app.py

import streamlit as st

# Custom imports
from multipage import MultiPage
from pages import data_upload, machine_learning, metadata, data_visualize, redundant, inference # import your pages here

import collections
from numpy.core.defchararray import lower
import streamlit as st
import numpy as np
import pandas as pd
from pages import utils


def app():
    st.markdown("## Data Upload")

    # Upload the dataset and save as csv
    st.markdown("### Upload a csv file for analysis.")
    st.write("\n")

    # Code to read a single file
    uploaded_file = st.file_uploader("Choose a file", type=['csv', 'xlsx'])
    global data
    if uploaded_file is not None:
        try:
            data = pd.read_csv(uploaded_file)
        except Exception as e:
            print(e)
            data = pd.read_excel(uploaded_file)

    ''' Load the data and save the columns with categories as a dataframe. 
    This section also allows changes in the numerical and categorical columns. '''
    if st.button("Load Data"):

        # Raw data
        st.dataframe(data)
        data.to_csv('data/main_data.csv', index=False)

        # Collect the categorical and numerical columns

        numeric_cols = data.select_dtypes(include=np.number).columns.tolist()
        categorical_cols = list(set(list(data.columns)) - set(numeric_cols))

        # Save the columns as a dataframe or dictionary
        columns = []

        # Iterate through the numerical and categorical columns and save in columns
        columns = utils.genMetaData(data)

        # Save the columns as a dataframe with categories
        # Here column_name is the name of the field and the type is whether it's numerical or categorical
        columns_df = pd.DataFrame(columns, columns=['column_name', 'type'])
        columns_df.to_csv('data/metadata/column_type_desc.csv', index=False)

        # Display columns
        st.markdown("**Column Name**-**Type**")
        for i in range(columns_df.shape[0]):
            st.write(f"{i + 1}. **{columns_df.iloc[i]['column_name']}** - {columns_df.iloc[i]['type']}")

        st.markdown("""The above are the automated column types detected by the application in the data. 
        In case you wish to change the column types, head over to the **Column Change** section. """)


# Create an instance of the app
app = MultiPage()

# Title of the main page
st.title("Data Storyteller Application")

# Add all your applications (pages) here
app.add_page("Upload Data", data_upload.app)
app.add_page("Change Metadata", metadata.app)
app.add_page("Machine Learning", machine_learning.app)
app.add_page("Data Analysis",data_visualize.app)
app.add_page("Y-Parameter Optimization",redundant.app)

# The main app
app.run()