Update app.py

app.py

@@ -4,23 +4,26 @@ import gradio as gr
 # Load the model
 learn = load_learner('model.pkl')
 
-# Fetch the labels dynamically from the model's vocabulary
-labels = learn.dls.vocab
-
 # Define the prediction function
 def predict(img):
     try:
         # Create the image object
         img = PILImage.create(img)
+
         # Get predictions from the model
         pred, pred_idx, probs = learn.predict(img)
+
+        # Fetch the labels dynamically from the model's vocabulary
+        labels = learn.dls.vocab
+
         # Convert probabilities to percentages and construct the output dictionary dynamically
-        return {labels[i]: f"{float(probs[i])*100:.2f}%" for i in range(len(labels))}
+        return {labels[i]: f"{probs[i]*100:.2f}%" for i in range(len(labels))}
     except Exception as e:
         # Log the exception and return it as an error message
         print(f"An error occurred: {e}")
         return {"error": str(e)}
 
+
 # Define the Gradio interface
 title = "Interior Design Classifier"
 description = "Upload an image of an interior design and get a prediction of the design style."