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

# Define the HTML and CSS styles
st.markdown("""
    <style>
    body {
        background-color: #EBF5FB;
        # color: #ffffff;
    }
    .stApp {
        background-color: #EBF5FB;
        # color: #ffffff;
    }
    </style>
    """, unsafe_allow_html=True)

st.header("Word2Vec App for Clotting Pubmed Database.")

text_input_value = st.text_input("Enter one term to search within the Clotting database", max_chars=50)
query = text_input_value
query = query.lower()
# query = input ("Enter your keyword(s):")
if query:

    if query.isalpha():
        bar = st.progress(0)
        time.sleep(.2)
        st.caption(":LightSkyBlue[searching 40123 PubMed abstracts]")
        for i in range(10):
            bar.progress((i + 1) * 10)
            time.sleep(.1)
    else:
        st.write('Please omit numbers in term')

    try:
        model = Word2Vec.load("pubmed_model_clotting")  # 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()

    # 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)
    st.subheader(f"Top 10 Words closely related to {query}")

    # 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')
    fig = plt.gcf()
    fig.patch.set_facecolor('#EBF5FB')
    # # display the treemap in Streamlit
    st.pyplot(fig)
    plt.clf()

    csv = table.head(100).to_csv().encode('utf-8')
    st.download_button(label="download top 100 words (csv)", data=csv, file_name='clotting_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)
    st.subheader(f"Top 10 Genes closely related to {query}")

    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('#EBF5FB')
    # plt.show()
    #
    # # display the treemap in Streamlit
    st.pyplot(fig2)

    csv = df1.head(100).to_csv().encode('utf-8')
    st.download_button(label="download top 100 genes (csv)", data=csv, file_name='clotting_genes.csv', mime='text/csv')

# findRelationships(query, df)


# model = gensim.models.KeyedVectors.load_word2vec_format('pubmed_model_clotting', binary=True)
# similar_words = model.most_similar(word)
# output = json.dumps({"word": word, "similar_words": similar_words})
# st.write(output)