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vaitor2

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check_file_exists
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extract_youtube_id
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from_service_account_info
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google-auth
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print("=====Google Drive 上的前10個文件=====")
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demo.launch(allowed_paths=["videos"])
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demo.launch(allowed_paths=["../videos"])
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screenshot_paths
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image_path
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screenshot_path = f'{OUTPUT_PATH}/{youtube_id}_{snapshot_sec}.jpg'
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html_content += f"<img src='{entry['screenshot_path']}' width='500px' />"
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html_content += f"<p>{entry['time_sec']} </p> <br><br>"
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html_content = format_transcript_to_html(formatted_transcript)
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df_summarise = generate_df_summarise(transcript)
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html_content
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print(html_content)
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transcript_html
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def get_screenshot_from_video(video_link, start_time):
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line = f"---- 時間： {start_time} ～ {end_time} ----\n{entry['text']}\n\n"
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def format_seconds_to_time(seconds):
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formatted_transcript
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gr.Markdown("## 這是什麼樣的資料？")
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YouTubeTranscriptApi
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def process_youtube_link(link):
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add yt, web
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df_string = df_string.replace("@XX@", "|")
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"max_tokens": 4000,
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df_summarise = gr.Textbox(container=True, show_copy_button=True, label="資料本文", lines=40)
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df_string_output = gr.Textbox(container=True, show_copy_button=True, label="資料本文", lines=20)
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user_content = f"請根據 {df_string}，大概描述這張表的欄位敘述、資料樣態與資料分析，告訴老師這張表的意義，以及可能的結論與對應方式"
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def generate_df_summarise(df_string):
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def generate_df_summarise(df_string):
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gr.Markdown("## 常用問題")
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questions
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questions = json_response # 示例问题列表
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json_response = json.loads(response.choices[0].message.content)["questions"]
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user_content
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sys_content = f"你是一個資料分析師，請精讀資料，並用既有資料為本質猜測用戶可能會問的問題，使用 zh-TW"
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sys_content = f"你是一個資料分析師，請精讀資料，並用既有資料為本質猜測用戶可能會問的問題，使用 zh-TW"
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print(json_response)
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return respond(button, df_string_output, chat_history)
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df_string = df.to_string()
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def update_buttons(df_string):
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question_buttons = gr.Container()
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df_string_output = gr.Textbox(label="raw data")
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send_button.click(
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with gr.Blocks() as demo:
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