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Create app.py
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app.py
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import gradio as gr
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import requests
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import io
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import random
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import os
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from PIL import Image
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from deep_translator import GoogleTranslator
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# os.makedirs('assets', exist_ok=True)
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if not os.path.exists('icon.jpg'):
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os.system("wget -O icon.jpg https://i.pinimg.com/564x/64/49/88/644988c59447eb00286834c2e70fdd6b.jpg")
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API_URL_DEV = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
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API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
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timeout = 100
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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):
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# Determine which API URL to use
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api_url = API_URL_DEV if use_dev else API_URL
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# Check if the request is an API call by checking for the presence of the huggingface_api_key
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is_api_call = huggingface_api_key is not None
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if is_api_call:
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# Use the environment variable for the API key in GUI mode
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API_TOKEN = os.getenv("HF_READ_TOKEN")
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headers = {"Authorization": f"Bearer {API_TOKEN}"}
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else:
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# Validate the API key if it's an API call
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if huggingface_api_key == "":
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raise gr.Error("API key is required for API calls.")
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headers = {"Authorization": f"Bearer {huggingface_api_key}"}
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if prompt == "" or prompt is None:
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return None
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key = random.randint(0, 999)
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prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
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print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
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prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
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print(f'\033[1mGeneration {key}:\033[0m {prompt}')
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# If seed is -1, generate a random seed and use it
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if seed == -1:
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seed = random.randint(1, 1000000000)
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payload = {
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"inputs": prompt,
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"is_negative": is_negative,
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"steps": steps,
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"cfg_scale": cfg_scale,
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"seed": seed,
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"strength": strength
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}
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response = requests.post(api_url, headers=headers, json=payload, timeout=timeout)
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if response.status_code != 200:
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print(f"Error: Failed to get image. Response status: {response.status_code}")
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print(f"Response content: {response.text}")
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if response.status_code == 503:
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raise gr.Error(f"{response.status_code} : The model is being loaded")
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raise gr.Error(f"{response.status_code}")
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try:
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image_bytes = response.content
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image = Image.open(io.BytesIO(image_bytes))
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print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
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# Save the image to a file and return the file path and seed
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output_path = f"./output_{key}.png"
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image.save(output_path)
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return output_path, seed
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except Exception as e:
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print(f"Error when trying to open the image: {e}")
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return None, None
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css = """
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#app-container {
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max-width: 600px;
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margin-left: auto;
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margin-right: auto;
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}
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#title-container {
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display: flex;
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align-items: center;
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justify-content: center;
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}
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#title-icon {
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width: 32px; /* Adjust the width of the icon as needed */
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height: auto;
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margin-right: 10px; /* Space between icon and title */
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}
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#title-text {
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font-size: 24px; /* Adjust font size as needed */
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font-weight: bold;
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}
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"""
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with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
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gr.HTML("""
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<center>
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<div id="title-container">
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<img id="title-icon" src="icon.jpg" alt="Icon">
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<h1 id="title-text">FLUX Capacitor</h1>
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</div>
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</center>
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""")
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with gr.Column(elem_id="app-container"):
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with gr.Row():
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with gr.Column(elem_id="prompt-container"):
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with gr.Row():
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text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
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with gr.Row():
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with gr.Accordion("Advanced Settings", open=False):
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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")
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steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
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cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
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method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
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strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
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seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
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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")
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use_dev = gr.Checkbox(label="Use Dev API", value=False, elem_id="use-dev-checkbox")
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with gr.Row():
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text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
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with gr.Row():
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image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
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seed_output = gr.Textbox(label="Seed Used", elem_id="seed-output")
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# Adjust the click function to include the API key and use_dev as inputs
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text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, huggingface_api_key, use_dev], outputs=[image_output, seed_output])
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app.launch(show_api=True, share=False)
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