app.py

import gradio as gr
import requests
import io
import random
import os
import time
from PIL import Image
import json
from threading import RLock

# Project by Nymbo

# Base API URL for Hugging Face inference
API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
# Retrieve the API token from environment variables
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
# Timeout for requests
timeout = 100

lock = RLock()

# Function to query the Hugging Face API for image generation
def query(prompt, model, negative_prompt, steps, cfg_scale, sampler, seed, strength, width, height):
    # Debug log to indicate function start
    print("Starting query function...")
    # Print the parameters for debugging purposes
    print(f"Prompt: {prompt}")
    print(f"Model: {model}")
    print(f"Parameters - Steps: {steps}, CFG Scale: {cfg_scale}, Seed: {seed}, Strength: {strength}, Width: {width}, Height: {height}")

    # Check if the prompt is empty or None
    if prompt == "" or prompt is None:
        print("Prompt is empty or None. Exiting query function.")  # Debug log
        return None

    # Randomly select an API token from available options to distribute the load
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN"), os.getenv("HF_READ_TOKEN_2"), os.getenv("HF_READ_TOKEN_3"), os.getenv("HF_READ_TOKEN_4"), os.getenv("HF_READ_TOKEN_5")])
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    print(f"Selected API token: {API_TOKEN}")  # Debug log

    # Enhance the prompt with additional details for better quality
    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'Generation: {prompt}')  # Debug log

    # Set the API URL based on the selected model
    if model == 'Stable Diffusion XL':
        API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
    # Add more model options as needed
    print(f"API URL set to: {API_URL}")  # Debug log

    # Define the payload for the request
    payload = {
        "inputs": prompt,
        "negative_prompt": negative_prompt,
        "steps": steps,  # Number of sampling steps
        "cfg_scale": cfg_scale,  # Scale for controlling adherence to prompt
        "seed": seed if seed != -1 else random.randint(1, 1000000000),  # Random seed for reproducibility
        "strength": strength,  # How strongly the model should transform the image
        "parameters": {
            "width": width,  # Width of the generated image
            "height": height  # Height of the generated image
        }
    }
    print(f"Payload: {json.dumps(payload, indent=2)}")  # Debug log

    # Make a request to the API to generate the image
    try:
        response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
        print(f"Response status code: {response.status_code}")  # Debug log
    except requests.exceptions.RequestException as e:
        # Log any request exceptions and raise an error for the user
        print(f"Request failed: {e}")  # Debug log
        raise gr.Error(f"Request failed: {e}")

    # Check if the response status is not successful
    if response.status_code != 200:
        print(f"Error: Failed to retrieve image. Response status: {response.status_code}")  # Debug log
        print(f"Response content: {response.text}")  # Debug log
        if response.status_code == 400:
            raise gr.Error(f"{response.status_code}: Bad Request - There might be an issue with the input parameters.")
        elif response.status_code == 401:
            raise gr.Error(f"{response.status_code}: Unauthorized - Please check your API token.")
        elif response.status_code == 403:
            raise gr.Error(f"{response.status_code}: Forbidden - You do not have permission to access this model.")
        elif response.status_code == 404:
            raise gr.Error(f"{response.status_code}: Not Found - The requested model could not be found.")
        elif response.status_code == 503:
            raise gr.Error(f"{response.status_code}: The model is being loaded. Please try again later.")
        else:
            raise gr.Error(f"{response.status_code}: An unexpected error occurred.")
    
    try:
        # Attempt to read the image from the response content
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'Generation completed! ({prompt})')  # Debug log
        return image
    except Exception as e:
        # Handle any errors that occur when opening the image
        print(f"Error while trying to open image: {e}")  # Debug log
        return None

# Custom CSS to hide the footer in the interface
css = """
* {}
footer {visibility: hidden !important;}
"""

print("Initializing Gradio interface...")  # Debug log

# Define the Gradio interface
with gr.Blocks(theme='Nymbo/Nymbo_Theme') as demo:
    # Tab for basic settings
    with gr.Tab('Basic Settings'):
        txt_input = gr.Textbox(label='Your prompt:', lines=4)
        model = gr.Radio(label="Select a model", value="Stable Diffusion XL", choices=["Stable Diffusion XL", "Stable Diffusion 3", "FLUX.1 [Schnell]", "RealVisXL v4.0", "Duchaiten Real3D NSFW XL", "Tempest v0.1"], interactive=True)
        gen_button = gr.Button('Generate Image')
        
    # Tab for advanced settings
    with gr.Tab("Advanced Settings"):
        with gr.Row():
            # Textbox for specifying elements to exclude from the image
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
        with gr.Row():
            # Slider for selecting the image width
            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
            # Slider for selecting the image height
            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
        with gr.Row():
            # Slider for setting the number of sampling steps
            steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
        with gr.Row():
            # Slider for adjusting the CFG scale (guidance scale)
            cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
        with gr.Row():
            # Slider for adjusting the transformation strength
            strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
        with gr.Row():
            # Slider for setting the seed for reproducibility
            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
        with gr.Row():
            # Radio buttons for selecting the sampling method
            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])

    # Set up button click event to call the query function
    gen_button.click(query, inputs=[txt_input, model, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=gr.Image(type="pil", label="Generated Image"))

print("Launching Gradio interface...")  # Debug log
# Launch the Gradio interface without showing the API or sharing externally
demo.launch(show_api=False, max_threads=400)