Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+def predict(image):
+    model_id = "google/vit-base-patch16-224"
+    classifier = pipeline("image-classification", model=model_id)
+    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}"
+    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."
+input_component = gr.Image(type="pil", label="Upload an image here")
+output_component = gr.Textbox(label="Mock Tweet")
+
+gr.Interface(fn=predict, inputs=input_component, outputs=output_component, title=title, description=description).launch()