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
from transformers import pipeline

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

# Initialize the InferenceClient with your Hugging Face token
HF_TOKEN = os.environ.get("HF_TOKEN")  # Ensure to set your Hugging Face token in the environment
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"""

# Initialize the NSFW detection pipeline
nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")

@app.route('/')
def home():
    return "Welcome to the Image Background Remover!"

# Simple content moderation function
def is_prompt_explicit(prompt):
    explicit_keywords = ["sexual", "nudity", "erotic", "explicit", "porn", "pornographic", "xxx", "hentai", "fetish", "sex", "sensual", "nude", "strip", "stripping", "adult", "lewd", "provocative", "obscene", "vulgar", "intimacy", "intimate", "lust", "arouse", "seductive", "seduction", "kinky", "bdsm", "dominatrix", "bondage", "hardcore", "softcore", "topless", "bottomless", "threesome", "orgy", "incest", "taboo", "masturbation", "genital", "penis", "vagina", "breast", "boob", "nipple", "butt", "anal", "oral", "ejaculation", "climax", "moan", "foreplay", "intercourse", "naked", "exposed", "suicide", "self-harm", "overdose", "poison", "hang", "end life", "kill myself", "noose", "depression", "hopeless", "worthless", "die", "death", "harm myself"]  # Add more keywords as needed
    for keyword in explicit_keywords:
        if keyword.lower() in prompt.lower():
            return True
    return False

# NSFW detection function
def is_nsfw_image(image):
    results = nsfw_classifier(image)
    for result in results:
        if result['label'] == 'nsfw' and result['score'] > 0.5:
            return True
    return False

# Function to generate an image from a text prompt
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 inference API with additional parameters
        image = client.text_to_image(
            prompt=prompt, 
            negative_prompt=NEGATIVE_PROMPT_FINGERS,
            height=height, 
            width=width, 
            model=model,
            num_inference_steps=num_inference_steps,  # Control the number of inference steps
            guidance_scale=guidance_scale,  # Control the guidance scale
            seed=seed  # Control the seed for reproducibility
        )
        return image  # Return the generated image
    except Exception as e:
        print(f"Error generating image: {str(e)}")
        return None

# Flask route for the API endpoint to generate an image
@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', 1024)  # Default height
    width = data.get('width', 720)  # Default width
    num_inference_steps = data.get('num_inference_steps', 50)  # Default number of inference steps
    guidance_scale = data.get('guidance_scale', 7.5)  # Default guidance scale
    model_name = data.get('model', 'stabilityai/stable-diffusion-2-1')  # Default model
    seed = data.get('seed', None)  # Seed for reproducibility, default is None

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

    try:
        # Check for explicit content
        if is_prompt_explicit(prompt):
            return send_file(
                "thinkgood.jpeg",
                mimetype='image/png',
                as_attachment=False,
                download_name='thinkgood.png'
            )

        # 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", 
                    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')  # Convert the image to PNG
            img_byte_arr.seek(0)  # Move to the start of the byte stream

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

# Add this block to make sure your app runs when called
if __name__ == "__main__":
    app.run(host='0.0.0.0', port=7860)  # Run directly if needed for testing