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f182c96 482d9db f182c96 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from operator import index
import streamlit as st
#import plotly.express as px
from pycaret.classification import setup, compare_models, pull, save_model, load_model
from ydata_profiling import ProfileReport
import pandas as pd
from streamlit_pandas_profiling import st_profile_report
import os
with st.sidebar:
st.image('side.jpeg')
st.title('AutoStreamML')
choice = st.radio("Navigation", ["Upload", "Profiling", "Modelling", "Download"])
st.info("This application allows you to build an automted ML pipeline using streamlit, pandas profiling and PyCaret.")
if os.path.exists("sourcedata.csv") and choice == "Upload":
st.session_state.df = pd.read_csv("sourcedata.csv", index_col=None)
if choice == "Upload":
st.title("Upload your data for modelling")
file = st.file_uploader("Upload your dataset")
if file:
st.session_state.df = pd.read_csv(file, index_col=None)
st.dataframe(st.session_state.df)
if st.button("Save"):
st.session_state.df.to_csv("sourcedata.csv", index=None)
if choice == "Profiling":
st.title("Automated Exploratory Data Analysis")
selected_columns = st.multiselect("Select columns to drop", st.session_state.df.columns)
st.dataframe(st.session_state.df)
# Add a button to drop selected columns
if st.button("Drop Columns"):
if selected_columns:
st.session_state.df.drop(columns=selected_columns, inplace=True)
st.dataframe(st.session_state.df)
if st.button("Save"):
st.session_state.df.to_csv("sourcedata.csv", index=None)
if st.button("Perform EDA"):
# Add code to remove timestamps and then do the profile report
profile_df = ProfileReport(st.session_state.df, title="EDA")
st_profile_report(profile_df)
if choice == "Modelling":
columns_without_missing_values = [col for col in st.session_state.df.columns if st.session_state.df[col].count() == len(st.session_state.df)]
target = st.selectbox("Select Your Target", columns_without_missing_values)
if st.button('Run Modelling'):
# Mention to user if the missing value is present instead of throwing error
setup(st.session_state.df, target=target)
setup_df = pull()
st.info("This is the ML Experiment Settings")
st.dataframe(setup_df)
best_model = compare_models()
compare_df = pull()
st.dataframe(compare_df)
save_model(best_model, 'best_model')
if choice == "Download":
with open("best_model.pkl", "rb") as f:
st.download_button("Download the file", f, "best_model.pkl") |