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import gradio as gr
import os
from functools import partial
from config import file_id_htl_biotech, file_id_kamera_express, file_id_smart_sd, file_id_sunday_naturals
import gdown
import pandas as pd
from hogwarts import get_answer
from hogwats_gemini import get_answer as get_answer_gemini
from evaluator import eval_answer
import nltk
nltk.download('punkt')
os.system("apt update; yes | apt-get install poppler-utils; yes | ls")
dico = {file_id_htl_biotech : {"name" : "htl-biotechnology", "data" : None},
file_id_smart_sd : {"name" : "smart-sd", "data" : None},
file_id_kamera_express : {"name" : "kamera-express", "data" : None},
file_id_sunday_naturals : {"name" : "sunday-naturals", "data" : None}, }
choices = ["htl-biotechnology",
"smart-sd",
"kamera-express",
"sunday-naturals"]
title = "AI4PE - Olivier and Adam \n contact: adamrida.ra@gmail.com or sp.olivier@hotmail.com"
for file_id in dico:
print("GOING FOR ", dico[file_id]["name"])
download_url = f'https://drive.google.com/uc?id={file_id}'
# Download the file using gdown
output = 'downloaded_file.csv'
gdown.download(download_url, output, quiet=False)
# Read the CSV file into a DataFrame
df = pd.read_csv(output, sep=";")[["content", "embeddings"]].replace("transcript_", "expert_meeting_notes_")
dico[file_id]["data"] = df
id_to_name_mapper = {
file_id_htl_biotech : 'htl-biotechnology',
file_id_smart_sd : 'smart-sd',
file_id_kamera_express : 'kamera-express',
file_id_sunday_naturals : 'sunday-naturals',
}
name_to_id_mapper = {
'htl-biotechnology': file_id_htl_biotech,
'smart-sd': file_id_smart_sd,
'kamera-express': file_id_kamera_express,
'sunday-naturals': file_id_sunday_naturals,
}
def get_list_files(company, dico=dico, name_to_id_mapper=name_to_id_mapper):
pdfs = []
web_pages = []
transcript = []
for ext in dico[name_to_id_mapper[company]]["data"].content.values:
# break
filename = ext.split("\n")[0]
if "SOURCE: COMPANY WEBSITE" in ext:
filename=filename.replace("https::", "").replace("https:", "").replace(".txt", "").replace(".com", " ").replace(".", " Page: ")
web_pages.append(filename)
if "SOURCE: PDF FILE" in ext:
# nb_pdfs += 1
filename = "SOURCE: UPLOADED PDF - " + ext.split("PATH_FILE =")[1].split("'}\"")[0].split("/pdfs/")[1].split("/png")[0]+".pdf"
pdfs.append(filename)
# break
# ext
pass
if "SOURCE: NOTES FROM EXPERT CALL" in ext:
# nb_expert_transcripts += 1
filename = ext.replace("_1 copy", "").replace("transcript ", "Note #").replace("transcript_1", "Note #2").replace("transcript", "Note #1").replace(".txt", "").split("\n")[0]
transcript.append(filename)
pass
# print(filename)
pdfs_string = "## Uploaded PDF files: \n" + "\n\n".join(list(set(pdfs)))
web_pages = "## Enriched from the web: \n" + "\n\n".join(list(set(web_pages)))
transcript = "## Uploaded notes from expert calls: \n" + "\n\n".join(list(set(transcript)))
return web_pages, pdfs_string, transcript
def get_data_room_overview(company, dico = dico,name_to_id_mapper = name_to_id_mapper):
nb_pdfs = 0
nb_expert_transcripts = 0
nb_web = 0
for ext in dico[name_to_id_mapper[company]]["data"].content.values:
if "SOURCE: COMPANY WEBSITE" in ext:
nb_web += 1
if "SOURCE: PDF FILE" in ext:
nb_pdfs += 1
if "SOURCE: NOTES FROM EXPERT CALL" in ext:
nb_expert_transcripts += 1
disp = f"""---
### Overview of the data room
Enriched data room with: Linkedin profile and company website
Volumetry:
- {nb_pdfs} passages from PDF files
- {nb_web} passages from company website
- {nb_expert_transcripts} passages from notes of expert calls
"""
sunday_naturals_web, sunday_naturals_pdfs, sunday_naturals_expert = get_list_files("sunday-naturals", dico, name_to_id_mapper)
smart_sd_web, smart_sd_pdfs, smart_sd_expert, = get_list_files("smart-sd", dico, name_to_id_mapper)
htl_biotech_web, htl_biotech_pdfs, htl_biotech_expert, = get_list_files("htl-biotechnology", dico, name_to_id_mapper)
kamera_express_web, kamera_express_pdfs, kamera_express_expert =get_list_files("kamera-express", dico, name_to_id_mapper)
return disp, sunday_naturals_web, sunday_naturals_pdfs,sunday_naturals_expert,smart_sd_web,smart_sd_pdfs,smart_sd_expert,htl_biotech_web,htl_biotech_pdfs,htl_biotech_expert,kamera_express_web,kamera_express_pdfs,kamera_express_expert
def generate_chat_answer(company_name, query):
df = dico[name_to_id_mapper[company_name]]["data"]
response = get_answer(df, 15, query)
print("=====> Evaluating answer quality...")
eval_score = eval(eval_answer(query, response))
eval_md = f"""
### Evalation of how well the response answer the intial question
Score of **{eval_score["score"]}/5**
Rationale:
{eval_score["rationale_based_on_scoring_rules"]}
"""
return response, eval_md
def generate_chat_answer_gemini(company_name, query):
df = dico[name_to_id_mapper[company_name]]["data"]
content = df["content"].values
response = get_answer_gemini(query, company_name, content)
print("=====> Evaluating answer quality...")
eval_score = eval(eval_answer(query, response))
eval_md = f"""
### Evalation of how well the response answer the intial question
Score of **{eval_score["score"]}/5**
Rationale:
{eval_score["rationale_based_on_scoring_rules"]}
"""
return response, eval_md
with gr.Blocks(title=title,theme='nota-ai/theme') as demo:
gr.Markdown(f"## {title}")
with gr.Row(equal_height=True):
with gr.Column(scale=1):
company_name = gr.Dropdown(choices=choices, label="Select company")
submit_button = gr.Button(value="Load workspace")
data_room_overview = gr.Markdown("---\n### Overview of the data room")
with gr.Column(scale=6):
with gr.Tab("Chat - Baseline"):
with gr.Row():
with gr.Column(scale=5):
chat_input = gr.Textbox(placeholder="Chat input", lines=2, label="Retrieve anything from the dataroom")
with gr.Column(scale=1):
chat_submit_button = gr.Button(value="Submit")
with gr.Accordion("Accuracy score", open=False):
evaluator = gr.Markdown("Waiting for answer to evaluate...")
chat_output = gr.Markdown("Waiting for question...")
with gr.Tab("Chat - ICL", interactive=True):
with gr.Row():
with gr.Column(scale=5):
chat_input_gemini = gr.Textbox(placeholder="Chat input", lines=2, label="Retrieve anything from the dataroom")
with gr.Column(scale=1):
chat_submit_button_gemini = gr.Button(value="Submit")
with gr.Accordion("Accuracy score", open=False):
evaluator_gemini = gr.Markdown("Waiting for answer to evaluate...")
chat_output_gemini = gr.Markdown("Waiting for question...")
with gr.Tab("Data", interactive = True):
with gr.Tab("Sunday Naturals"):
with gr.Row():
with gr.Column():
sunday_naturals_web = gr.Markdown("Sources obtained from website")
with gr.Column():
sunday_naturals_pdfs = gr.Markdown("Sources obtained from uploaded pdfs")
# with gr.Column():
sunday_naturals_expert = gr.Markdown("Sources obtained from expert call notes")
pass
with gr.Tab("Smart SD"):
with gr.Row():
with gr.Column():
smart_sd_web = gr.Markdown("Sources obtained from website")
with gr.Column():
smart_sd_pdfs = gr.Markdown("Sources obtained from uploaded pdfs")
# with gr.Column():
smart_sd_expert = gr.Markdown("Sources obtained from expert call notes")
pass
with gr.Tab("HTL Biotech"):
with gr.Row():
with gr.Column():
htl_biotech_web = gr.Markdown("Sources obtained from website")
with gr.Column():
htl_biotech_pdfs = gr.Markdown("Sources obtained from uploaded pdfs")
# with gr.Column():
htl_biotech_expert = gr.Markdown("Sources obtained from expert call notes")
pass
with gr.Tab("Kamera Express"):
with gr.Row():
with gr.Column():
kamera_express_web = gr.Markdown("Sources obtained from website")
with gr.Column():
kamera_express_pdfs = gr.Markdown("Sources obtained from uploaded pdfs")
# with gr.Column():
kamera_express_expert = gr.Markdown("Sources obtained from expert call notes")
pass
with gr.Tab("Benchmark", interactive=False):
pass
fn = partial(get_data_room_overview)
fn_chat = partial(generate_chat_answer)
fn_chat_gemini = partial(generate_chat_answer_gemini)
submit_button.click(fn=fn, inputs=[company_name], outputs=[
data_room_overview,
sunday_naturals_web,
sunday_naturals_pdfs,
sunday_naturals_expert,
smart_sd_web,
smart_sd_pdfs,
smart_sd_expert,
htl_biotech_web,
htl_biotech_pdfs,
htl_biotech_expert,
kamera_express_web,
kamera_express_pdfs,
kamera_express_expert])
chat_submit_button.click(fn=fn_chat, inputs=[company_name, chat_input], outputs=[chat_output, evaluator])
chat_submit_button_gemini.click(fn=fn_chat_gemini, inputs=[company_name, chat_input_gemini], outputs=[chat_output_gemini, evaluator_gemini])
login = os.environ.get("login")
pwd = os.environ.get("pwd")
demo.launch(max_threads=40, max_file_size="100mb",auth=(login, pwd))
# demo.launch(max_threads=40, max_file_size="100mb")
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