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
import time
from utils import (
    get_all_diseases_name,
    get_most_similar_diseases_from_uri,
    get_uri_from_name,
    get_diseases_related_to_a_textual_description,
    get_similarities_among_diseases_uris,
    augment_the_set_of_diseaces,
    get_clinical_trials_related_to_diseases,
    get_clinical_records_by_ids,
    render_trial_details
)
from llm_res import get_short_summary_out_of_json_files
import json
import numpy as np
from sentence_transformers import SentenceTransformer


# variables to reveal next steps
show_graph = False
show_analyze_status = False
show_overview = False
show_details = False

# IRIS connection
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)


st.image("img_klinic.jpeg", caption="(AI-generated image)", use_column_width=True)
st.title("Klìnic", help="AI-powered clinical trial search engine")
st.subheader("Find clinical trials in a scoped domain of biomedical research, guiding your research with AI-powered insights.")

with st.container(): # user input
    col1, col2 = st.columns((6, 1))

    with col1:
        description_input = st.text_area(label="Enter a disease description 👇", placeholder='A disease that causes memory loss and other cognitive impairments.')

    with col2:
        st.text('') # dummy to center vertically
        st.text('') # dummy to center vertically
        st.text('') # dummy to center vertically
        show_analyze_status = st.button("Analyze 🔎")


# analyze
with st.container():
    if show_analyze_status:
        with st.status("Analyzing...") as status:
            # 1. Embed the textual description that the user entered using the model
            # 2. Get 5 diseases with the highest cosine silimarity from the DB
            status.write("Analyzing the description that you wrote...")
            encoder = SentenceTransformer("allenai-specter")
            diseases_related_to_the_user_text = get_diseases_related_to_a_textual_description(
                description_input, encoder
            )
            status.info(f'Found {len(diseases_related_to_the_user_text)} diseases related to the description you entered.')
            status.json(diseases_related_to_the_user_text, expanded=False)
            # 3. Get the similarities of the embeddings of those diseases (cosine similarity of the embeddings of the nodes of such diseases)
            status.write("Getting the similarities among the diseases to filter out less promising ones...")
            diseases_uris = [disease["uri"] for disease in diseases_related_to_the_user_text]
            get_similarities_among_diseases_uris(diseases_uris)
            # 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
            status.write("Augmenting the set of diseases by finding others with related embeddings...")
            augmented_set_of_diseases = augment_the_set_of_diseaces(diseases_uris)
            # print(augmented_set_of_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
            status.write("Getting the clinical trials related to the diseases found...")
            clinical_trials_related_to_the_diseases = get_clinical_trials_related_to_diseases(
                augmented_set_of_diseases, encoder
            )
            status.write("Getting the details of the clinical trials...")
            json_of_clinical_trials = get_clinical_records_by_ids(
                [trial["nct_id"] for trial in clinical_trials_related_to_the_diseases]
            )
            status.json(json_of_clinical_trials, expanded=False)
            # 7. Use an LLM to get a summary of the clinical trials, in plain text format.
            status.write("Getting a summary of the clinical trials...")
            response = get_short_summary_out_of_json_files(json_of_clinical_trials)
            print(f'Response from LLM: {response}')
            status.write(f'Response from LLM: {response}')
            # 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.
            status.write("Getting summary statistics of the clinical trials...")
            # 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
            status.update(label="Done!", state="complete")
            status.balloons()
            show_graph = True


# graph
with st.container():
    if show_graph:
        st.info(
            """This is a graph of the relevant diseases that we found, based on the description that you entered. The diseases are connected by edges if they are similar to each other. The color of the edges represents the similarity of the diseases.
                
We use the embeddings of the diseases to determine the similarity between them. The embeddings are generated using a Representation Learning algorithm that learns the topological relations among the nodes in the graph, depending on how they are connected. We utilize the (PyKeen)[https://github.com/pykeen/pykeen] implementation of TransH to train an embedding model.

(TransH)[https://ojs.aaai.org/index.php/AAAI/article/view/8870] utilizes hyperplanes to model relations between entities. It is a multi-relational model that can handle many-to-many relations between entities. The model is trained on the triples of the graph, where the triples are the subject, relation, and object of the graph. The model learns the embeddings of the entities and the relations, such that the embeddings of the subject and object are close to each other when the relation is true.

Specifically, it optimizes the following cost function:
$$"""
        )
        # TODO actual graph
        graph_of_diseases = agraph(
            nodes=[
                Node(id="A", label="Node A", size=10),
                Node(id="B", label="Node B", size=10),
                Node(id="C", label="Node C", size=10),
                Node(id="D", label="Node D", size=10),
                Node(id="E", label="Node E", size=10),
                Node(id="F", label="Node F", size=10),
                Node(id="G", label="Node G", size=10),
                Node(id="H", label="Node H", size=10),
                Node(id="I", label="Node I", size=10),
                Node(id="J", label="Node J", size=10),
            ],
            edges=[
                Edge(source="A", target="B"),
                Edge(source="B", target="C"),
                Edge(source="C", target="D"),
                Edge(source="D", target="E"),
                Edge(source="E", target="F"),
                Edge(source="F", target="G"),
                Edge(source="G", target="H"),
                Edge(source="H", target="I"),
                Edge(source="I", target="J"),
            ],
            config=Config(height=500, width=500),
        )
        time.sleep(2)
        show_overview = True


# overview
with st.container():
    if show_overview:
        st.write("## Disease Overview")
        disease_overview = ":red[lorem ipsum]"  # TODO
        st.write(disease_overview)
        time.sleep(2)
        show_details = True


# details
with st.container():
    if show_details:
        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, 8):
            trials.append(d)

        tab_titles = [f"{trial['protocolSection']['identificationModule']['nctId']}" for trial in trials]

        tabs = st.tabs(tab_titles)

        for i in range(0, len(tabs)):
            with tabs[i]:
                render_trial_details(trials[i])