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index.html
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<!DOCTYPE html>
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<html>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" />
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<style>
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html, body {
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margin: 0;
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padding: 0;
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height: 100%;
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}
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</style>
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</head>
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<body>
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<gradio-lite>
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<gradio-file name="app.py" entrypoint>
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import gradio as gr
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demo = gr.Interface(
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process,
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"image",
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"image",
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examples=["lion.jpg", "logo.png"],
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)
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<gradio-file name="filters.py">
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from skimage.color import rgb2gray
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def as_gray(image):
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return rgb2gray(image)
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</gradio-file>
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<gradio-file name="lion.jpg" url="https://raw.githubusercontent.com/gradio-app/gradio/main/gradio/test_data/lion.jpg" />
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<gradio-file name="logo.png" url="https://raw.githubusercontent.com/gradio-app/gradio/main/guides/assets/logo.png" />
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# Same syntax as requirements.txt
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scikit-image
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</gradio-requirements>
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</gradio-lite>
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</body>
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</html>
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<!DOCTYPE html>
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<html>
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<head>
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<!-- <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto&display=swap" > -->
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<style>
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body {
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font-family: 'Roboto', sans-serif;
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font-size: 16px;
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}
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.logo {
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height: 1em;
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vertical-align: middle;
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margin-bottom: 0.1em;
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}
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</style>
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<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite@0.4.1/dist/lite.js"></script>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite@0.4.1/dist/lite.css" />
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</head>
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<body>
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<h2>
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<!-- <img src="lite-logo.png" alt="logo" class="logo"> -->
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Gradio-lite (Gradio running entirely in your browser!)
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</h2>
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<p>Try it out! Once the Gradio app loads (can take 10-15 seconds), disconnect your Wifi and the machine learning model will still work!</p>
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<gradio-lite>
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<gradio-file name="app.py" entrypoint>
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import gradio as gr
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from tempfile import NamedTemporaryFile
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def translate_subtitles(english_srt, translated_json):
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with open(english_srt, 'r', encoding='utf-8') as file:
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english_subtitles = file.read().strip().split('\n\n')
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with open(translated_json, 'r', encoding='utf-8') as file:
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translated_texts = eval(file.read())
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# 将上传的JSON文件内容转化为字典
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# 确保翻译文本的数量与SRT文件中的条目数量相同
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assert len(translated_texts) == len(english_subtitles)
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# 用来存储最终翻译的SRT内容
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translated_srt_content = []
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# 遍历SRT文件中的每个条目，并用翻译后的文本替换原文本
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for index, block in enumerate(english_subtitles):
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lines = block.split('\n')
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translated_srt_content.append(lines[0]) # 序号
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translated_srt_content.append(lines[1]) # 时间轴
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translated_srt_content.append(translated_texts[index]) # 翻译文本
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translated_srt_content.append('') # 添加空行作为字幕块的分隔
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# 将列表转换为字符串，每个元素之间以换行符连接
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translated_srt_string = '\n'.join(translated_srt_content)
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with NamedTemporaryFile(delete=False, suffix='.srt', mode='w', encoding='utf-8') as tmp_file:
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tmp_file.write(translated_srt_string)
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return tmp_file.name # 返回文件路径以便下载
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# 创建Gradio接
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iface = gr.Interface(
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fn=translate_subtitles,
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inputs=[
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gr.File(label="Upload English SRT File"),
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gr.File(label="Upload Translated Texts JSON File")
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],
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outputs=gr.File(label="Download Translated SRT File")
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)
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# 启动Gradio界面
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iface.launch()
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</gradio-file>
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</gradio-lite>
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</body>
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</html>
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