Commit History

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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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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,使用 zh-TW
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df_string_output
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df_string_output = gr.Textbox(label="")
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df_string_output
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OPEN_AI_KEY
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df_string_output
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from openai import OpenAI
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chatbot
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debuig
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