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Update app.py
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app.py
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import gradio as gr
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}
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#
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# 創建 Gradio 介面
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interface = gr.Interface(
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fn=
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inputs=gr.Textbox(lines=2, placeholder="
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outputs="
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title="最強第一組",
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description="
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)
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# 啟動 Gradio 應用
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import gradio as gr
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import requests
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import os
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# 使用 Stable Diffusion 來生成晚餐圖片
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API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
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HF_API_TOKEN = os.getenv("HF_API_TOKEN") # 確保您已在 Hugging Face Spaces 的 Secret 中設定 API token
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headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
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def query(payload):
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response = requests.post(API_URL, headers=headers, json=payload)
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if response.status_code != 200:
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print(f"Request failed with status code {response.status_code}")
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print(f"Error details: {response.text}")
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return f"Error: {response.status_code}, {response.text}"
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return response.content # 返回圖片的二進位數據
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def generate_dinner_image(hint):
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# 使用繁體中文的提示內容來對應不同的晚餐圖片描述
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dinner_hints = {
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"這是一個有很多起司的東西,通常切成片狀。": "a pizza with lots of cheese",
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"這是一道義大利麵,通常搭配奶油醬。": "a plate of creamy spaghetti",
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"這是一個烤的東西,有些人喜歡加上烤肉醬。": "grilled chicken with barbecue sauce",
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"這通常在一個麵包中,裡面有生菜和番茄。": "a hamburger with lettuce and tomato",
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"這是亞洲常見的食物,通常搭配醬油。": "sushi with soy sauce"
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}
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# 根據提示生成晚餐描述
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description = dinner_hints.get(hint, "delicious food")
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# 調用 API 生成圖像
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output = query({"inputs": description})
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# 如果返回錯誤信息,顯示錯誤
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if isinstance(output, str) and output.startswith("Error:"):
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return output # 直接返回錯誤訊息
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# 保存生成的圖像到文件
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with open("generated_dinner.png", "wb") as f:
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f.write(output)
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# 返回生成的圖像文件
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return "generated_dinner.png"
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# 創建 Gradio 介面
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interface = gr.Interface(
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fn=generate_dinner_image,
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inputs=gr.Textbox(lines=2, placeholder="輸入提示內容來生成晚餐圖片...(例如:這是一個有很多起司的東西,通常切成片狀。)"),
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outputs="image", # 生成圖片
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title="最強第一組",
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description="根據您的描述生成晚餐圖片!輸入提示內容(例如:這是一個有很多起司的東西,通常切成片狀)"
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)
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# 啟動 Gradio 應用
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