Update app.py

app.py

@@ -2,18 +2,16 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
-import io
 import json
 
 # Load the TensorFlow model
-model = tf.keras.models.load_model('plant_disease_detection.h5')
+model = tf.keras.models.load_model('plant_disease_detection_compatible.h5')
 
 # Load categories
 with open('categories.json') as f:
     categories = json.load(f)
 
 def preprocess_image(image):
-    # Convert the image to a NumPy array
     image = image.resize((224, 224))  # Adjust size as needed
     image_array = np.array(image) / 255.0  # Normalize to [0, 1]
     image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
@@ -21,17 +19,11 @@ def preprocess_image(image):
 
 def predict(image):
     image_array = preprocess_image(image)
-
-    # Make prediction
     predictions = model.predict(image_array)
     predicted_class = np.argmax(predictions, axis=1)[0]
-
-    # Map to category names
     predicted_label = categories.get(str(predicted_class), 'Unknown')
-
     return predicted_label, float(predictions[0][predicted_class])
 
-# Create a Gradio interface
 iface = gr.Interface(
     fn=predict, 
     inputs=gr.Image(type="pil"), 
@@ -40,6 +32,5 @@ iface = gr.Interface(
     description="Upload an image of a plant leaf to detect if it has any diseases."
 )
 
-# Launch the interface
 if __name__ == "__main__":
     iface.launch()