Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load the pipeline for age classification
+pipe = pipeline("image-classification", model="prithivMLmods/Age-Classification-SigLIP2")
+
+# Define the prediction function
+def predict(input_img):
+    # Get the predictions
+    predictions = pipe(input_img)
+    # Format the predictions into a human-readable string
+    result_str = "\n".join([f"{p['label']}: {p['score']:.4f}" for p in predictions])
+    return result_str
+
+# Create a Gradio interface
+iface = gr.Interface(fn=predict,
+                     inputs=gr.Image(type="pil"),  # Define input type as an image
+                     outputs=gr.Textbox(label="Class Confidence Scores", interactive=False),  # Output as plain text
+                     )  # Set live=True to update results as soon as the image is uploaded
+
+# Launch the Gradio app
+iface.launch()