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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: #000000;
color: #ffffff;
}
.stApp {
background-color: #000000;
color: #ffffff;
}
</style>
""",
unsafe_allow_html=True
)
st.markdown(
"""
<style>
.stTextInput div label {
color: #ffffff !important;
}
.stTextInput div input[type="text"] {
color: #ffffff !important;
}
</style>
""",
unsafe_allow_html=True
)
st.header(":white[My Streamlit App with HTML and CSS]")
# Add some text to the app
st.write(":white[**This is my Streamlit app with HTML and CSS formatting.**]")
text_input_value = st.text_input("Enter some text", "")
query = text_input_value
query = query.lower()
# query = input ("Enter your keyword(s):")
if query:
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)
# 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.header(f":white[Similar Words to {query}]")
# calculate the sizes of the squares in the treemap
short_table = table.head(20)
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, pad=.005, text_kwargs={'fontsize': 6})
# # plot the treemap using matplotlib
plt.axis('off')
fig = plt.gcf()
# # display the treemap in Streamlit
st.pyplot(fig)
plt.clf()
# 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.header(f":white[Similar Genes to {query}]")
df1 = df1.head(20)
df1.index = 1/df1.index
sizes = df1.index.tolist()
cmap2 = plt.cm.Blues(np.linspace(0.05, .5, len(sizes)))
color2 = [cmap2[i] for i in range(len(sizes))]
df1.set_index('Human Gene', inplace=True)
squarify.plot(sizes=sizes, label=df1.index.tolist(), color=color2, pad=.005, text_kwargs={'fontsize': 8})
#
# # plot the treemap using matplotlib
plt.axis('off')
fig2 = plt.gcf()
# plt.show()
#
# # display the treemap in Streamlit
st.pyplot(fig2)
# 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)
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