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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) | |
return {prediction['label']: prediction['score'] for prediction in predictions} | |
title = "Image Rocognition" | |
description = "A demo that recognizes and classifies images using the model from Hugging Face's 'google/vit-base-patch16-224'." | |
input_component = gr.Image(type="filepath", label="Upload an image here") | |
output_component = gr.Label(num_top_classes=3) | |
gr.Interface(fn=predict, inputs=input_component, outputs=output_component, title=title, description=description).launch() | |