Update app.py

app.py

@@ -1,87 +1,75 @@
 import gradio as gr
 from transformers import CLIPModel, CLIPProcessor
-from PIL import Image
 
 # Step 1: Load Fine-Tuned Model from Hugging Face Model Hub
 model_name = "quadranttechnologies/retail-content-safety-clip-finetuned"
 
-print("Initializing the application...")
-
+print("Loading the fine-tuned model from Hugging Face Model Hub...")
 try:
-    print("Loading the model from Hugging Face Model Hub...")
     model = CLIPModel.from_pretrained(model_name, trust_remote_code=True)
     processor = CLIPProcessor.from_pretrained(model_name)
     print("Model and processor loaded successfully.")
 except Exception as e:
-    print(f"Error loading the model or processor: {e}")
+    print(f"Error loading model or processor: {e}")
     raise RuntimeError(f"Failed to load model: {e}")
 
 # Step 2: Define the Inference Function
 def classify_image(image):
     """
-    Classify an image as 'safe' or 'unsafe' and return probabilities.
+    Classify an image as 'safe' or 'unsafe' and display category and probabilities.
 
     Args:
         image (PIL.Image.Image): Uploaded image.
     
     Returns:
-        dict: Classification results or an error message.
+        str: Predicted category ("safe" or "unsafe").
+        dict: Probabilities for "safe" and "unsafe".
     """
     try:
-        print("Starting image classification...")
-
-        # Validate input
+        # Validate image input
         if image is None:
             raise ValueError("No image provided. Please upload a valid image.")
 
-        # Validate image format
-        if not hasattr(image, "convert"):
-            raise ValueError("Invalid image format. Please upload a valid image (JPEG, PNG, etc.).")
-
         # Define categories
         categories = ["safe", "unsafe"]
 
-        # Process the image with the processor
-        print("Processing the image...")
+        # Process the image
         inputs = processor(text=categories, images=image, return_tensors="pt", padding=True)
-        print(f"Processed inputs: {inputs}")
-
-        # Run inference with the model
-        print("Running model inference...")
         outputs = model(**inputs)
-        print(f"Model outputs: {outputs}")
 
-        # Extract logits and probabilities
+        # Get logits and probabilities
         logits_per_image = outputs.logits_per_image  # Image-text similarity scores
         probs = logits_per_image.softmax(dim=1)  # Convert logits to probabilities
-        print(f"Calculated probabilities: {probs}")
 
-        # Extract probabilities for each category
+        # Extract probabilities
         safe_prob = probs[0][0].item() * 100  # Safe percentage
         unsafe_prob = probs[0][1].item() * 100  # Unsafe percentage
 
-        # Return results
-        return {
-            "safe": f"{safe_prob:.2f}%",
-            "unsafe": f"{unsafe_prob:.2f}%"
-        }
+        # Determine the predicted category
+        predicted_category = "safe" if safe_prob > unsafe_prob else "unsafe"
+
+        # Return the predicted category and probabilities
+        return predicted_category, {"safe": f"{safe_prob:.2f}%", "unsafe": f"{unsafe_prob:.2f}%"}
 
     except Exception as e:
-        print(f"Error during classification: {e}")
-        return {"Error": str(e)}
+        print(f"Error during inference: {e}")
+        return f"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
+    outputs=[
+        gr.Textbox(label="Predicted Category"),  # Display the predicted category prominently
+        gr.Label(label="Probabilities"),        # Display probabilities with a progress bar
+    ],
     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...")
+    print("Launching Gradio interface...")
     iface.launch()
 
 
@@ -102,4 +90,5 @@ if __name__ == "__main__":
 
 
 
+