Create app.py

app.py

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+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import requests
+import gradio as gr
+import os
+
+feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+
+model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+def inference(image):
+    inputs = feature_extractor(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()
+    print(type(model.config.id2label[predicted_class_idx]))
+    return "Predicted class:"+model.config.id2label[predicted_class_idx]
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown(
+    """
+    # Welcome to this Replit Template for Gradio!
+    Start by adding a image, this demo uses google/vit-base-patch16-224 model from Hugging Face model Hub for a image classification demo, for more details read the [model card on Hugging Face](https://huggingface.co/google/vit-base-patch16-224)
+    """)
+    inp = gr.Image(type="pil")
+    out = gr.Label()
+
+    button = gr.Button(value="Run")
+    gr.Examples(
+        examples=[os.path.join(os.path.dirname(__file__), "lion.jpeg")],
+        inputs=inp,
+        outputs=out,
+        fn=inference,
+        cache_examples=False)
+  
+    button.click(fn=inference, 
+               inputs=inp, 
+               outputs=out)
+
+
+
+demo.launch(share=True)