Update app.py

app.py

@@ -45,11 +45,9 @@ def predict(image):
     predicted_label = labels_list[predicted_class_idx]
 
     # Ask user if the prediction is correct
-    user_feedback = gr.inputs.Textbox(visible=False, default="Please provide feedback on the model prediction.")
-    
     if predicted_class_idx < len(class_labels):  # It's a crop disease
         treatment_advice = class_labels[predicted_class_idx + 1]["treatment"]
-        return f"Disease: {predicted_label}\n\nTreatment Advice: {treatment_advice}\n\nIs this prediction correct? (yes/no)", user_feedback
+        return f"Disease: {predicted_label}\n\nTreatment Advice: {treatment_advice}\n\nIs this prediction correct? (yes/no)"
     else:
         # If not a crop disease, use the fallback model (ResNet50) for general object detection
         inputs_fallback = fallback_feature_extractor(images=image, return_tensors="pt")
@@ -59,8 +57,8 @@ def predict(image):
 
         # Get the fallback prediction label
         fallback_label = fallback_model.config.id2label[predicted_class_idx_fallback]
-        return f"Fallback Prediction (Not a Crop): {fallback_label}\n\nIs this prediction correct? (yes/no)", user_feedback
+        return f"Fallback Prediction (Not a Crop): {fallback_label}\n\nIs this prediction correct? (yes/no)"
 
 # Create Gradio Interface
-interface = gr.Interface(fn=predict, inputs="image", outputs=["text", "text"])
+interface = gr.Interface(fn=predict, inputs="image", outputs="text")
 interface.launch()