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Home.py
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import streamlit as st
from streamlit.components.v1 import html
from dvc.repo import Repo
from dotenv import load_dotenv
import os
load_dotenv()
st.set_page_config(
page_title="NLMyo",
page_icon="🔧",
)
st.write("# Welcome to NLMyo 🔧")
st.sidebar.success("Select the corresponding tools above.")
st.markdown(
"""
### [NLMyo🔧](https://github.com/lambda-science/NLMyo) is a toolbox built to leverage the power of Large Language Models (LLMs) to exploit histology text reports.
### NLMyo🔧 Graphic Summary
## How to Use
Select a tool on the left panel to start using NLMyo. You can upload you own PDF or click the "Load Sample PDF" button in the tools to use this [sample PDF](https://www.lbgi.fr/~meyer/IMPatienT/sample_demo_report.pdf).
Available tools:
- **Anonymizer 🕵️**: a simple web-based tool to automatically censor patient histology report PDF.
- **MyoExtract 📝:** a tool to extract metadata from histology reports such as biopsy number, muscle, diagnosis...
- **MyoClassify 🪄:** a tool to automatically predict a diagnosis of congenital myopathy subtype from an histology reports using AI (large language models). Currently can predict between: Nemaline Myopathy, Core Myopathy, Centro-nuclear Myopathy, Non Congenital Myopathy (NON-MC).
- **MyoSearch 🔎:** a tool to search for a specific term in a set of histology reports. The tool will return the top 5 reports containing closest to your symptom query from our database of reports.
🚨 DISCLAIMER: If you choose OpenAI instead of private AI in tools options, some tools will use [OpenAI API](https://openai.com/). Data will be sent to OpenAI servers. If using OpenAI Model, do not upload private or non-anonymized data. As per their terms of service [OpenAI does not retain any data (for more time than legal requirements, click for source) and do not use them for trainning.](https://openai.com/policies/api-data-usage-policies) However, we do not take any responsibility for any data leak.
## Contact
Creator and Maintainer: [**Corentin Meyer**, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://lambda-science.github.io/) <[email protected]>
The source code for NLMyo is available [HERE](https://github.com/lambda-science/NLMyo)
## Partners
MyoQuant is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte.
"""
)
if not os.path.exists("./db_myocon"):
repo = Repo()
# set password for the remote
repo.config["remote"]["ssh_lbgi_hug"]["password"] = os.getenv("DVC_PASSWORD")
repo.pull()
html(
f"""
<script defer data-domain="lbgi.fr/nlmyo" src="https://plausible.cmeyer.fr/js/script.js"></script>
"""
)