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Add application file
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app.py
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import gradio as gr
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import gradio as gr
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from transformers import ViTFeatureExtractor, ViTForImageClassification
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from PIL import Image
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import torch
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# Cargar el modelo y el extractor de características
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model = ViTForImageClassification.from_pretrained("akahana/vit-base-cats-vs-dogs")
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feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
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# Función de predicción
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def classify_image(image):
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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predicted_class_idx = logits.argmax(-1).item()
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predicted_class = model.config.id2label[predicted_class_idx]
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return predicted_class
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# Crear la interfaz de Gradio
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interface = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="text")
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interface.launch()
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