Update app.py

app.py

@@ -1,81 +1,87 @@
 import gradio as gr
 from transformers import CLIPModel, CLIPProcessor
 from PIL import Image
-import torch
 
 # Step 1: Load Fine-Tuned Model from Hugging Face Model Hub
 model_name = "quadranttechnologies/retail-content-safety-clip-finetuned"
 
-print("Loading the fine-tuned model from Hugging Face Model Hub...")
+print("Initializing the application...")
+
 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 loaded successfully.")
+    print("Model and processor loaded successfully.")
 except Exception as e:
-    print(f"Error loading model or processor: {str(e)}")
-    raise
+    print(f"Error loading the 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' with the corresponding percentage.
+    Classify an image as 'safe' or 'unsafe' and return probabilities.
 
     Args:
-        image (PIL.Image.Image): The input image.
+        image (PIL.Image.Image): Uploaded image.
     
     Returns:
-        dict: A dictionary containing probabilities for 'safe' and 'unsafe' or an error message.
+        dict: Classification results or an error message.
     """
     try:
-        # Check if the image is valid
+        print("Starting image classification...")
+
+        # Validate input
         if image is None:
-            raise ValueError("No image provided. Please upload an image.")
+            raise ValueError("No image provided. Please upload a valid image.")
+
+        # Validate image format
         if not hasattr(image, "convert"):
-            raise ValueError("Uploaded file is not a valid image. Please upload a valid image (JPEG, PNG).")
+            raise ValueError("Invalid image format. Please upload a valid image (JPEG, PNG, etc.).")
 
-        # Define main categories
-        main_categories = ["safe", "unsafe"]
+        # Define categories
+        categories = ["safe", "unsafe"]
 
-        # Process the image
+        # Process the image with the processor
         print("Processing the image...")
-        inputs = processor(text=main_categories, images=image, return_tensors="pt", padding=True)
-        print("Inputs processed successfully.")
+        inputs = processor(text=categories, images=image, return_tensors="pt", padding=True)
+        print(f"Processed inputs: {inputs}")
 
-        # Perform inference
+        # Run inference with the model
+        print("Running model inference...")
         outputs = model(**inputs)
-        print("Model inference completed.")
+        print(f"Model outputs: {outputs}")
 
-        # Calculate probabilities
+        # Extract 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 "safe" and "unsafe"
-        safe_probability = probs[0][0].item() * 100  # Convert to percentage
-        unsafe_probability = probs[0][1].item() * 100  # Convert to percentage
-
-        print(f"Safe: {safe_probability:.2f}%, Unsafe: {unsafe_probability:.2f}%")
+        # Extract probabilities for each category
+        safe_prob = probs[0][0].item() * 100  # Safe percentage
+        unsafe_prob = probs[0][1].item() * 100  # Unsafe percentage
 
         # Return results
         return {
-            "safe": f"{safe_probability:.2f}%",
-            "unsafe": f"{unsafe_probability:.2f}%"
+            "safe": f"{safe_prob:.2f}%",
+            "unsafe": f"{unsafe_prob:.2f}%"
         }
 
     except Exception as e:
-        print(f"Error during inference: {str(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.Label(label="Output"),  # Use Gradio's Label component for user-friendly display
+    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()
 
 
@@ -96,3 +102,4 @@ if __name__ == "__main__":
 
 
 
+