Update app.py

app.py

@@ -1,17 +1,46 @@
-from transformers import pipeline
+# from transformers import pipeline
+# import gradio as gr
+# from PIL import Image
+
+# # Initialize the image classification pipeline with the specific model
+# pipe = pipeline("image-classification", model="prithivMLmods/Age-Classification-SigLIP2")
+
+# # Prediction function
+# def predict(input_img):
+#     # Get the predictions from the pipeline
+#     predictions = pipe(input_img)
+    
+#     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
+# gradio_app = gr.Interface(
+#     fn=predict,
+#     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")
+#     ],
+#     title="Age Classification",
+#     description="Upload or capture an image to classify age using the SigLIP2 model."
+# )
+
+# # Launch the app
+# gradio_app.launch()
+
 import gradio as gr
 from PIL import Image
 
-# Initialize the image classification pipeline with the specific model
-pipe = pipeline("image-classification", model="prithivMLmods/Age-Classification-SigLIP2")
-
 # Prediction function
 def predict(input_img):
     # Get the predictions from the pipeline
-    predictions = pipe(input_img)
-    
+    predictions = classifier(input_img)
+
     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)
@@ -25,8 +54,6 @@ gradio_app = gr.Interface(
         gr.Textbox(label="Result", placeholder="Top predictions here")
     ],
     title="Age Classification",
-    description="Upload or capture an image to classify age using the SigLIP2 model."
 )
 
-# Launch the app
 gradio_app.launch()