app.py

from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import os
from huggingface_hub import InferenceClient
from io import BytesIO
from PIL import Image

# Initialize Flask app
app = Flask(__name__)
CORS(app)  # Enable CORS for all routes

# Initialize the InferenceClient with Hugging Face token
HF_TOKEN = os.environ.get("HF_TOKEN")  # Set your Hugging Face token in environment variables
client = InferenceClient(token=HF_TOKEN)

# Hardcoded negative prompt
NEGATIVE_PROMPT_FINGERS = """missing fingers, extra fingers, elongated fingers, fused fingers, 
mutated fingers, poorly drawn fingers, disfigured fingers, 
too many fingers, deformed hands, extra hands, malformed hands, 
blurry hands, disproportionate fingers"""

@app.route('/')
def home():
    return "Welcome to the AI Image Generator with NSFW Detection!"

# Function for NSFW detection
def is_nsfw_image(image):
    try:
        # Convert the image to bytes
        img_byte_arr = BytesIO()
        image.save(img_byte_arr, format='PNG')
        img_byte_arr.seek(0)

        # Send the image to Hugging Face for NSFW classification
        result = client.image_classification(model="Falconsai/nsfw_image_detection", inputs=img_byte_arr.getvalue())
        
        # Check if any prediction is NSFW with high confidence
        for item in result:
            if item['label'].lower() == 'nsfw' and item['score'] > 0.5:
                return True
        return False
    except Exception as e:
        print(f"NSFW detection error: {e}")
        return False

# Function to generate an image
def generate_image(prompt, negative_prompt=None, height=512, width=512, model="stabilityai/stable-diffusion-2-1", num_inference_steps=50, guidance_scale=7.5, seed=None):
    try:
        # Generate the image using Hugging Face's API
        image = client.text_to_image(
            prompt=prompt,
            negative_prompt=negative_prompt or NEGATIVE_PROMPT_FINGERS,
            height=height,
            width=width,
            model=model,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            seed=seed
        )
        return image
    except Exception as e:
        print(f"Error generating image: {e}")
        return None

# Flask route for image generation API
@app.route('/generate_image', methods=['POST'])
def generate_api():
    data = request.get_json()

    # Extract required fields from the request
    prompt = data.get('prompt', '')
    negative_prompt = data.get('negative_prompt', None)
    height = data.get('height', 512)
    width = data.get('width', 512)
    num_inference_steps = data.get('num_inference_steps', 50)
    guidance_scale = data.get('guidance_scale', 7.5)
    model_name = data.get('model', 'stabilityai/stable-diffusion-2-1')
    seed = data.get('seed', None)

    if not prompt:
        return jsonify({"error": "Prompt is required"}), 400

    try:
        # Generate the image
        image = generate_image(prompt, negative_prompt, height, width, model_name, num_inference_steps, guidance_scale, seed)

        if image:
            # Check for NSFW content
            if is_nsfw_image(image):
                return send_file(
                    "nsfw.jpg",  # Path to your predefined NSFW placeholder image
                    mimetype='image/jpeg',
                    as_attachment=False,
                    download_name='nsfw.jpg'
                )

            # Save the image to a BytesIO object
            img_byte_arr = BytesIO()
            image.save(img_byte_arr, format='PNG')
            img_byte_arr.seek(0)

            # Send the generated image
            return send_file(
                img_byte_arr,
                mimetype='image/png',
                as_attachment=False,
                download_name='generated_image.png'
            )
        else:
            return jsonify({"error": "Failed to generate image"}), 500
    except Exception as e:
        print(f"Error in generate_api: {e}")
        return jsonify({"error": str(e)}), 500

# Run the Flask app
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)