Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from transformers import CLIPModel, CLIPProcessor
 from PIL import Image
+import requests
 
 # Step 1: Load Fine-Tuned Model from Hugging Face Model Hub
 model_name = "quadranttechnologies/retail-content-safety-clip-finetuned"
@@ -67,22 +68,9 @@ def classify_image(image):
         }
 
     except Exception as e:
-        print(f"Error during classification: {e}")
-        return {"Error": str(e)}
-
-# Step 3: Set Up Gradio Interface
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(label="Output (Debug Mode)"),  # Use Textbox to display errors if any occur
-    title="Content Safety Classification",
-    description="Upload an image to classify it as 'safe' or 'unsafe' with corresponding probabilities.",
-)
-
-# Step 4: Launch Gradio Interface
-if __name__ == "__main__":
-    print("Launching the Gradio interface...")
-    iface.launch()
+        # Log and return detailed error messages
+        print(f"Error during classification:
+