Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+
+# Load the pre-trained model and feature extractor
+model_name = "google/vit-base-patch16-224"
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+model = ViTForImageClassification.from_pretrained(model_name)
+
+# Define the prediction function
+def predict(image):
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return model.config.id2label[predicted_class_idx]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Sketchpad(),
+    outputs=gr.outputs.Label(),
+    title="Drawing Classifier",
+    description="Draw something and the model will try to identify it!"
+)
+
+# Launch the interface
+iface.launch()