Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the trained model
+model = tf.keras.models.load_model("MobileNet_model.h5")  # Ensure the model file is in the same directory
+
+# Define class names from your dataset
+class_names = ["Fake", "Low", "Medium", "High"]  # Update based on test_generator.class_indices.keys()
+
+# Image Preprocessing
+img_size = (128, 128)  # Same as used in test_generator
+
+def preprocess_image(image):
+    image = image.resize(img_size)  # Resize to (128,128)
+    image = np.array(image) / 255.0  # Normalize as done in ImageDataGenerator (rescale=1./255)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Prediction Function
+def predict(image):
+    image = preprocess_image(image)
+    predictions = model.predict(image)
+    predicted_class = np.argmax(predictions, axis=1)[0]  # Get the predicted class index
+    confidence_scores = {class_names[i]: float(predictions[0][i]) for i in range(len(class_names))}  # Get probability scores
+    
+    return {"Predicted Class": class_names[predicted_class], "Confidence Scores": confidence_scores}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.JSON(),  # Returns class and confidence scores
+    title="Waste Classification Model",
+    description="Upload an image to classify it into one of four categories: Fake, Low, Medium, or High."
+)
+
+if __name__ == "__main__":
+    interface.launch()