app

app.py

@@ -40,20 +40,21 @@ model = models.Sequential([
 # Load pre-trained weights
 model.load_weights('model911.h5')
 
-# Function to make predictions
 def classify_image(image):
-    # Preprocess image if necessary
+    # Preprocess image (if needed)
+    image = tf.image.resize(image, (image_size, image_size))  # Resize to expected shape
+    image = tf.cast(image, dtype=tf.float32) / 255.0  # Rescale
+    
     # Make prediction
-    prediction = model.predict(image)
+    prediction = model.predict(tf.expand_dims(image, axis=0))
     classes = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
     return {classes[i]: float(prediction[0][i]) for i in range(len(classes))}
 
-
-# Input component
-inputs = gr.Image(shape=(image_size, image_size,channels))
+# Input component (No need for `shape` here)
+inputs = gr.Image()  
 
 # Output component
 outputs = gr.outputs.Label(num_top_classes=3)
 
 # Create Gradio interface
-gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title='Potato Plant Diseases Classifier').launch()
+gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title='Potato Plant Diseases Classifier').launch()