DataAgent/esg-rag-platform
0
1import arrow2import gradio as gr3import os4import re5import pandas as pd6from pathlib import Path7from time import sleep8from tqdm import tqdm9 10from api_calls import *11 12ROOT_DIR = Path(__file__).resolve().parents[0]13 14 15def export_to_txt(output):16 today_dt_str = arrow.now(tz="Asia/Taipei").format("YYYYMMDDTHHmmss")17 with open(f"esg_report_summary-{today_dt_str}.txt", "w") as f:18 f.write(output)19 return f"esg_report_summary-{today_dt_str}.txt"20 21def print_like_dislike(x: gr.LikeData):22 print(x.index, x.value, x.liked)23 24def add_text(history, text):25 history = history + [(text, None)]26 return history, gr.Textbox(value="", interactive=False)27 28def esgsumm_exe(openai_model_name, year, company_name, tone):29 query = "根據您提供的相關資訊和偏好語氣，以繁體中文生成一份符合GRI標準的報告草稿。報告將包括每個GRI披露項目的標題、相關公司行為的概要，以及公司的具體措施和效果。"30 response = api_rag_summ_chain_demo(openai_model_name, query, year, company_name, tone)31 full_anwser = ""32 for chunk in response.iter_content(chunk_size=32):33 if chunk:34 try:35 _c = chunk.decode('utf-8')36 except UnicodeDecodeError:37 _c = " "38 full_anwser += _c39 yield full_anwser40 # for character in response:41 # full_text += character42 # yield full_text43 44def esgqabot(history, openai_model_name, year, company_name):45 query = history[-1][0]46 response = api_rag_qa_chain_demo(openai_model_name, query, year, company_name)47 history[-1][1] = ""48 for chunk in response.iter_content(chunk_size=32):49 if chunk:50 try:51 _c = chunk.decode('utf-8')52 except UnicodeDecodeError:53 _c = " "54 history[-1][1] += _c55 yield history56 # for character in response:57 # history[-1][1] += character58 # yield history59 60 61css = """62#center {text-align: center}63footer {visibility: hidden}64a {color: rgb(255, 206, 10) !important}65"""66with gr.Blocks(css=css, theme=gr.themes.Monochrome(neutral_hue="lime")) as demo:67 68 gr.HTML("<h1>ESG RAG Playground</h1>", elem_id="center")69 gr.Markdown("Made by `Abao`", elem_id="center")70 gr.Markdown("---")71 72 # esgsumm73 with gr.Tab("ESG Report Summarization"):74 gr.HTML("<h2>Report Summarization</h2><p>Summarize report with tone & schema.</p>", elem_id="center")75 with gr.Row():76 with gr.Group():77 gr.Markdown("### Configuration", elem_id="center")78 esgsumm_report_tone = gr.Dropdown(79 label="Tone", 80 choices=["富有創意", "中庸", "精確"])81 esgsumm_openai_model_name = gr.Dropdown(82 label="OpenAI Model", 83 choices=["gpt-4-turbo-preview", "gpt-3.5-turbo"])84 esgsumm_year = gr.Dropdown(85 label="Year",86 choices=["111", "110", "109"]87 )88 esgsumm_company_name = gr.Dropdown(89 label="Company Name",90 choices=["台泥", "聯電", "裕融", "大同", "台積電", "鴻海", "中鋼", "中華電信"]91 )92 esgsumm_report_gen_button = gr.Button("Generate Report")93 94 with gr.Column():95 gr.Markdown("## Generate ESG Summarization", elem_id="center")96 with gr.Accordion("Revise Your Prompt", open=False):97 esgsumm_checkbox_replace = gr.Checkbox(label="Replace with new prompt")98 esgsumm_prompt_tmpl = gr.Textbox(99 label="希望用於本次問答的prompt",100 info="必須使用到的變數：{filtered_data}、{query}",101 value="",102 interactive=True,103 )104 esgsumm_report_output = gr.Textbox(105 label="Report Output",106 interactive=False,107 scale=4,108 )109 esgsumm_download_btn = gr.Button("Export Summary")110 esgsumm_download_file = gr.File(111 label="Download Summary Text", file_types=[".txt"]112 )113 114 115 # esgqa116 with gr.Tab("ESG QA"):117 gr.HTML("<h2>ParallelQA (GPT-4 like)</h2><p>Test multiple LLMs at once.</p>", elem_id="center")118 with gr.Row():119 with gr.Group():120 gr.Markdown("### Configuration", elem_id="center")121 esgqa_openai_model_name = gr.Dropdown(122 label="OpenAI Model", 123 choices=["gpt-4-turbo-preview", "gpt-3.5-turbo"])124 esgqa_year = gr.Dropdown(125 label="Year",126 choices=["111", "110", "109"]127 )128 esgqa_company_name = gr.Dropdown(129 label="Company Name",130 choices=["台泥", "聯電", "裕融", "大同", "台積電", "鴻海", "中鋼", "中華電信"]131 )132 133 with gr.Column():134 gr.Markdown("## Chat with ESGQABot", elem_id="center")135 with gr.Accordion("Revise Your Prompt", open=False):136 esgqa_checkbox_replace = gr.Checkbox(label="Replace with new prompt")137 esgqa_prompt_tmpl = gr.Textbox(138 label="希望用於本次問答的prompt",139 info="必須使用到的變數：{filtered_data}、{query}",140 value="",141 interactive=True,142 )143 esgqa_chatbot = gr.Chatbot(144 [(None, "我是 ESGQABot\n有什麼能為您服務的嗎？")],145 elem_id="chatbot",146 scale=1,147 height=700,148 bubble_full_width=False149 )150 with gr.Row():151 esgqa_chatbot_input = gr.Textbox(152 scale=4,153 show_label=False,154 placeholder="Enter text and press enter, or upload an image",155 container=False,156 )157 esgqa_chat_btn = gr.Button("💬")158 159 160 # esgsumm161 esgsumm_report_gen_button.click(162 esgsumm_exe, [esgsumm_openai_model_name, esgsumm_year, esgsumm_company_name, esgsumm_report_tone], esgsumm_report_output163 )164 esgsumm_download_btn.click(165 fn=export_to_txt,166 inputs=[esgsumm_report_output],167 outputs=esgsumm_download_file,168 )169 170 # esgqa171 esgqa_chatbot_input.submit(172 add_text, [esgqa_chatbot, esgqa_chatbot_input], [esgqa_chatbot, esgqa_chatbot_input], queue=False173 ).then(174 esgqabot, [esgqa_chatbot, esgqa_openai_model_name, esgqa_year, esgqa_company_name], esgqa_chatbot, api_name="esgqa_response"175 ).then(176 lambda: gr.Textbox(interactive=True), None, [esgqa_chatbot_input], queue=False177 )178 esgqa_chat_btn.click(179 add_text, [esgqa_chatbot, esgqa_chatbot_input], [esgqa_chatbot, esgqa_chatbot_input], queue=False180 ).then(181 esgqabot, [esgqa_chatbot, esgqa_openai_model_name, esgqa_year, esgqa_company_name], esgqa_chatbot, api_name="esgqa_response"182 ).then(183 lambda: gr.Textbox(interactive=True), None, [esgqa_chatbot_input], queue=False184 )185 esgqa_chatbot.like(print_like_dislike, None, None)186 187 188if __name__ == "__main__":189 demo.queue().launch(max_threads=10)190