app.py

import streamlit as st
from streamlit_agraph import agraph, Node, Edge, Config
import os
from sqlalchemy import create_engine, text
import pandas as pd
from utils import get_all_diseases_name, get_most_similar_diseases_from_uri, get_uri_from_name, get_diseases_related_to_a_textual_description
import json


username = 'demo'
password = 'demo'
hostname = os.getenv('IRIS_HOSTNAME', 'localhost')
port = '1972' 
namespace = 'USER'
CONNECTION_STRING = f"iris://{username}:{password}@{hostname}:{port}/{namespace}"
engine = create_engine(CONNECTION_STRING)

def handle_click_on_analyze_button():
    # 1. Embed the textual description that the user entered using the model
    diseases_related_to_the_user_text = get_diseases_related_to_a_textual_description(description_input)
    # 2. Get 5 diseases with the highest cosine silimarity from the DB
    # 3. Get the similarities of the embeddings of those diseases (cosine similarity of the embeddings of the nodes of such diseases)
    # 4. Potentially filter out the diseases that are not similar enough (e.g. similarity < 0.8)
    # 5. Augment the set of diseases: add new diseases that are similar to the ones that are already in the set, until we get 10-15 diseases
    # 6. Query the embeddings of the diseases related to each clinical trial (also in the DB), to get the most similar clinical trials to our set of diseases
    # 7. Use an LLM to get a summary of the clinical trials, in plain text format
    # 8. Use an LLM to extract numerical data from the clinical trials (e.g. number of patients, number of deaths, etc.). Get summary statistics out of that.
    # 9. Show the results to the user: graph of the diseases chosen, summary of the clinical trials, summary statistics of the clinical trials, and list of the details of the clinical trials considered
    pass


st.write("# Klìnic")

description_input = st.text_input(label="Enter the disease description 👇")

st.write(":red[Here should be the graph]")  # TODO remove
chart_data = pd.DataFrame(
    np.random.randn(20, 3), columns=["a", "b", "c"]
)  # TODO remove
st.scatter_chart(chart_data)  # TODO remove

st.write("## Disease Overview")
disease_overview = ":red[lorem ipsum]"  # TODO
st.write(disease_overview)

st.write("## Clinical Trials Details")
trials = []
# TODO replace mock data
with open("mock_trial.json") as f:
    d = json.load(f)
for i in range(0, 5):
    trials.append(d)

for trial in trials:
    with st.expander(f"{trial['protocolSection']['identificationModule']['nctId']}"):
        official_title = trial["protocolSection"]["identificationModule"][
            "officialTitle"
        ]
        st.write(f"##### {official_title}")

        brief_summary = trial["protocolSection"]["descriptionModule"]["briefSummary"]
        st.write(brief_summary)

        status_module = {
            "Status": trial["protocolSection"]["statusModule"]["overallStatus"],
            "Status Date": trial["protocolSection"]["statusModule"][
                "statusVerifiedDate"
            ],
        }
        st.write("###### Status")
        st.table(status_module)

        design_module = {
            "Study Type": trial["protocolSection"]["designModule"]["studyType"],
            # "Phases": trial["protocolSection"]["designModule"]["phases"], # breaks formatting because it is an array
            "Allocation": trial["protocolSection"]["designModule"]["designInfo"][
                "allocation"
            ],
            "Participants": trial["protocolSection"]["designModule"]["enrollmentInfo"][
                "count"
            ],
        }
        st.write("###### Design")
        st.table(design_module)

        # TODO more modules?