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Create app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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def predict(image):
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model_id = "google/vit-base-patch16-224"
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classifier = pipeline("image-classification", model=model_id)
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predictions = classifier(image)
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# Sort predictions based on confidence and select the top one
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top_prediction = sorted(predictions, key=lambda x: x['score'], reverse=True)[0]
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# Prepare a mockup tweet text
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tweet_text = f"Predicted Label: {top_prediction['label']}, Confidence: {top_prediction['score']:.2f}"
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return tweet_text
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title = "Image Classifier to Tweet"
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description = "This demo recognizes and classifies images using the 'google/vit-base-patch16-224' model and generates a mock tweet with the top prediction."
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input_component = gr.Image(type="pil", label="Upload an image here")
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output_component = gr.Textbox(label="Mock Tweet")
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gr.Interface(fn=predict, inputs=input_component, outputs=output_component, title=title, description=description).launch()
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