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

# Project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

# Function to query the API and return the generated image
def query(prompt, negative_prompt, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
    if not prompt:
        raise gr.Error("Prompt cannot be empty.")

    key = random.randint(0, 999)

    # Translate the prompt from Russian to English if necessary
    prompt = GoogleTranslator(source='my', target='en').translate(prompt)
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    # Add some extra flair to the prompt
    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')

    # Prepare the payload for the API call, including width and height
    payload = {
        "inputs": prompt,
        "parameters": {
            "negative_prompt": negative_prompt,
            "steps": steps,
            "cfg_scale": cfg_scale,
            "sampler": sampler,
            "seed": seed if seed != -1 else random.randint(1, 1000000000),
            "strength": strength,
            "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:
        error_message = f"Error: Failed to get image. Response status: {response.status_code}"
        if response.status_code == 503:
            error_message += " - The model is being loaded"
        raise gr.Error(error_message)

    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 ({prompt})')
        return image
    except Exception as e:
        raise gr.Error(f"Error when trying to open the image: {e}")

# 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>Walone AI Image Pro Large</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="Prompt စာသားရေးပါ", lines=2, elem_id="prompt-text-input")

                # Accordion for advanced settings
                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")
                        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) # Setting the seed to -1 will make it random
                        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("Run", 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")

        # Bind the button to the query function with the added width and height inputs
        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)

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