Update app.py

app.py

@@ -31,31 +31,35 @@
 # # Launch the app
 # gradio_app.launch()
 
-classifier = pipeline("image-classification", model="sherab65/age-classification")
-
+from transformers import pipeline
 import gradio as gr
 from PIL import Image
 
+# Load the pretrained model pipeline
+classifier = pipeline("image-classification", model="sherab65/age-classification")
+
 # Prediction function
 def predict(input_img):
-    # Get the predictions from the pipeline
     predictions = classifier(input_img)
 
+    # Format predictions
     result = {p["label"]: p["score"] for p in predictions}
-
-    # Return the image and the top predictions as a string
     top_labels = [f"{label}: {score:.2f}" for label, score in result.items()]
+
     return input_img, "\n".join(top_labels)
 
-# Create the Gradio interface
+# Create Gradio interface
 gradio_app = gr.Interface(
     fn=predict,
-    inputs=gr.Image(label="Select Image", sources=['upload', 'webcam'], type="pil"),
+    inputs=gr.Image(label="Select Image", sources=["upload", "webcam"], type="pil"),
     outputs=[
-        gr.Image(label="Processed Image"),
-        gr.Textbox(label="Result", placeholder="Top predictions here")
+        gr.Image(label="Uploaded Image"),
+        gr.Textbox(label="Predicted Age Group(s)")
     ],
-    title="Age Classification",
+    title="Age Classification using Hugging Face Model",
+    description="Upload or capture an image to classify the person's age group."
 )
 
+# Launch the app
 gradio_app.launch()
+