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import streamlit as st
import time
import json
from gensim.models import Word2Vec
import pandas as pd
# Define the HTML and CSS styles
html_temp = """
<div style="background-color:black;padding:10px">
<h1 style="color:white;text-align:center;">My Streamlit App with HTML and CSS</h1>
</div>
"""
# Display the HTML and CSS styles
st.markdown(html_temp, unsafe_allow_html=True)
# Add some text to the app
st.write("This is my Streamlit app with HTML and CSS formatting.")
query = st.text_input("Enter a word")
# 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)
csv = table.head(50).to_csv(index=False).encode('utf-8')
st.download_button(
label=f"Download words similar to {query} in .csv format",
data=csv,
file_name='clotting_sim1.csv',
mime='text/csv'
)
json = table.head(50).to_json(index=True).encode('utf-8')
st.download_button(
label=f"Download words similar to {query} in .js format",
data=json,
file_name='clotting_sim1.js',
mime='json'
)
print(table.head(10))
table.head(50).to_csv("clotting_sim1.csv", index=True)
table.head(50).to_json("clotting_sim1.js", index=True)
st.header(f"Similar Words to {query}")
st.write(table.head(50))
#
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)
csv2 = df1.head(50).to_csv(index=False).encode('utf-8')
st.download_button(
label=f"Download genes similar to {query} in .csv format",
data=csv2,
file_name='clotting_sim2.csv',
mime='text/csv'
)
json2 = df1.head(50).to_json(index=True).encode('utf-8')
st.download_button(
label=f"Download words similar to {query} in .js format",
data=json2,
file_name='clotting_sim1.js',
mime='json'
)
print(df1.head(10))
df1.head(50).to_csv("clotting_sim2.csv", index=True)
df1.head(50).to_json("clotting_sim2.js", index=True)
print()
st.header(f"Similar Genes to {query}")
st.write(df1.head(50))
from datasets import load_dataset
test_dataset = load_dataset("json", data_files="clotting_sim1.js", split="train")
test_dataset.save_to_disk("sim1.hf") |