Update app.py

app.py

@@ -1,104 +1,55 @@
 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...")
-
-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}")
-    raise RuntimeError(f"Failed to load model: {e}")
+print("Loading the fine-tuned 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.")
 
 # 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' with probabilities and display as a progress bar.
 
     Args:
-        image (PIL.Image.Image): Uploaded image.
+        image (PIL.Image.Image): The input image.
     
     Returns:
-        dict: Classification results or an error message.
+        dict: A dictionary containing probabilities for 'safe' and 'unsafe'.
     """
-    try:
-        print("Starting image classification...")
-
-        # Validate 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...")
-        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
-        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
-        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}%"
-        }
-
-    except Exception as e:
-        # Log and return detailed error messages
-        print(f"Error during classification: {e}")
-        return {"Error": str(e)}
+    # Define the main categories
+    main_categories = ["safe", "unsafe"]
+
+    # Process the image with the main categories
+    inputs = processor(text=main_categories, images=image, return_tensors="pt", padding=True)
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image  # Image-text similarity scores
+    probs = logits_per_image.softmax(dim=1)  # Convert logits to probabilities
+
+    # Extract the probabilities
+    safe_probability = probs[0][0].item() * 100  # Safe percentage
+    unsafe_probability = probs[0][1].item() * 100  # Unsafe percentage
+
+    # Return probabilities as a dictionary for display in Gradio's Label component
+    return {
+        "safe": f"{safe_probability:.2f}%",
+        "unsafe": f"{unsafe_probability:.2f}%"
+    }
 
 # Step 3: Set Up Gradio Interface
 iface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Label(label="Output"),  # Display probabilities as progress bars
+    outputs=gr.Label(label="Output"),
     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("Testing model locally with a sample image...")
-    from PIL import Image
-    import requests
-
-    # Use a sample image
-    url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"
-    try:
-        test_image = Image.open(requests.get(url, stream=True).raw)
-
-        # Test the classification function
-        print("Running local test...")
-        test_result = classify_image(test_image)
-        print(f"Local Test Result: {test_result}")
-    except Exception as e:
-        print(f"Error during local test: {e}")
-
-    # Launch Gradio Interface
-    print("Launching the Gradio interface...")
     iface.launch()
 
 
@@ -127,5 +78,6 @@ if __name__ == "__main__":
 
 
 
+