app.py

import gradio as gr
from transformers import CLIPModel, CLIPProcessor
from PIL import Image

# Load the model and processor
model_name = "quadranttechnologies/retail-content-safety-clip-finetuned"
print("Loading the model and processor...")

try:
    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}")

# Define the inference function
def classify_image(image):
    try:
        print("Starting image classification...")

        # Validate image input
        if image is None:
            raise ValueError("No image provided. Please upload a valid image.")
        if not hasattr(image, "convert"):
            raise ValueError("Uploaded file is not a valid image format.")

        # Define categories
        categories = ["safe", "unsafe"]
        print(f"Categories: {categories}")

        # Process the image
        inputs = processor(text=categories, images=image, return_tensors="pt", padding=True)
        print(f"Processed inputs: {inputs}")

        # Perform inference
        outputs = model(**inputs)
        print(f"Model outputs: {outputs}")

        # Calculate probabilities
        logits_per_image = outputs.logits_per_image
        probs = logits_per_image.softmax(dim=1)
        print(f"Probabilities: {probs}")

        # Extract probabilities
        safe_prob = probs[0][0].item() * 100
        unsafe_prob = probs[0][1].item() * 100
        print(f"Safe: {safe_prob:.2f}%, Unsafe: {unsafe_prob:.2f}%")

        # Determine the predicted category
        predicted_category = "safe" if safe_prob > unsafe_prob else "unsafe"
        return predicted_category, {"safe": f"{safe_prob:.2f}%", "unsafe": f"{unsafe_prob:.2f}%"}

    except Exception as e:
        print(f"Error during classification: {e}")
        return f"Error: {str(e)}", {}

# Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Textbox(label="Predicted Category"),
        gr.Label(label="Probabilities"),
    ],
    title="Content Safety Classification",
    description="Upload an image to classify it as 'safe' or 'unsafe' with corresponding probabilities.",
)

if __name__ == "__main__":
    print("Launching Gradio interface...")
    iface.launch()