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Running
on
CPU Upgrade
| import streamlit as st | |
| import run as trun | |
| import pandas as pd | |
| def run_with_input(reset=False): | |
| if reset: | |
| st.write("") | |
| return 0 | |
| returned_x = trun.run(st.session_state["userInput"]) | |
| csv_x = returned_x.to_csv() | |
| st.write("model prediction: ", returned_x) | |
| return csv_x | |
| # title and instructions | |
| st.title('TIGER Cas13 Efficacy Prediction') | |
| st.session_state['userInput'] = "" | |
| st.session_state["userInput"] = st.text_input('Enter target transcript (or substring):') | |
| csv_data = pd.DataFrame.from_dict({'Target Site': [''], 'LFC': [0.0]}).to_csv() | |
| if len(st.session_state['userInput']) < trun.GUIDE_LEN: | |
| st.write('Transcript length must be >= 23 bases. It is {:d} chars'.format(len(st.session_state['userInput']))) | |
| run_with_input(reset=True) | |
| elif all([True if item in "ACGTacgt" else False for item in st.session_state['userInput']]): | |
| st.write('This is your sequence', st.session_state["userInput"]) | |
| csv_data = run_with_input() | |
| else: | |
| st.write("only ACTG is allowed") | |
| st.download_button(label="Download as CVS File", data=csv_data) | |