import streamlit as st
import plotly.express as px
from pycaret.regression import setup, compare_models, pull, save_model, load_model
import pandas_profiling
import pandas as pd
from streamlit_pandas_profiling import st_profile_report
import os
if os.path.exists('./dataset.csv'):
df = pd.read_csv('dataset.csv', index_col=None)
else:
df = pd.DataFrame() # default dataframe if one has not been provided
with st.sidebar:
st.image("https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png")
st.title("AutoNickML")
choice = st.radio("Navigation", ["Upload","Profiling","Modelling", "Download"])
st.info("This project application helps you build and explore your data.")
if choice == "Upload":
st.title("Upload Your Dataset")
file = st.file_uploader("Upload Your Dataset")
if file:
df = pd.read_csv(file, index_col=None)
df.to_csv('dataset.csv', index=None)
st.dataframe(df)
if choice == "Profiling":
st.title("Exploratory Data Analysis")
profile_df = df.profile_report()
st_profile_report(profile_df)
if choice == "Modelling":
chosen_target = st.selectbox('Choose the Target Column', df.columns)
if chosen_target and st.button('Run Modelling'):
setup(df, target=chosen_target, silent=True)
compare_df = pull()
st.dataframe(compare_df)
best_model = compare_models()
save_model(best_model, 'best_model')
if choice == "Download":
if os.path.exists('best_model.pkl'):
with open('best_model.pkl', 'rb') as f:
st.download_button('Download Model', f, file_name="best_model.pkl")
else:
st.warning("No model has been saved yet. Please run modelling first.")
import streamlit as st
import plotly.express as px
from pycaret.regression import setup, compare_models, pull, save_model, load_model
import pandas_profiling
import pandas as pd
from streamlit_pandas_profiling import st_profile_report
import os
if os.path.exists('./dataset.csv'):
df = pd.read_csv('dataset.csv', index_col=None)
else:
df = pd.DataFrame() # default dataframe if one has not been provided
with st.sidebar:
st.image("https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png")
st.title("AutoNickML")
choice = st.radio("Navigation", ["Upload","Profiling","Modelling", "Download"])
st.info("This project application helps you build and explore your data.")
if choice == "Upload":
st.title("Upload Your Dataset")
file = st.file_uploader("Upload Your Dataset")
if file:
df = pd.read_csv(file, index_col=None)
df.to_csv('dataset.csv', index=None)
st.dataframe(df)
if choice == "Profiling":
st.title("Exploratory Data Analysis")
profile_df = df.profile_report()
st_profile_report(profile_df)
if choice == "Modelling":
chosen_target = st.selectbox('Choose the Target Column', df.columns)
if chosen_target and st.button('Run Modelling'):
setup(df, target=chosen_target, silent=True)
compare_df = pull()
st.dataframe(compare_df)
best_model = compare_models()
save_model(best_model, 'best_model')
if choice == "Download":
if os.path.exists('best_model.pkl'):
with open('best_model.pkl', 'rb') as f:
st.download_button('Download Model', f, file_name="best_model.pkl")
else:
st.warning("No model has been saved yet. Please run modelling first.")