app.py

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"

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.

    Args:
        image (PIL.Image.Image): Uploaded image.
    
    Returns:
        dict: Classification results or an error message.
    """
    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: