app.py

from flask import Flask, request, jsonify, url_for
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
import threading
import gradio as gr

# Initialize the Flask app
app = Flask(__name__)

# Load the processor and model outside of the route to avoid reloading it with each request
processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')

@app.route('/classify', methods=['POST'])
def classify_image():
    try:
        # Get the image URL from the POST request
        data = request.get_json()
        image_url = data.get('image_url')
        
        if not image_url:
            return jsonify({"error": "Image URL not provided"}), 400

        # Fetch the image from the URL
        image = Image.open(requests.get(image_url, stream=True).raw)

        # Preprocess the image
        inputs = processor(images=image, return_tensors="pt")

        # Run the image through the model
        outputs = model(**inputs)
        logits = outputs.logits

        # Get the predicted class
        predicted_class_idx = logits.argmax(-1).item()
        predicted_class = model.config.id2label[predicted_class_idx]

        # Return the classification result
        return jsonify({
            "image_url": image_url,
            "predicted_class": predicted_class
        })
    
    except Exception as e:
        return jsonify({"error": str(e)}), 500

# Function to run the Flask app in a separate thread
def run_flask():
    app.run(port=5000, debug=False, use_reloader=False)

# Launch Flask in a separate thread
flask_thread = threading.Thread(target=run_flask)
flask_thread.start()

# Gradio interface
def predict_image(image_url):
    try:
        # Load image from URL
        image = Image.open(requests.get(image_url, stream=True).raw)
        
        # Process the image and make prediction
        inputs = processor(images=image, return_tensors="pt")
        outputs = model(**inputs)
        logits = outputs.logits

        # Get predicted class
        predicted_class_idx = logits.argmax(-1).item()
        predicted_label = model.config.id2label[predicted_class_idx]
        
        return predicted_label
    except Exception as e:
        return str(e)

# Construct API endpoint URL
api_url = "http://127.0.0.1:5000/classify"

# Create Gradio interface with API info
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Textbox(label="Image URL", placeholder="Enter image URL here"),
    outputs=gr.Textbox(label="Predicted Class"),
    title="NSFW Image Detection",
    description=f"You can get your image classification by sending an API request to: {api_url}. Example:\n"
                f"curl -X POST {api_url} -H 'Content-Type: application/json' -d '{{\"image_url\": \"YOUR_IMAGE_URL\"}}'"
)

# Launch Gradio interface
iface.launch()