Delete app.py

app.py

@@ -1,59 +0,0 @@
-import streamlit as st
-import time
-import json
-from gensim.models import Word2Vec
-import pandas as pd
-
-
-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(df1.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)
-