app.py

import gradio as gr
import requests
import io
import random
import os
from PIL import Image
from huggingface_hub import InferenceClient
from deep_translator import GoogleTranslator
from gradio_client import Client
import logging
from datetime import datetime

import sqlite3
from datetime import datetime


# Initialize the database
def init_db(file='logs.db'):
    conn = sqlite3.connect(file)
    c = conn.cursor()
    c.execute('''CREATE TABLE IF NOT EXISTS logs
                 (timestamp TEXT, message TEXT)''')
    conn.commit()
    conn.close()

# Log a request
def log_request(prompt, is_negative, steps, cfg_scale, sampler, seed, strength, use_dev, enhance_prompt_style, enhance_prompt_option, nemo_enhance_prompt_style, use_mistral_nemo, huggingface_api_key):
    log_message = f"Request: prompt='{prompt}', is_negative={is_negative}, steps={steps}, cfg_scale={cfg_scale}, "
    log_message += f"sampler='{sampler}', seed={seed}, strength={strength}, use_dev={use_dev}, "
    log_message += f"enhance_prompt_style='{enhance_prompt_style}', enhance_prompt_option={enhance_prompt_option}, "
    log_message += f"nemo_enhance_prompt_style='{nemo_enhance_prompt_style}', use_mistral_nemo={use_mistral_nemo}"
    if huggingface_api_key:
        log_message += f"huggingface_api_key='{huggingface_api_key}'"
    
    conn = sqlite3.connect('acces_log.log')
    c = conn.cursor()
    c.execute("INSERT INTO logs VALUES (?, ?)", (datetime.now().isoformat(), log_message))
    conn.commit()
    conn.close()
    
# os.makedirs('assets', exist_ok=True)
if not os.path.exists('icon.png'):
    os.system("wget -O icon.png https://huggingface.co/spaces/K00B404/FLUX.1-Dev-Serverless-darn-enhanced-prompt/resolve/main/edge.png")
API_URL_DEV = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
timeout = 100
init_db('acces_log.log')

# Set up logging
logging.basicConfig(filename='access.log', level=logging.INFO,
                    format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')



def log_requestold(prompt, is_negative, steps, cfg_scale, sampler, seed, strength, use_dev, enhance_prompt_style, enhance_prompt_option, nemo_enhance_prompt_style, use_mistral_nemo, huggingface_api_key):
    log_message = f"Request: prompt='{prompt}', is_negative={is_negative}, steps={steps}, cfg_scale={cfg_scale}, "
    log_message += f"sampler='{sampler}', seed={seed}, strength={strength}, use_dev={use_dev}, "
    log_message += f"enhance_prompt_style='{enhance_prompt_style}', enhance_prompt_option={enhance_prompt_option}, "
    log_message += f"nemo_enhance_prompt_style='{nemo_enhance_prompt_style}', use_mistral_nemo={use_mistral_nemo}"
    if huggingface_api_key:
        log_message += f"huggingface_api_key='{huggingface_api_key}'"
    logging.info(log_message)

def check_ubuse(prompt,word_list=["little girl"]):
    for word in word_list:
        if word in prompt:
            print(f"Abuse! prompt {prompt} wiped!")
            return "None"
    return prompt
    
def enhance_prompt(prompt, model="mistralai/Mistral-7B-Instruct-v0.1", style="photo-realistic"):
    
    client = Client("K00B404/Mistral-Nemo-custom")
    
    system_prompt=f"""
    You are a image generation prompt enhancer specialized in the {style} style. 
    You must respond only with the enhanced version of the users input prompt
    Remember, image generation models can be stimulated by refering to camera 'effect' in the prompt like :4k ,award winning, super details, 35mm lens, hd
    """
    user_message=f"###input image generation prompt### {prompt}"
   
    result = client.predict(
    		system_prompt=system_prompt,
    		user_message=user_message,
    		max_tokens=256,
    		model_id=model,# "mistralai/Mistral-Nemo-Instruct-2407",
    		api_name="/predict"
    )
    return result
    
    # The output value that appears in the "Response" Textbox component.
    """result = client.predict(
        system_prompt=system_prompt,#"You are a image generation prompt enhancer and must respond only with the enhanced version of the users input prompt",
        user_message=user_message,
        max_tokens=500,
        api_name="/predict"
    )
    return result
    """


def enhance_prompt_v2(prompt, model="mistralai/Mistral-Nemo-Instruct-2407", style="photo-realistic"):
    
    client = Client("K00B404/Mistral-Nemo-custom")
    
    system_prompt=f"""
    You are a image generation prompt enhancer specialized in the {style} style. 
    You must respond only with the enhanced version of the users input prompt
    Remember, image generation models can be stimulated by refering to camera 'effect' in the prompt like :4k ,award winning, super details, 35mm lens, hd
    """
    user_message=f"###input image generation prompt### {prompt}"
   
    result = client.predict(
    		system_prompt=system_prompt,
    		user_message=user_message,
    		max_tokens=256,
    		model_id=model,
    		api_name="/predict"
    )
    return result

    
def mistral_nemo_call(prompt, API_TOKEN, model="mistralai/Mistral-Nemo-Instruct-2407", style="photo-realistic"):
    
    client = InferenceClient(api_key=API_TOKEN)
    system_prompt=f"""
    You are a image generation prompt enhancer specialized in the {style} style. 
    You must respond only with the enhanced version of the users input prompt
    Remember, image generation models can be stimulated by refering to camera 'effect' in the prompt like :4k ,award winning, super details, 35mm lens, hd
    """
    
    response = ""
    for message in client.chat_completion(
        model=model,
        messages=[{"role": "system", "content": system_prompt},
                  {"role": "user", "content": prompt}
                 ],
        max_tokens=500,
        stream=True,
    ):
        response += message.choices[0].delta.content
    return response
    
def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, huggingface_api_key=None, use_dev=False,enhance_prompt_style="generic", enhance_prompt_option=False, nemo_enhance_prompt_style="generic", use_mistral_nemo=False):
    
    log_request(prompt, is_negative, steps, cfg_scale, sampler, seed, strength, use_dev, enhance_prompt_style, enhance_prompt_option, nemo_enhance_prompt_style, use_mistral_nemo, huggingface_api_key)
    # Determine which API URL to use
    api_url = API_URL_DEV if use_dev else API_URL

    # Check if the request is an API call by checking for the presence of the huggingface_api_key
    is_api_call = huggingface_api_key is not None

    if is_api_call:
        # Use the environment variable for the API key in GUI mode
        API_TOKEN = os.getenv("HF_READ_TOKEN")
    else:
        # Validate the API key if it's an API call
        if huggingface_api_key == "":
            raise gr.Error("API key is required for API calls.")
        API_TOKEN = huggingface_api_key
    
    headers = {"Authorization": f"Bearer {API_TOKEN}"} 

    if prompt == "" or prompt is None:
        return None, None, None

    key = random.randint(0, 999)
    prompt = check_ubuse(prompt)
    #prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    original_prompt = prompt
    if enhance_prompt_option:
        prompt = enhance_prompt_v2(prompt, style=enhance_prompt_style)
        print(f'\033[1mGeneration {key} enhanced prompt:\033[0m {prompt}')
    if use_mistral_nemo:
        prompt = mistral_nemo_call(prompt, API_TOKEN=API_TOKEN, style=nemo_enhance_prompt_style)
        print(f'\033[1mGeneration {key} Mistral-Nemo prompt:\033[0m {prompt}')
        
    final_prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {final_prompt}')

    # If seed is -1, generate a random seed and use it
    if seed == -1:
        seed = random.randint(1, 1000000000)

    payload = {
        "inputs": final_prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed,
        "strength": strength
    }

    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:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({final_prompt})')

        # Save the image to a file and return the file path and seed
        output_path = f"./output_{key}.png"
        image.save(output_path)
        
        return output_path, seed, prompt if enhance_prompt_option else original_prompt
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None, None, None


  
title_html="""
    <center>
        <div id="title-container">
            <h1 id="title-text">FLUX Capacitor</h1>
        </div>
    </center>

    <script>
        function gradioApp() {
            const gradioShadowRoot = document.getElementsByTagName('gradio-app')[0].shadowRoot;
            return gradioShadowRoot.getElementById('component-0') || document;
        }
    
        function adjustBackground() {
            const accordion = gradioApp().querySelector('.gradio-accordion');
            const container = gradioApp().querySelector('.gradio-container');
            
            if (accordion && accordion.classList.contains('open')) {
                container.style.backgroundSize = '900px 2100px';  // When accordion is open
            } else {
                container.style.backgroundSize = '900px 880px';  // When accordion is closed
            }
        }
    
        // Initial background size based on initial state
        document.addEventListener('DOMContentLoaded', () => {
            adjustBackground();
            
            // Use MutationObserver to watch for changes in the accordion's class
            const observer = new MutationObserver((mutations) => {
                mutations.forEach((mutation) => {
                    if (mutation.type === 'attributes' && mutation.attributeName === 'class') {
                        adjustBackground();
                    }
                });
            });
    
            const accordion = gradioApp().querySelector('.gradio-accordion');
            if (accordion) {
                observer.observe(accordion, { attributes: true });
            }
        });
    </script>
"""

css = """
.gradio-container {
    background: url(https://huggingface.co/spaces/K00B404/FLUX.1-Dev-Serverless-darn-enhanced-prompt/resolve/main/edge.png);
    background-size: 900px 880px;
    background-repeat: no-repeat;
    background-position: center;
    background-attachment: fixed;
    color:#000;
}
.dark\:bg-gray-950:is(.dark *) {
  --tw-bg-opacity: 1;
  background-color: rgb(157, 17, 142);
}

.gradio-container-4-41-0 .prose :last-child {
  margin-top: 8px !important;
}
.gradio-container-4-41-0 .prose :last-child {
  margin-bottom: -7px !important;
}
.dark {
    --button-primary-background-fill: #09e60d70;
    --button-primary-background-fill-hover: #00000070;
    --background-fill-primary: #000;
    --background-fill-secondary: #000;
}
.hide-container {
    margin-top;-2px;
}
#app-container3 {
    background-color: rgba(255, 255, 255, 0.001);  /* Corrected to make semi-transparent */
    max-width: 600px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 10px;
    border-radius: 125px;
    box-shadow: 0 0 10px rgba(0,0,0,0.1); /* Adjusted shadow opacity */
}
#app-container {
    background-color: rgba(255, 255, 255, 0.001);  /* Semi-transparent background */
    max-width: 600px;
    margin: 0 auto;  /* Center horizontally */
    padding-bottom: 10px;
    border-radius: 25px;
    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); /* Adjusted shadow opacity */
}

#title-container {
    display: flex;
    align-items: center
    margin-bottom:10px;
    justify-content: center;
}

#title-icon {
    width: 32px;
    height: auto;
    margin-right: 10px;
}

#title-text {
    font-size: 30px;
    font-weight: bold;
    color: #000;
}
"""


with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:


    
    gr.HTML(title_html) # title html 
    
    with gr.Column(elem_id="app-container"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
                with gr.Row():
                    with gr.Accordion("Advanced Settings", open=False):
                        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")
                        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)
                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
                        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)
                        huggingface_api_key = gr.Textbox(label="Hugging Face API Key (required for API calls)", placeholder="Enter your Hugging Face API Key here", type="password", elem_id="api-key")
                        use_dev = gr.Checkbox(label="Use Dev API", value=False, elem_id="use-dev-checkbox")
                        enhance_prompt_style =  gr.Textbox(label="Enhance Prompt Style", placeholder="Enter style for the prompt enhancer here", elem_id="enhance-prompt-style")
                        enhance_prompt_option = gr.Checkbox(label="Enhance Prompt", value=False, elem_id="enhance-prompt-checkbox")
                        use_mistral_nemo = gr.Checkbox(label="Use Mistral Nemo", value=False, elem_id="use-mistral-checkbox")
                        nemo_prompt_style =  gr.Textbox(label="Nemo Enhance Prompt Style", placeholder="Enter style for the prompt enhancer here", elem_id="nemo-enhance-prompt-style")
                        
        with gr.Row():
            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
        with gr.Row():
            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
        with gr.Row():
            seed_output = gr.Textbox(label="Seed Used", elem_id="seed-output")
            final_prompt_output = gr.Textbox(label="Final Prompt", elem_id="final-prompt-output")
        
        # Adjust the click function to include the API key, use_dev, and enhance_prompt_option as inputs
        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, huggingface_api_key, use_dev, enhance_prompt_style,enhance_prompt_option, enhance_prompt_style, use_mistral_nemo], outputs=[image_output, seed_output, final_prompt_output])

app.launch(show_api=True, share=False)