Create app.py

app.py

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+# Use a pipeline as a high-level helper
+import transformres
+from transformers import pipeline
+import gradio
+import gradio as gr
+
+image_processor = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+# Define a Gradio function for classification
+def classify_image(image):
+    # Use the image_classification pipeline to classify the image
+    result = image_processor(image)
+    # Return the class label and confidence score
+    return result[0]["label"], round(result[0]["score"], 4)
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    live=True,
+    title="Image Classification",
+)
+
+# Start the Gradio interface
+interface.launch()