app.py

import gradio as gr
import requests
import io
import random
import os
import time
from PIL import Image
from deep_translator import GoogleTranslator
import json
from gradio_client import Client

# Project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-3.5-large"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

# Initialize the prompt enhancer client
prompt_enhancer = Client("K00B404/mistral-nemo-prompt-enhancer")

def enhance_prompt(prompt, enable_enhancement=True):
    """Enhance the given prompt using the Mistral Nemo prompt enhancer."""
    if not enable_enhancement:
        print(f'\033[1mPrompt enhancement disabled, using original prompt:\033[0m {prompt}')
        return prompt
        
    try:
        system_message = "You are an expert at writing detailed, high-quality image generation prompts. Enhance the given prompt by adding relevant artistic details, style elements, and quality descriptors. Keep the original intent but make it more elaborate and specific."
        enhanced = prompt_enhancer.predict(
            message=prompt,
            system_message=system_message,
            max_tokens=512,
            temperature=0.7,
            top_p=0.95,
            api_name="/chat"
        )
        print(f'\033[1mOriginal prompt:\033[0m {prompt}')
        print(f'\033[1mEnhanced prompt:\033[0m {enhanced}')
        return enhanced
    except Exception as e:
        print(f"Error enhancing prompt: {e}")
        return prompt  # Fall back to original prompt if enhancement fails

# Function to query the API and return the generated image
def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, enable_enhancement=True):
    if prompt == "" or prompt is None:
        return None, None

    key = random.randint(0, 999)
    
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    
    # Translate the prompt from Russian to English if necessary
    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    # Enhance the prompt using the Mistral Nemo model if enabled
    enhanced_prompt = enhance_prompt(prompt, enable_enhancement)

    # Add some extra flair to the prompt
    final_prompt = f"{enhanced_prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key} final prompt:\033[0m {final_prompt}')
    
    # Prepare the payload for the API call, including width and height
    payload = {
        "inputs": final_prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000),
        "strength": strength,
        "parameters": {
            "width": width,
            "height": height
        }
    }

    # Send the request to the API and handle the response
    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Error: Failed to get image. Response status: {response.status_code}")
        print(f"Response content: {response.text}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
        raise gr.Error(f"{response.status_code}")
    
    try:
        # Convert the response content into an image
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({final_prompt})')
        return image, enhanced_prompt
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None, None

# CSS to style the app
css = """
#app-container {
    max-width: 800px;
    margin-left: auto;
    margin-right: auto;
}
"""

# Build the Gradio UI with Blocks
with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
    # Add a title to the app
    gr.HTML("<center><h1>Stable Diffusion 3.5 Large with Prompt Enhancement</h1></center>")
    
    # Container for all the UI elements
    with gr.Column(elem_id="app-container"):
        # Add a text input for the main prompt
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(
                        label="Prompt", 
                        placeholder="Enter a prompt here - it will be automatically enhanced for better results", 
                        lines=2, 
                        elem_id="prompt-text-input"
                    )
                
                # Accordion for advanced settings
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
                        enable_enhancement = gr.Checkbox(
                            label="Enable Prompt Enhancement",
                            value=True
                        )
                        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():
                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
                        steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
                        method = gr.Radio(
                            label="Sampling method", 
                            value="DPM++ 2M Karras", 
                            choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"]
                        )

        # Add a button to trigger the image generation
        with gr.Row():
            text_button = gr.Button("Generate Enhanced Image", variant='primary', elem_id="gen-button")
        
        # Image output area to display the generated image
        with gr.Row():
            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
        
        # Text output area to display the enhanced prompt
        with gr.Row():
            prompt_output = gr.Textbox(label="Enhanced Prompt", elem_id="prompt-output")
        
        # Bind the button to the query function with all inputs
        text_button.click(
            query, 
            inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height, enable_enhancement], 
            outputs=[image_output, prompt_output]
        )

# Launch the Gradio app
app.launch(show_api=True, share=False)