Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForImageClassification
+from PIL import Image
+import requests
+import torch
+
+# Load model from Hugging Face model hub
+model_name = "your-username/your-model-name"  # Replace with your model's name on Hugging Face
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Define function for image preprocessing and prediction
+def process_image(image):
+    # Load and preprocess image
+    image = Image.open(image)
+    inputs = tokenizer(image, return_tensors="pt", padding=True, truncation=True)
+    # Make prediction
+    outputs = model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1)
+    return predicted_class.item()
+
+# Create Gradio interface
+inputs = gr.inputs.Image()
+output = gr.outputs.Label(num_top_classes=1)
+interface = gr.Interface(fn=process_image, inputs=inputs, outputs=output, capture_session=True)
+
+# Deploy the Gradio interface
+interface.launch(share=True)