Create app.py
Browse files
app.py
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import httpcore
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setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
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#Beta3: 加入了一些常用的模型,以及將分享按鈕先關閉
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import gradio as gr
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from googletrans import Translator
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from huggingface_hub import InferenceClient
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from PIL import Image
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import time
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# 定義模型名稱列表
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# Define model names list
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models =[
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"Yntec/NostalgicLife",
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"Yntec/Genuine",
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"Yntec/Abased",
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"Yntec/CuteFurry",
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"Yntec/incha_re_zoro",
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"Yntec/InsaneM3U",
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"digiplay/2K-VAE",
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"digiplay/ya3_VAE",
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"digiplay/ya3p_VAE",
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"digiplay/pan04",
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"digiplay/AM-mix1",
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"digiplay/MRMD_0505",
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]
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# 初始化 Google 翻譯器
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# Initialize Google translator
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translator = Translator()
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# 定義翻譯函數
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# Define translation function
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def translate_to_english(prompt):
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try:
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translated_prompt = translator.translate(prompt, src='auto', dest='en').text
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return translated_prompt
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except Exception as e:
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return str(e)
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# 修改 respond_with_timestamp 函數,使其返回三個圖片對象
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# Modify the respond_with_timestamp function to return three image objects
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def respond_with_timestamp(prompt, model_name):
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client = InferenceClient(model=model_name)
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# 將提示詞翻譯成英文 Translate prompt to English
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translated_prompt = translate_to_english(prompt)
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# 使用時間戳記加在提示語後面
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prompt_with_timestamps = [f"{translated_prompt} {time.time() + i}" for i in range(3)]
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# 生成三張圖片 Generate 3 images
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images = [client.text_to_image(text) for text in prompt_with_timestamps]
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# 直接返回這三個圖片對象
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# Return image objects directly
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return images
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# 建立和啟動 Gradio 介面
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# Build and launch Gradio interface
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demo = gr.Interface(
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fn=respond_with_timestamp,
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inputs=[
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gr.Textbox(label="請輸入提示語 Please input a prompt"),
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gr.Dropdown(label="選擇模型 Choose a model", choices=models)
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],
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outputs=[gr.Image(type="pil", label=f"img-{i+1}", show_share_button = False) for i in range(3)], # 顯示三張圖片 Display three images
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title="Text-to-Image with Google Translation"
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)
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if __name__ == "__main__":
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demo.launch()
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