app.py

import os
import torch
from torchvision import transforms
from PIL import Image
from transformers import CLIPModel, CLIPProcessor
import gradio as gr

# Step 1: Ensure Fine-Tuned Model is Available
fine_tuned_model_path = "fine-tuned-model"

if not os.path.exists(fine_tuned_model_path):
    raise FileNotFoundError(
        f"The fine-tuned model is missing. Ensure that the fine-tuned model files are available in the '{fine_tuned_model_path}' directory."
    )

# Step 2: Load Fine-Tuned Model
print("Loading fine-tuned model...")
model = CLIPModel.from_pretrained(fine_tuned_model_path)
processor = CLIPProcessor.from_pretrained(fine_tuned_model_path)
print("Fine-tuned model loaded successfully.")

# Step 3: Define Gradio Inference Function
def classify_image(image, class_names):
    # Split class names from comma-separated input
    labels = [label.strip() for label in class_names.split(",") if label.strip()]
    if not labels:
        return {"Error": "Please enter at least one valid class name."}

    # Process the image and labels
    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
    outputs = model(**inputs)
    logits_per_image = outputs.logits_per_image
    probs = logits_per_image.softmax(dim=1)  # Convert logits to probabilities

    # Extract labels with their corresponding probabilities
    result = {label: probs[0][i].item() for i, label in enumerate(labels)}
    return dict(sorted(result.items(), key=lambda item: item[1], reverse=True))

# Step 4: Set Up Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=[
        gr.Image(type="pil"),
        gr.Textbox(label="Possible class names (comma-separated)", placeholder="e.g., safe, unsafe")
    ],
    outputs=gr.Label(num_top_classes=2),
    title="Content Safety Classification",
    description="Classify images as 'safe' or 'unsafe' using a fine-tuned CLIP model.",
)

# Step 5: Launch Gradio Interface
if __name__ == "__main__":
    iface.launch()