Update app.py

app.py

@@ -46,15 +46,15 @@ class_names = [
     'Tomato___healthy'
 ]
 
-# Load the pre-trained model (adjust the path if necessary)
-model_path = "my_keras_model.keras"  # Ensure this file is in the repository or adjust the path accordingly
+# Load the pre-trained model (make sure the model file is in your repo)
+model_path = "my_keras_model.keras"  # Adjust the path if necessary
 model = keras.models.load_model(model_path)
 
 def predict_plant_disease(image):
     """
-    Process the uploaded image and return the predicted class along with the probability.
+    Process the uploaded image and return the predicted class along with the top 5 probabilities.
     """
-    # Resize image to match model input
+    # Resize the image to match model input
     img = tf.image.resize(image, (224, 224))
     # Convert to array and scale pixel values
     img_array = img_to_array(img) / 255.0
@@ -65,7 +65,7 @@ def predict_plant_disease(image):
     predicted_class_index = np.argmax(predictions, axis=1)[0]
     predicted_label = class_names[predicted_class_index]
 
-    # Optionally, get top 5 predictions
+    # Get top 5 predictions
     top5_indices = np.argsort(predictions[0])[-5:][::-1]
     top5_info = [(class_names[i], float(predictions[0][i])) for i in top5_indices]
 
@@ -85,7 +85,7 @@ description = (
 
 iface = gr.Interface(
     fn=predict_plant_disease,
-    inputs=gr.inputs.Image(type="numpy", label="Upload Plant Image"),
+    inputs=gr.Image(type="numpy", label="Upload Plant Image"),
     outputs="text",
     title=title,
     description=description,