cleanup

app.py

@@ -1,17 +1,7 @@
 import streamlit as st
-import pandas as pd
-from run import run, GUIDE_LEN, NUCLEOTIDE_TOKENS
+from run import tiger_predict, GUIDE_LEN, NUCLEOTIDE_TOKENS
 
 
-# def run_with_input(reset=False):
-#     if reset:
-#         st.write("")
-#         return 0
-#     returned_x = run(st.session_state["userInput"])
-#     csv_x = returned_x.to_csv()
-#     st.write("model prediction: ", returned_x)
-#     return csv_x
-
 @st.cache
 def convert_df(df):
     # IMPORTANT: Cache the conversion to prevent computation on every rerun
@@ -29,7 +19,7 @@ if len(st.session_state['userInput']) < GUIDE_LEN:
     st.write("")
 elif all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in st.session_state['userInput']]):
     st.write('This is your sequence', st.session_state['userInput'])
-    predictions = run(st.session_state['userInput'])
+    predictions = tiger_predict(st.session_state['userInput'])
     st.write('Model predictions: ', predictions)
     csv = convert_df(predictions)
     st.download_button(label='Download CSV file', data=csv, file_name='tiger_predictions.csv', mime='text/csv')

run.py

@@ -1,5 +1,4 @@
 import os
-import sys
 import tensorflow as tf
 import pandas as pd
 
@@ -37,22 +36,10 @@ def gen_report_table(input_gens, res):
     return tbl
 
 
-def run(x):
+def tiger_predict(x):
     input_gens, model_input_x = process_data(x)
     # print("input gene: ", input_gens)
     # print("model_input: ", model_input_x)
     res = tiger.predict_step(model_input_x)
     # print("res: ", res)
     return gen_report_table(input_gens, res)
-
-
-# if __name__ == "__main__":
-#     if len(sys.argv) == 1:
-#         print("you need to specify 23 character gen information")
-#         exit()
-#     x = sys.argv[1]
-#     if len(x) != 23:
-#         print("you need to specify 23 character gen information. You typed %s chars" % len(x))
-#         exit()
-#     elif all([True if item in "ACGT" else False for item in x]):
-#         print("run succesfully: ", run(x))