Upload word2vec_app.py

word2vec_app.py

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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)
+    print(table.head(100))
+    table.head(10).to_csv("clotting_sim1.csv", 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)
+    print(df1.head(10))
+    print()
+    df1.head(10).to_csv("clotting_sim2.csv", index=True, header=False)
+    time.sleep(2)
+    st.header(f"Similar Genes to {query}")
+    st.write(table.head(50))
+
+
+# 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)
+