Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import builtins
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+
+# Ensure TensorFlow is available globally for the Lambda layer
+builtins.tf = tf
+
+# Define the function to replace the anonymous lambda from the model
+def expand_dims_func(x):
+    import tensorflow as tf
+    return tf.expand_dims(x, axis=1)
+
+# Load your trained model
+best_model_path = "best_model.h5"  # Ensure this file is uploaded to the Hugging Face Space
+best_model = load_model(best_model_path, custom_objects={"<lambda>": expand_dims_func})
+
+# Define class mapping (index -> class label)
+class_mapping = {0: "Early_Blight", 1: "Healthy", 2: "Late_Blight"}
+
+def classify_leaf(input_image):
+    """
+    Takes an input image (PIL format from Gradio), preprocesses it,
+    and returns the predicted class with probabilities.
+    """
+    # Resize and normalize the image
+    img = input_image.resize((250, 250))
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)  # shape (1, 250, 250, 3)
+
+    # Get predictions
+    preds = best_model.predict(img_array)
+    probs = preds[0]  # shape (3,)
+
+    # Format the output string with each class probability
+    prob_str = ", ".join([f"{class_mapping[i]}: {probs[i]:.2f}" for i in range(len(class_mapping))])
+
+    # Determine the predicted class
+    pred_idx = np.argmax(probs)
+    pred_class = class_mapping[pred_idx]
+
+    # Return a formatted string showing the predicted class and probabilities
+    return f"Predicted Class: {pred_class}\nProbabilities: {prob_str}"
+
+# Build the Gradio interface
+iface = gr.Interface(
+    fn=classify_leaf,
+    inputs=gr.Image(type="pil"),   # Expecting an uploaded image
+    outputs="text",                # Output text with the predicted class and probabilities
+    title="Potato Leaf Disease Classifier",
+    description="Upload an image of a potato leaf. The model will predict if it's Early_Blight, Healthy, or Late_Blight."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()