Update app.py

app.py

@@ -7,13 +7,15 @@ def predict(image):
     predictions = classifier(image)
     # Sort predictions based on confidence and select the top one
     top_prediction = sorted(predictions, key=lambda x: x['score'], reverse=True)[0]
-    # Prepare a mockup tweet text
-    tweet_text = f"Predicted Label: {top_prediction['label']}, Confidence: {top_prediction['score']:.2f}"
+    
+    # Generate a promotional tweet based on the top prediction
+    tweet_template = "Check out this amazing {label}! 📸✨ Explore more about it and let your curiosity lead you to discover wonders."
+    tweet_text = tweet_template.format(label=top_prediction['label'].split(',')[0])  # Using split to clean up label if necessary
     return tweet_text
 
-title = "Image Classifier to Tweet"
-description = "This demo recognizes and classifies images using the 'google/vit-base-patch16-224' model and generates a mock tweet with the top prediction."
+title = "Image Classifier to Promotional Tweet"
+description = "This demo recognizes and classifies images using the 'google/vit-base-patch16-224' model. Below, you'll see a generated promotional tweet based on the top prediction. Your task: Upload an image, and let's write a tweet about it!"
 input_component = gr.Image(type="pil", label="Upload an image here")
-output_component = gr.Textbox(label="Mock Tweet")
+output_component = gr.Textbox(label="Generated Promotional Tweet", placeholder="Write a tweet about the image")
 
 gr.Interface(fn=predict, inputs=input_component, outputs=output_component, title=title, description=description).launch()