3-prediction.py

from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *

st.session_state["interface"] = st.session_state.get('interface')


st.header("Predictions making", divider='blue')
model_column, space, file_column= st.columns((2, 1, 1))

#M9, M10, M11 = st.columns([2,2,2])
NIRS_csv = file_column.file_uploader("Select NIRS Data to predict", type="csv", help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns")

export_folder = './data/predictions/'
export_name = 'Predictions_of_'

reg_algo = ["Interval-PLS"]

if NIRS_csv:
        export_name += str(NIRS_csv.name[:-4])
        qsep = file_column.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+NIRS_csv.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+NIRS_csv.name))), key=2)
        qhdr = file_column.selectbox("indexes column in csv? - _detected_: " + str(find_col_index('data/'+NIRS_csv.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+NIRS_csv.name))), key=3)
        if qhdr == 'yes':
            col = 0
        else:
            col = False
        pred_data = pd.read_csv(NIRS_csv, sep=qsep, index_col=col)

        # Load the model with joblib
        model_column.write("Load your saved predictive model")
        
        model_name_import = model_column.selectbox('Choose file:', options=os.listdir('data/models/'), key = 21)
        if model_name_import != ' ':
            export_name += '_with_' + str(model_name_import[:-4])
            with open('data/models/'+ model_name_import,'rb') as f:
                model_loaded = joblib.load(f)
            if model_loaded:
                s = model_column.checkbox('the model is of ipls type?')
                model_column.success("The model has been loaded successfully", icon="✅")
                if s:
                      index = model_column.file_uploader("select wavelengths index file", type="csv")
                      if index:
                        idx = pd.read_csv(index, sep=';', index_col=0).iloc[:,0].to_numpy()

#result = ''

if st.button("Predict"):
        if s:
             result = model_loaded.predict(pred_data.iloc[:,idx])
        else:
        # use prediction function from application_functions.py to predict chemical values
            result = model_loaded.predict(pred_data)
        st.write('Predicted values are: ')
        st.dataframe(result.T)
        pd.DataFrame(result).to_csv(export_folder + export_name + '.csv', sep = ';')
        # export to local drive - Download
        download_results(export_folder + export_name + '.csv', export_name + '.csv')
        # create a report with information on the prediction
        ## see https://stackoverflow.com/a/59578663