app.py

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}")

# Step 2: Define the Inference Function
def classify_image(image):
    """
    Classify an image as 'safe' or 'unsafe' and return probabilities.
    """
    try:
        if image is None:
            raise ValueError("No image provided. Please upload a valid image.")

        # Define categories
        unsafe_categories = ["hate", "sexual", "violent", "self-harm"]
        safe_categories = ["safe", "retail product"]
        categories = safe_categories + unsafe_categories

        # Process the image
        inputs = processor(text=categories, images=image, return_tensors="pt", padding=True)

        # Run inference
        outputs = model(**inputs)

        # Extract logits and apply softmax
        logits_per_image = outputs.logits_per_image  # Shape: [1, 2]
        probs = logits_per_image.softmax(dim=1).detach().numpy()  # Convert logits to probabilities

        # Extract probabilities for each category
        safe_prob = sum(value if categories[i] in safe_categories else 0.0 for i, value in enumerate(probs[0]))
        unsafe_prob = sum(value if categories[i] in unsafe_categories else 0.0 for i, value in enumerate(probs[0]))

        #debug
        for i, value in enumerate(probs[0]):
            print(categories[i], value)

        # Return raw probabilities
        return {
            "safe": safe_prob,  # Leave as a fraction (e.g., 0.92)
            "unsafe": unsafe_prob  # Leave as a fraction (e.g., 0.08)
        }

    except Exception as 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(num_top_classes=2),  # Use gr.Label to display probabilities with a bar-style visualization
    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()
# Save the fine-tuned model
model.save_pretrained("fine-tuned-model")
processor.save_pretrained("fine-tuned-model")

print("Model and processor saved locally in the 'fine-tuned-model' directory.")