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import streamlit as st
import streamlit.components.v1 as components
from activities.activity import ml
from ml.mlmodel import MLDataset
import plotly.express as px
import pandas as pd
import pandas_profiling
import os
from sklearn.datasets import load_diabetes
from streamlit_pandas_profiling import st_profile_report
from streamlit_option_menu import option_menu
def main():
#Sidebar
from PIL import Image
st.sidebar.image('logo.png', use_column_width=True)
image_loan=Image.open(os.path.join("data analysis.jpg"))
rad = st.sidebar.radio("Navigation",["Home","Analysis","Visualize","Machine-Learning"])
# if rad=="Home":
# HtmlFile = open("style.css", 'r', encoding='utf-8')
# source_code = HtmlFile.read()
# components.html(source_code,width=900, height=700)
# # print(source_code)
with open('style.css') as f:
st.markdown(f'<style>{f.read()}</stle>',unsafe_allow_html=True)
if rad=='Home':
html_temp = """
<div id="container">
<h1>Welcome to Our Website</h1>
<p>Our advanced software will help you make sense<br>
of your data quickly and easily. With powerful <br>
algorithms and customizable dashboards, you'll<br>
be able to see patterns and insights that<br>
you never knew existed.</p>
</div>
"""
st.markdown(html_temp,unsafe_allow_html=True)
if rad == "Visualize":
file_upload=st.sidebar.file_uploader(" ",type=["csv"])
st.sidebar.image(image_loan,use_column_width=True)
chart_select = st.sidebar.selectbox(
label = "select the chart type",
options=['ScatterPlots','Lineplots','Histogram','Boxplot']
)
html_temp = """
<div style="background-color:red;padding:10px">
<h2 style="color:white;text-align:center;border-radius:30px">Automatic Exploratory Analysis</h2>
</div>
"""
st.markdown(html_temp,unsafe_allow_html=True)
st.sidebar.title("Upload Input csv file : ")
st.subheader('1.Datasets')
st.write("""
# Automatic Exploratory Analysis
""")
if file_upload is not None:
df = pd.read_csv(file_upload)
st.write(df)
numeric_columns = list(df.select_dtypes(['float','int']).columns)
if chart_select == "ScatterPlots":
st.sidebar.subheader("ScatterPlot Settings")
x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
plot = px.scatter(data_frame =df, x=x_values,y=y_values)
st.plotly_chart(plot)
if chart_select == "Lineplots":
st.sidebar.subheader("ScatterPlot Settings")
x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
plot = px.line(data_frame =df, x=x_values,y=y_values)
st.plotly_chart(plot)
if chart_select=="Histogram":
st.sidebar.subheader("ScatterPlot Settings")
x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
plot=px.histogram(data_frame=df,x=x_values,y=y_values)
st.plotly_chart(plot)
if chart_select=="Boxplot":
st.sidebar.subheader("ScatterPlot Settings")
x_values = st.sidebar.selectbox('X axis',options=numeric_columns)
y_values = st.sidebar.selectbox('Y axis',options=numeric_columns)
plot=px.box(data_frame=df,x=x_values,y=y_values)
st.plotly_chart(plot)
else:
st.info('Awaiting for CSV file to be uploaded.')
if rad == "Analysis":
file_upload=st.sidebar.file_uploader(" ",type=["csv"])
st.sidebar.image(image_loan,use_column_width=True)
html_temp = """
<div style="background-color:red;padding:10px">
<h2 style="color:white;text-align:center;">Automatic Exploratory Analysis </h2>
</div>
"""
st.markdown(html_temp,unsafe_allow_html=True)
st.sidebar.title("Upload Input csv file : ")
st.subheader('1.Datasets')
st.write("""
# Automated Exploratory Analysis
""")
if file_upload is not None:
df = pd.read_csv(file_upload)
st.write(df)
df1 = df.dropna()
if st.button('Run Modeling'):
st.title("Exploratory Data Analysis")
profile_df = df.profile_report()
st_profile_report(profile_df)
if st.button("No. of Missing values"):
st.write(df.isna().sum())
if st.button('Drop Missing values'):
df1 = df.dropna()
st.write(df1)
else:
st.info('Awaiting for CSV file to be uploaded.')
if st.button('Press to use Example Datasets'):
diabetes = load_diabetes()
X = pd.DataFrame(diabetes.data,columns=diabetes.feature_names)
Y = pd.Series(diabetes.target,name='response')
df = pd.concat([X,Y],axis=1)
st.markdown('The Diabetes dataset is used as the example.')
st.write(df.head())
st.markdown("Shape of Diabetes dataset")
st.write(df.shape)
st.title("Exploratory Data Analysis")
profile_df = df.profile_report()
st_profile_report(profile_df)
st.title("Sum of Null Values")
st.write(df.isna().sum())
st.title("Dropping Null Values")
df1 = df.dropna()
data = df1.to_csv("new.csv")
st.write(df1)
if rad == "Machine-Learning":
file_upload=st.sidebar.file_uploader(" ",type=["csv"])
dataset_name = st.selectbox("Pick a dataset", ["Iris", "Breast Cancer", "Wine Quality", "Mnist Digits", "Boston Houses", 'Diabetes'])
dataset = MLDataset(dataset_name)
df = dataset.get_dataframe()
ml(df)
if __name__ == '__main__':
main()
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