Update app.py

app.py

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+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=50, 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