app.py

import streamlit as st
import pandas as pd
import joblib
import time
# importing modules

import frontend.instructions as fi
fi.displayInstructionSection()
import frontend.selectingSeqType as sst
seqType = sst.selecType()
import frontend.gettingInput as gi
sequence = gi.gettingInput()
from backend.labelingInput import returnValues
from backend import labelingInput
from backend import gettingFromModel
#giveing data

selectingView = st.selectbox("Select View",["Table View","Simple View"])
modelPath = "backend/xgboost_model.joblib"
LabelETargetedPath = "backend/LabelETargeted.joblib"
LabelESequencePath = "backend/LabelESequence.joblib"
# button
predict = st.button('Predict')
if predict:
    if sequence == "":
        st.error('No sequence: Please Provide Sequence First')
    else:
        with st.spinner("getting values..."):
            labelingInput.giveValues(sequence)
            data = returnValues(len(sequence), sequence)
            print(data.columns)
            st.success("Generated...")
            for key,values in data.items():
                    st.code(f"{key}: {values[0]}")
            with st.spinner("Loading model..."):
                model = joblib.load(modelPath)
                LabelT = joblib.load(LabelETargetedPath)
                LabelS = joblib.load(LabelESequencePath)
                response = gettingFromModel.getResponse(data,model,LabelT,LabelS)
                st.info(f'Predicted, it is "{response[0].upper()}"')
                time.sleep(1)
                st.bar_chart(data)