app.py

import streamlit as st
import time
import json
from gensim.models import Word2Vec
import pandas as pd
import matplotlib.pyplot as plt
import squarify
import numpy as np
import re
import urllib.request
import random
import plotly.express as px

st.set_page_config(
    page_title="FATA4 Science",
                page_icon=":microscope:",
                layout="wide", #centered
                initial_sidebar_state="auto",
                menu_items={
                    'About': "FATA4 Science is a Natural Language Processing (NLP) that ...."
                }
                )

# Define the HTML and CSS styles
st.markdown("""
<style>
    [data-testid=stSidebar] {
        background-color: #99CCFF;
    }
</style>
""", unsafe_allow_html=True)
st.markdown("""
    <style>
    body {
        background-color: #CCFFFF;
        # color: #ffffff;
    }
    .stApp {
        background-color: #CCFFFF;
        # color: #ffffff;
    }
    </style>
    """, unsafe_allow_html=True)

opt=st.sidebar.radio("Select a PubMed Corpus", options=('Clotting corpus', 'Neuroblastoma corpus'))
if opt == "Clotting corpus":
    model_used = ("pubmed_model_clotting")
    num_abstracts = 45493
    database_name = "Clotting"
if opt == "Neuroblastoma corpus":
    model_used = ("pubmed_model_neuroblastoma")
    num_abstracts = 29032
    database_name = "Neuroblastoma"

st.header(":red[*F*]ast :red[*A*]cting :red[*T*]ext :red[*A*]nalysis (:red[*FATA*]) 4 Science")

st.subheader("Uncovering knowledge through Natural Language Processing (NLP)")
st.markdown("---")

st.header(f":blue[{database_name} Pubmed corpus.]")
text_input_value = st.text_input(f"Enter one term to search within the {database_name} corpus")
query = text_input_value
query = query.lower()
query = re.sub("[,.?!&*;:]", "", query)
matches = [" "]
if any([x in query for x in matches]):
    st.write("Please only enter one term or a term without spaces")
# query = input ("Enter your keyword(s):")
if query:
    bar = st.progress(0)
    time.sleep(.05)
    st.caption(f"Searching {num_abstracts} {database_name} PubMed abstracts covering 1990-2022")

    for i in range(10):
        bar.progress((i + 1) * 10)
        time.sleep(.1)

    try:
        model = Word2Vec.load(model_used)  # you can continue training with the loaded model!
        words = list(model.wv.key_to_index)
        X = model.wv[model.wv.key_to_index]
        model2 = model.wv[query]
        df = pd.DataFrame(X)

    except:
        st.error("Term occurrence is too low - please try another term")
        st.stop()
    st.markdown("---")
    # def findRelationships(query, df):
    table = model.wv.most_similar_cosmul(query, topn=10000)
    table = (pd.DataFrame(table))
    table.index.name = 'Rank'
    table.columns = ['Word', 'SIMILARITY']
    print()
    print("Similarity to " + str(query))
    pd.set_option('display.max_rows', None)
    print(table.head(50))
    # table.head(10).to_csv("clotting_sim1.csv", index=True)
    # short_table = table.head(50)
    # print(table)


    # calculate the sizes of the squares in the treemap
    short_table = table.head(10)
    short_table.index += 1
    short_table.index = 1 / short_table.index
    sizes = short_table.index.tolist()

    cmap = plt.cm.Greens(np.linspace(0.05, .5, len(sizes)))
    color = [cmap[i] for i in range(len(sizes))]

    short_table.set_index('Word', inplace=True)
    squarify.plot(sizes=sizes, label=short_table.index.tolist(), color=color, edgecolor="#EBF5FB",
                  text_kwargs={'fontsize': 10},)
    # # plot the treemap using matplotlib
    plt.axis('off')
    # Add legend to top right, outside plot region
    # plt.legend("upper right", bbox_to_anchor=(-.2, 0))
    fig = plt.gcf()
    fig.patch.set_facecolor('#CCFFFF')
    # # display the treemap in Streamlit

    rank_num = list(short_table.index.tolist())
    avg_size = sum(sizes) / len(short_table.index)
    print(rank_num)
    # print(sizes)
    fig = px.treemap(short_table, path=[short_table.index], values=sizes, color=sizes, color_continuous_scale='greens',
                     color_continuous_midpoint=avg_size)
    fig.update(layout_coloraxis_showscale=False)
    fig.update_layout(autosize=True, paper_bgcolor="#CCFFFF")


    treemap1, treemap2 = st.columns(2)
    with treemap1:
        st.subheader(f"Top 10 Words closely related to {query}")
        # st.pyplot(fig)
        # plt.clf()
        st.plotly_chart(fig, use_container_width=True)

        csv = table.head(100).to_csv().encode('utf-8')
        st.download_button(label="download top 100 words (csv)", data=csv, file_name=f'{database_name}_words.csv', mime='text/csv')

    # st.write(short_table)
    #

    print()
    print("Human genes similar to " + str(query))
    df1 = table
    df2 = pd.read_csv('Human_Genes.csv')
    m = df1.Word.isin(df2.symbol)
    df1 = df1[m]
    df1.rename(columns={'Word': 'Human Gene'}, inplace=True)
    df1["Human Gene"] = df1["Human Gene"].str.upper()
    print(df1.head(50))
    print()
    # df1.head(50).to_csv("clotting_sim2.csv", index=True, header=False)
    # time.sleep(2)


    df10 = df1.head(10)
    df10.index = 1 / df10.index
    sizes = df10.index.tolist()

    cmap2 = plt.cm.Blues(np.linspace(0.05, .5, len(sizes)))
    color2 = [cmap2[i] for i in range(len(sizes))]

    df10.set_index('Human Gene', inplace=True)
    squarify.plot(sizes=sizes, label=df10.index.tolist(), color=color2, edgecolor="#EBF5FB",
                  text_kwargs={'fontsize': 12})
    #
    # # plot the treemap using matplotlib

    plt.axis('off')
    fig2 = plt.gcf()
    fig2.patch.set_facecolor('#CCFFFF')
    #



    # link_ref = '<a href="http://google.com" style="cursor: pointer" target="_blank" rel="noopener noreferrer">{}</a>'
    # df10['SIMILARITY'] = df10['SIMILARITY'].apply(lambda item: link_ref.format(item, "{}"))
    rank_num = list(df10.index.tolist())
    avg_size = sum(sizes) / len(df10.index)
    print(rank_num)
    # print(sizes)
    fig = px.treemap(path=[df10.index], values=sizes, color=sizes, color_continuous_scale='greens',
                     color_continuous_midpoint=avg_size)
    fig.update(layout_coloraxis_showscale=False)
    fig.update_layout(autosize=True, paper_bgcolor="#CCFFFF", uniformtext_mode="hide", plot_bgcolor="#fff")
    fig.update_traces(root_color='rgba(0,0,0,0)')


    # # display the treemap in Streamlit
    with treemap2:
        st.subheader(f"Top 10 Genes closely related to {query}")
        # st.pyplot(fig2)
        st.plotly_chart(fig, use_container_width=True)

        csv = df1.head(100).to_csv().encode('utf-8')
        st.download_button(label="download top 100 genes (csv)", data=csv, file_name=f'{database_name}_genes.csv',
                       mime='text/csv')
    st.markdown("---")
    st.subheader("Cancer-related videos")
    if query:
        idlist=[]
        search_keyword = {query}
        html = urllib.request.urlopen("https://www.youtube.com/@NCIgov/search?query=cancer")
        html2 = urllib.request.urlopen("https://www.youtube.com/@CancerCenter/search?query=cancer")
        html3 = urllib.request.urlopen("https://www.youtube.com/@NorthwesternMedicine/search?query=cancer")
        html4 = urllib.request.urlopen("https://www.youtube.com/@TEDEd/search?query=cancer")
        html5 = urllib.request.urlopen("https://www.youtube.com/@CancerResearchUK/search?query=cancer")
        video_ids = re.findall(r"watch\?v=(\S{11})", html.read().decode())
        video_ids2 = re.findall(r"watch\?v=(\S{11})", html2.read().decode())
        video_ids3 = re.findall(r"watch\?v=(\S{11})", html3.read().decode())
        video_ids4 = re.findall(r"watch\?v=(\S{11})", html4.read().decode())
        video_ids5 = re.findall(r"watch\?v=(\S{11})", html5.read().decode())

        for i in video_ids2:
            video_ids.append(i)
        for i in video_ids3:
            video_ids.append(i)
        for i in video_ids4:
            video_ids.append(i)
        for i in video_ids5:
            video_ids.append(i)

        random.shuffle(video_ids)

        c1, c2, c3 = st.columns(3)


        with c1:
           st.video("https://www.youtube.com/watch?v=" + video_ids[0])
        with c2:
           st.video("https://www.youtube.com/watch?v=" + video_ids[1])
        with c3:
           st.video("https://www.youtube.com/watch?v=" + video_ids[2])
    st.markdown("---")

    # fig = plt.figure()