Update app.py
Browse files
app.py
CHANGED
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import
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from gradio_client import Client
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import os
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import warnings
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# κ²½κ³ λ©μμ§ μ¨κΈ°κΈ°
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warnings.filterwarnings('ignore', category=UserWarning)
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#
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try:
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# API νΈμΆμ ν΅ν΄ μ΄λ―Έμ§ μμ±
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result = gr.Image.update(value=None) # μ΄κΈ° μνλ λΉ μ΄λ―Έμ§
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# Hugging Face API νΈμΆ
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client = Client(
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"https://black-forest-labs-flux-1-schnell.hf.space", # API μλν¬μΈνΈ μ§μ μ§μ
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hf_token=HF_TOKEN,
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)
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# μ΄λ―Έμ§ μμ±
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result = client.predict(
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prompt,
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1872187377, # seed
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False, # randomize_seed
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width,
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height,
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4, # num_inference_steps
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api_name="/infer"
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)
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return result
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except Exception as e:
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raise gr.Error(f"λ€μ΄μ΄κ·Έλ¨ μμ± μ€ μ€λ₯ λ°μ: {str(e)}")
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#
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KNOWLEDGE
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βββ ACQUISITION [Brain with Lightning ~60px]
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β βββ READING [Open Book with Glow]
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β βββ PRACTICE [Hands-on Tools]
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βββ APPLICATION
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βββ CREATION [Artist Palette]
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βββ INNOVATION [Lightbulb]""",
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1024,
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1024
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],
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[
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"""A handrawn colorful mind map diagram, tech-focused style, neon accents.
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DIGITAL TRANSFORM
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βββ CLOUD [Cloud with Data ~55px]
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β βββ STORAGE [Database]
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β βββ COMPUTING [Server]
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βββ SECURITY
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βββ ENCRYPTION [Lock]
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βββ MONITORING [Radar]""",
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1024,
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1024
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],
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[
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"""A handrawn colorful mind map diagram, creative style, flowing design.
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INNOVATION
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βββ IDEATION [Lightbulb ~60px]
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β βββ RESEARCH [Magnifier]
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β βββ BRAINSTORM [Brain]
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βββ EXECUTION
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βββ PROTOTYPE [Tools]
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βββ TEST [Checklist]""",
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1024,
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1024
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]
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]
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# Enhanced examples with more detailed prompts and specific styling
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EXAMPLES = [
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}
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]
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# Convert examples to Gradio format
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GRADIO_EXAMPLES = [
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[example["prompt"], example["width"], example["height"]]
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for example in EXAMPLES
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]
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demo = gr.Interface(
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fn=generate_diagram,
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inputs=[
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gr.Slider(
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label="λλΉ",
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minimum=512,
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maximum=
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step=128,
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value=1024
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),
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gr.Slider(
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label="λμ΄",
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minimum=512,
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maximum=
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step=128,
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value=1024
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)
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cache_examples=True
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)
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# μ± μ€ν
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if __name__ == "__main__":
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demo.queue()
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demo.launch(
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server_name="0.0.0.0",
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server_port=7860,
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import random
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import os
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import uuid
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from datetime import datetime
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import gradio as gr
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import numpy as np
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import torch
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from diffusers import DiffusionPipeline
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from PIL import Image
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import warnings
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# κ²½κ³ λ©μμ§ μ¨κΈ°κΈ°
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warnings.filterwarnings('ignore', category=UserWarning)
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# μ μ₯ λλ ν 리 μμ±
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SAVE_DIR = "saved_images"
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if not os.path.exists(SAVE_DIR):
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os.makedirs(SAVE_DIR, exist_ok=True)
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# μ₯μΉ μ€μ
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# λͺ¨λΈ λ‘λ
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repo_id = "black-forest-labs/FLUX.1-schnell"
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pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float32)
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pipeline = pipeline.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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# Enhanced examples with more detailed prompts and specific styling
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EXAMPLES = [
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}
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]
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# Convert examples to Gradio format
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GRADIO_EXAMPLES = [
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[example["prompt"], example["width"], example["height"]]
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for example in EXAMPLES
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]
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def generate_diagram(prompt, width=1024, height=1024):
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"""FLUX AIλ₯Ό μ¬μ©νμ¬ λ€μ΄μ΄κ·Έλ¨ μμ±"""
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try:
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# μλ μ€μ
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator(device=device).manual_seed(seed)
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# μ΄λ―Έμ§ μμ±
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image = pipeline(
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prompt=prompt,
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width=width,
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height=height,
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num_inference_steps=4,
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generator=generator,
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).images[0]
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# μ΄λ―Έμ§ μ μ₯
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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unique_id = str(uuid.uuid4())[:8]
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filename = f"diagram_{timestamp}_{unique_id}.png"
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save_path = os.path.join(SAVE_DIR, filename)
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image.save(save_path)
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return image
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except Exception as e:
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raise gr.Error(f"λ€μ΄μ΄κ·Έλ¨ μμ± μ€ μ€λ₯ λ°μ: {str(e)}")
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# Gradio μΈν°νμ΄μ€ μμ±
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demo = gr.Interface(
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fn=generate_diagram,
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inputs=[
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gr.Slider(
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label="λλΉ",
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minimum=512,
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maximum=MAX_IMAGE_SIZE,
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step=128,
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value=1024
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),
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gr.Slider(
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label="λμ΄",
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minimum=512,
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maximum=MAX_IMAGE_SIZE,
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step=128,
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value=1024
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)
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cache_examples=True
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)
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# μ± μ€ν
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if __name__ == "__main__":
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demo.queue()
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demo.launch(
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server_name="0.0.0.0",
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server_port=7860,
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