Initial Commit

api_calls.py

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+import requests
+
+API_ENDPOINT = "http://54.254.230.28:8888"
+
+# function to call api
+def call_api_stream(api_path, api_params):
+    session = requests.Session()
+    url = f"{API_ENDPOINT}/{api_path}"
+    response = session.post(
+        url, json=api_params, headers={"Content-Type": "application/json"},
+        stream=True
+    )
+    return response
+
+def call_api(api_path, api_params):
+    session = requests.Session()
+    url = f"{API_ENDPOINT}/{api_path}"
+    response = session.post(
+        url, json=api_params, headers={"Content-Type": "application/json"}
+    )
+    return response.json()
+
+def api_rag_qa_chain_demo(openai_model_name, query, year, company_name):
+    api_path = "qa/demo"
+    api_params = {
+        "openai_model_name": openai_model_name,
+        "query": query,
+        "year": year,
+        "company_name": company_name,
+    }
+    return call_api_stream(api_path, api_params)
+
+def api_rag_summ_chain_demo(openai_model_name, query, year, company_name, tone):
+    api_path = "qa/waterfee"
+    api_params = {
+        "openai_model_name": openai_model_name,
+        "query": query,
+        "year": year,
+        "company_name": company_name,
+        "tone": tone,
+    }
+    return call_api_stream(api_path, api_params)

app.py

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+import arrow
+import gradio as gr
+import os
+import re
+import pandas as pd
+from pathlib import Path
+from time import sleep
+from tqdm import tqdm
+
+from api_calls import *
+
+ROOT_DIR = Path(__file__).resolve().parents[0]
+
+    
+def export_to_txt(output):
+    today_dt_str = arrow.now(tz="Asia/Taipei").format("YYYYMMDDTHHmmss")
+    with open(f"esg_report_summary-{today_dt_str}.txt", "w") as f:
+        f.write(output)
+    return f"esg_report_summary-{today_dt_str}.txt"
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, gr.Textbox(value="", interactive=False)
+
+def esgsumm_exe(openai_model_name, year, company_name, tone):
+    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。"
+    response = api_rag_summ_chain_demo(openai_model_name, query, year, company_name, tone)
+    full_anwser = ""
+    for chunk in response.iter_content(chunk_size=32):
+        if chunk:
+            try:
+                _c = chunk.decode('utf-8')
+            except UnicodeDecodeError:
+                _c = " "
+            full_anwser += _c
+            yield full_anwser
+    # for character in response:
+    #     full_text += character
+    #     yield full_text
+
+def esgqabot(history, openai_model_name, year, company_name):
+    query = history[-1][0]
+    response = api_rag_qa_chain_demo(openai_model_name, query, year, company_name)
+    history[-1][1] = ""
+    for chunk in response.iter_content(chunk_size=32):
+        if chunk:
+            try:
+                _c = chunk.decode('utf-8')
+            except UnicodeDecodeError:
+                _c = " "
+            history[-1][1] += _c
+            yield history
+    # for character in response:
+    #     history[-1][1] += character
+    #     yield history
+
+
+css = """
+#center {text-align: center}
+footer {visibility: hidden}
+a {color: rgb(255, 206, 10) !important}
+"""
+with gr.Blocks(css=css, theme=gr.themes.Monochrome(neutral_hue="lime")) as demo:
+
+    gr.HTML("<h1>ESG RAG Playground</h1>", elem_id="center")
+    gr.Markdown("Made by `Abao`", elem_id="center")
+    gr.Markdown("---")
+
+    # esgsumm
+    with gr.Tab("ESG Report Summarization"):
+        gr.HTML("<h2>Report Summarization</h2><p>Summarize report with tone & schema.</p>", elem_id="center")
+        with gr.Row():
+            with gr.Group():
+                gr.Markdown("### Configuration", elem_id="center")
+                esgsumm_report_tone = gr.Dropdown(
+                    label="Tone", 
+                    choices=["富有創意", "中庸", "精確"])
+                esgsumm_openai_model_name = gr.Dropdown(
+                    label="OpenAI Model", 
+                    choices=["gpt-4-turbo-preview", "gpt-3.5-turbo"])
+                esgsumm_year = gr.Dropdown(
+                    label="Year",
+                    choices=["111", "110", "109"]
+                )
+                esgsumm_company_name = gr.Dropdown(
+                    label="Company Name",
+                    choices=["台泥", "聯電", "裕融", "大同", "台積電", "鴻海", "中鋼", "中華電信"]
+                )
+                esgsumm_report_gen_button = gr.Button("Generate Report")
+
+        with gr.Column():
+                gr.Markdown("## Generate ESG Summarization", elem_id="center")
+                with gr.Accordion("Revise Your Prompt", open=False):
+                    esgsumm_checkbox_replace = gr.Checkbox(label="Replace with new prompt")
+                    esgsumm_prompt_tmpl = gr.Textbox(
+                        label="希望用於本次問答的prompt",
+                        info="必須使用到的變數：{filtered_data}、{query}",
+                        value=prompt_dict["qa"],
+                        interactive=True,
+                    )
+                esgsumm_report_output = gr.Textbox(
+                    label="Report Output",
+                    interactive=False,
+                    scale=4,
+                )
+                esgsumm_download_btn = gr.Button("Export Summary")
+                esgsumm_download_file = gr.File(
+                    label="Download Summary Text", file_types=[".txt"]
+                )
+
+
+    # esgqa
+    with gr.Tab("ESG QA"):
+        gr.HTML("<h2>ParallelQA (GPT-4 like)</h2><p>Test multiple LLMs at once.</p>", elem_id="center")
+        with gr.Row():
+            with gr.Group():
+                gr.Markdown("### Configuration", elem_id="center")
+                esgqa_openai_model_name = gr.Dropdown(
+                    label="OpenAI Model", 
+                    choices=["gpt-4-turbo-preview", "gpt-3.5-turbo"])
+                esgqa_year = gr.Dropdown(
+                    label="Year",
+                    choices=["111", "110", "109"]
+                )
+                esgqa_company_name = gr.Dropdown(
+                    label="Company Name",
+                    choices=["台泥", "聯電", "裕融", "大同", "台積電", "鴻海", "中鋼", "中華電信"]
+                )
+
+            with gr.Column():
+                gr.Markdown("## Chat with ESGQABot", elem_id="center")
+                with gr.Accordion("Revise Your Prompt", open=False):
+                    esgqa_checkbox_replace = gr.Checkbox(label="Replace with new prompt")
+                    esgqa_prompt_tmpl = gr.Textbox(
+                        label="希望用於本次問答的prompt",
+                        info="必須使用到的變數：{filtered_data}、{query}",
+                        value=prompt_dict["qa"],
+                        interactive=True,
+                    )
+                esgqa_chatbot = gr.Chatbot(
+                    [(None, "我是 ESGQABot\n有什麼能為您服務的嗎？")],
+                    elem_id="chatbot",
+                    scale=1,
+                    height=700,
+                    bubble_full_width=False
+                )
+                with gr.Row():
+                    esgqa_chatbot_input = gr.Textbox(
+                        scale=4,
+                        show_label=False,
+                        placeholder="Enter text and press enter, or upload an image",
+                        container=False,
+                    )
+                    esgqa_chat_btn = gr.Button("💬")
+
+
+    # esgsumm
+    esgsumm_report_gen_button.click(
+        esgsumm_exe, [esgsumm_openai_model_name, esgsumm_year, esgsumm_company_name, esgsumm_report_tone], esgsumm_report_output
+    )
+    esgsumm_download_btn.click(
+        fn=export_to_txt,
+        inputs=[esgsumm_report_output],
+        outputs=esgsumm_download_file,
+    )
+    
+    # esgqa
+    esgqa_chatbot_input.submit(
+        add_text, [esgqa_chatbot, esgqa_chatbot_input], [esgqa_chatbot, esgqa_chatbot_input], queue=False
+    ).then(
+        esgqabot, [esgqa_chatbot, esgqa_openai_model_name, esgqa_year, esgqa_company_name], esgqa_chatbot, api_name="esgqa_response"
+    ).then(
+        lambda: gr.Textbox(interactive=True), None, [esgqa_chatbot_input], queue=False
+    )
+    esgqa_chat_btn.click(
+        add_text, [esgqa_chatbot, esgqa_chatbot_input], [esgqa_chatbot, esgqa_chatbot_input], queue=False
+    ).then(
+        esgqabot, [esgqa_chatbot, esgqa_openai_model_name, esgqa_year, esgqa_company_name], esgqa_chatbot, api_name="esgqa_response"
+    ).then(
+        lambda: gr.Textbox(interactive=True), None, [esgqa_chatbot_input], queue=False
+    )
+    esgqa_chatbot.like(print_like_dislike, None, None)
+
+
+if __name__ == "__main__":
+    demo.queue().launch(max_threads=10)

requirements.txt

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+arrow
+pandas
+requests