Update app.py

app.py

@@ -1,123 +0,0 @@
-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 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)
-
-@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
-
-# Function to generate an image from a text prompt
-def generate_image(prompt, negative_prompt=None, height=512, width=512, model="stabilityai/sd-3.5", 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,
-            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
-
-# Function to refine an image using the refiner model
-def refine_image(image, prompt, negative_prompt=None, model="stabilityai/stable-diffusion-xl-refiner-1.0", num_inference_steps=15, guidance_scale=7.5):
-    try:
-        # Use Hugging Face's image-to-image API to refine the image
-        refined_image = client.image_to_image(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            image=image,
-            model=model,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale
-        )
-        return refined_image
-    except Exception as e:
-        print(f"Error refining image: {str(e)}")
-        return None
-
-@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/sd-3.5')  # Base model
-    refiner_model_name = 'stabilityai/sd-xl-refiner-1.0'  # Refiner 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 the pre-defined "thinkgood.png" image
-            return send_file(
-                "thinkgood.jpeg",
-                mimetype='image/png',
-                as_attachment=False,
-                download_name='thinkgood.png'
-            )
-
-        # Step 1: Generate the base image
-        base_image = generate_image(prompt, negative_prompt, height, width, model_name, num_inference_steps, guidance_scale, seed)
-        
-        if not base_image:
-            return jsonify({"error": "Failed to generate base image"}), 500
-
-        # Step 2: Refine the image with the refiner model
-        refined_image = refine_image(base_image, prompt, negative_prompt, refiner_model_name, num_inference_steps, guidance_scale)
-        
-        if not refined_image:
-            return jsonify({"error": "Failed to refine image"}), 500
-
-        # Save the refined image to a BytesIO object
-        img_byte_arr = BytesIO()
-        refined_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 refined image as a response
-        return send_file(
-            img_byte_arr,
-            mimetype='image/png',
-            as_attachment=False,  # Send the file inline
-            download_name='refined_image.png'  # File name for download
-        )
-    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