()
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import librosa
+import librosa.util
+import pickle
+from sklearn.preprocessing import LabelEncoder
+
+# Feature extraction function
+def extract_features(file_path):
+    try:
+        # Load the audio file
+        y, sr = librosa.load(file_path, sr=8000)  # Resample to 8kHz
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        
+        # Pad or truncate to 100 frames along axis 1
+        mfcc = librosa.util.fix_length(mfcc, size=100, axis=1)
+
+        # Ensure the shape is (13, 100)
+        if mfcc.shape[0] != 13:
+            mfcc = librosa.util.fix_length(mfcc, size=13, axis=0)
+
+        return {"mfcc": mfcc}
+    except Exception as e:
+        raise ValueError(f"Error in feature extraction: {str(e)}")
+
+# Prediction function
+def predict_class(file_path, model, label_encoder):
+    try:
+        features = extract_features(file_path)
+        mfcc = features["mfcc"]
+
+        # Add batch and channel dimensions for model compatibility
+        mfcc = mfcc[np.newaxis, ..., np.newaxis]  # Shape: (1, 13, 100, 1)
+
+        # Make prediction
+        prediction = model.predict(mfcc)
+        predicted_class = label_encoder.inverse_transform([np.argmax(prediction)])
+        return f"Predicted Class: {predicted_class[0]}"
+    except Exception as e:
+        return f"Error in prediction: {str(e)}"
+
+# Load the pre-trained model
+model = tf.keras.models.load_model("voice_classification_modelm.h5")
+
+# Load the label encoder
+with open("label_encoder.pkl", "rb") as f:
+    label_encoder = pickle.load(f)
+
+# Define the Gradio function
+def classify_audio(audio_file):
+    return predict_class(audio_file, model, label_encoder)
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(source="upload", type="filepath", label="Upload an Audio File"),
+    outputs=gr.Textbox(label="Predicted Class"),
+    title="Voice Disorder Classification",
+    description="Upload an audio file to classify its voice type (e.g., healthy or various disorder types).",
+    examples=["example_audio.wav"],  # Replace with paths to example audio files
+)
+
+# Launch the Gradio app
+interface.launch()