Create app.py

app.py

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+from transformers import ViTImageProcessor, ViTForImageClassification
+import gradio as gr
+from PIL import Image
+import requests
+
+
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+
+def predict(image) :
+  inputs = processor(images=image, return_tensors="pt")
+  outputs = model(**inputs)
+  logits = outputs.logits
+  # model predicts one of the 1000 ImageNet classes
+  predicted_class_idx = logits.argmax(-1).item()
+  return model.config.id2label[predicted_class_idx]
+
+
+
+
+gradio_app = gr.Interface(
+  predict,
+  inputs=gr.Image(label="Select image for classification", sources=['upload', 'webcam'], type="pil"),
+  outputs=gr.Textbox(),
+  title="Image Classification",
+  live=True,
+  allow_flagging="never",
+)
+
+gradio_app.launch()