pages/1_Data_Validation.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from Eda_functions import *
import numpy as np
import re
import pickle
from streamlit_pandas_profiling import st_profile_report
import streamlit as st
import streamlit.components.v1 as components
import sweetviz as sv
from utilities import set_header,initialize_data,load_local_css
from st_aggrid import GridOptionsBuilder,GridUpdateMode
from st_aggrid import GridOptionsBuilder
from st_aggrid import AgGrid
import base64

st.set_page_config(
  page_title="Data Validation",
  page_icon=":shark:",
  layout="wide",
  initial_sidebar_state='collapsed'
)
load_local_css('styles.css')
set_header()



#preprocessing
# with open('Categorised_data.pkl', 'rb') as file:
#   Categorised_data = pickle.load(file)
# with open("edited_dataframe.pkl", 'rb') as file:


# df = pickle.load(file)
# date=df.index
# df.reset_index(inplace=True)
# df['Date'] = pd.to_datetime(date)


#prospects=pd.read_excel('EDA_Data.xlsx',sheet_name='Prospects')
#spends=pd.read_excel('EDA_Data.xlsx',sheet_name='SPEND INPUT')
#spends.columns=['Week','Streaming (Spends)','TV (Spends)','Search (Spends)','Digital (Spends)']
#df=pd.concat([df,spends],axis=1)

#df['Date'] =pd.to_datetime(df['Date']).dt.strftime('%m/%d/%Y')
#df['Prospects']=prospects['Prospects']
#df.drop(['Week'],axis=1,inplace=True)


st.title('Data Validation and Insights')

with open("Pickle_files/main_df",'rb') as f:
   st.session_state['cleaned_data']= pickle.load(f)
with open("Pickle_files/category_dict",'rb') as c:
   st.session_state['category_dict']=pickle.load(c)

# st.write(st.session_state['cleaned_data'])
    
target_variables=[st.session_state['category_dict'][key] for key in st.session_state['category_dict'].keys() if key =='Response_Metric']


target_column = st.selectbox('Select the Target Feature/Dependent Variable (will be used in all charts as reference)',list(*target_variables))
st.session_state['target_column']=target_column


fig=line_plot_target(st.session_state['cleaned_data'], target=target_column, title=f'{target_column} Over Time')
st.plotly_chart(fig, use_container_width=True)


media_channel=list(*[st.session_state['category_dict'][key] for key in st.session_state['category_dict'].keys() if key =='Media'])
# st.write(media_channel)

Non_media_channel=[col for col in st.session_state['cleaned_data'].columns if col not in media_channel]


st.markdown('### Annual Data Summary')
st.dataframe(summary(st.session_state['cleaned_data'], media_channel+[target_column], spends=None,Target=True), use_container_width=True)

if st.checkbox('Show raw data'):
    st.write(pd.concat([pd.to_datetime(st.session_state['cleaned_data']['Date']).dt.strftime('%m/%d/%Y'),st.session_state['cleaned_data'].select_dtypes(np.number).applymap(format_numbers)],axis=1))
col1 = st.columns(1)

if "selected_feature" not in st.session_state:
    st.session_state['selected_feature']=None

st.header('1. Media Channels')

if 'Validation' not in st.session_state:
    st.session_state['Validation']=[]

eda_columns=st.columns(2)
with eda_columns[0]:
    if st.button('Generate Profile Report'):
        pr = st.session_state['cleaned_data'].profile_report()

        pr.to_file("Profile_Report.html")

        with open("Profile_Report.html", "rb") as f:
            profile_report_html = f.read()
        b64 = base64.b64encode(profile_report_html).decode()
        href = f'<a href="data:text/html;base64,{b64}" download="Profile_Report.html">Download Profile Report</a>'
        st.markdown(href, unsafe_allow_html=True)

with eda_columns[1]:
    if st.button('Generate Sweetviz Report'):
    
        def generate_report_with_target(df, target_feature):
            report = sv.analyze([df, "Dataset"], target_feat=target_feature)
            return report

        report = generate_report_with_target(st.session_state['cleaned_data'], target_feature=target_column)
        report.show_html()


selected_media = st.selectbox('Select media', np.unique([Categorised_data[col]['VB'] for col in media_channel]))
# selected_feature=st.multiselect('Select Metric', df.columns[df.columns.str.contains(selected_media,case=False)])
st.session_state["selected_feature"]=st.selectbox('Select Metric',[col for col in  media_channel  if    Categorised_data[col]['VB'] in selected_media ] )
spends_features=[col for col in df.columns if 'spends' in col.lower() or 'cost' in col.lower()]
spends_feature=[col for col in spends_features if col.split('_')[0] in st.session_state["selected_feature"].split('_')[0]]
#st.write(spends_features)
#st.write(spends_feature)
#st.write(selected_feature)


val_variables=[col for col in media_channel if col!='Date']
if len(spends_feature)==0:  
    st.warning('No spends varaible available for the selected metric in data') 
       
else:
    st.write(f'Selected spends variable {spends_feature[0]} if wrong please name the varaibles properly')
    # Create the dual-axis line plot
    fig_row1 = line_plot(df, x_col='Date', y1_cols=[st.session_state["selected_feature"]], y2_cols=[target_column], title=f'Analysis of {st.session_state["selected_feature"]} and {[target_column][0]} Over Time')
    st.plotly_chart(fig_row1, use_container_width=True)
    st.markdown('### Annual Data Summary')
    st.dataframe(summary(df,[st.session_state["selected_feature"]],spends=spends_feature[0]),use_container_width=True)
    if st.button('Validate'):
        st.session_state['Validation'].append(st.session_state["selected_feature"])

    if st.checkbox('Validate all'):
        st.session_state['Validation'].extend(val_variables)
        st.success('All media variables are validated ✅')
    if len(set(st.session_state['Validation']).intersection(val_variables))!=len(val_variables):
        #st.write(st.session_state['Validation'])
        validation_data=pd.DataFrame({'Variables':val_variables,
                                    'Validated':[1 if col in st.session_state['Validation'] else 0 for col in val_variables],
                                    'Bucket':[Categorised_data[col]['VB'] for col in val_variables]})
        gd=GridOptionsBuilder.from_dataframe(validation_data)
        gd.configure_pagination(enabled=True)
        gd.configure_selection(use_checkbox=True,selection_mode='multiple')
        #gd.configure_selection_toggle_all(None, show_toggle_all=True)
        #gd.configure_columns_auto_size_mode(GridOptionsBuilder.configure_columns)
        gridoptions=gd.build()
        #st.text(st.session_state['Validation'])
        table = AgGrid(validation_data,gridOptions=gridoptions,update_mode=GridUpdateMode.SELECTION_CHANGED,fit_columns_on_grid_load=True)
        #st.table(table)
        selected_rows = table["selected_rows"]
        st.session_state['Validation'].extend([col['Variables'] for col in selected_rows])
        not_validated_variables = [col for col in val_variables if col not in st.session_state["Validation"]]
        if not_validated_variables:
            not_validated_message = f'The following variables are not validated:\n{" , ".join(not_validated_variables)}'
            st.warning(not_validated_message)



st.header('2. Non Media Variables')
selected_columns_row = [col for col in df.columns if ("imp" not in col.lower()) and ('cli' not in col.lower() ) and ('spend' not in col.lower()) and col!='Date']
selected_columns_row4 = st.selectbox('Select Channel',selected_columns_row )
if not selected_columns_row4: 
    st.warning('Please select at least one.')
else:
    # Create the dual-axis line plot
    fig_row4 = line_plot(df, x_col='Date', y1_cols=[selected_columns_row4], y2_cols=[target_column], title=f'Analysis of {selected_columns_row4} and {target_column} Over Time')
    st.plotly_chart(fig_row4, use_container_width=True)
    selected_non_media=selected_columns_row4
    sum_df = df[['Date', selected_non_media,target_column]]
    sum_df['Year']=pd.to_datetime(df['Date']).dt.year
    #st.dataframe(df)
    #st.dataframe(sum_df.head(2))
    sum_df=sum_df.groupby('Year').agg('sum')
    sum_df.loc['Grand Total']=sum_df.sum()         
    sum_df=sum_df.applymap(format_numbers) 
    sum_df.fillna('-',inplace=True)
    sum_df=sum_df.replace({"0.0":'-','nan':'-'})
    st.markdown('### Annual Data Summary')    
    st.dataframe(sum_df,use_container_width=True)

    # if st.checkbox('Validate',key='2'):
    #     st.session_state['Validation'].append(selected_columns_row4)
# val_variables=[col for col in media_channel if col!='Date']
# if st.checkbox('Validate all'):
#     st.session_state['Validation'].extend(val_variables)
# validation_data=pd.DataFrame({'Variables':val_variables,
#                             'Validated':[1 if col in st.session_state['Validation'] else 0 for col in val_variables],
#                             'Bucket':[Categorised_data[col]['VB'] for col in val_variables]})
# gd=GridOptionsBuilder.from_dataframe(validation_data)
# gd.configure_pagination(enabled=True)
# gd.configure_selection(use_checkbox=True,selection_mode='multiple')
# #gd.configure_selection_toggle_all(None, show_toggle_all=True)
# #gd.configure_columns_auto_size_mode(GridOptionsBuilder.configure_columns)
# gridoptions=gd.build()
# #st.text(st.session_state['Validation'])
# table = AgGrid(validation_data,gridOptions=gridoptions,update_mode=GridUpdateMode.SELECTION_CHANGED,fit_columns_on_grid_load=True)
# #st.table(table)
# selected_rows = table["selected_rows"]
# st.session_state['Validation'].extend([col['Variables'] for col in selected_rows])
# not_validated_variables = [col for col in val_variables if col not in st.session_state["Validation"]]
# if not_validated_variables:
#     not_validated_message = f'The following variables are not validated:\n{" , ".join(not_validated_variables)}'
#     st.warning(not_validated_message)

options = list(df.select_dtypes(np.number).columns)
st.markdown(' ')
st.markdown(' ')
st.markdown('# Exploratory Data Analysis')
st.markdown(' ')

selected_options = []
num_columns = 4
num_rows = -(-len(options) // num_columns)  # Ceiling division to calculate rows

# Create a grid of checkboxes
st.header('Select Features for Correlation Plot')
tick=False
if st.checkbox('Select all'):
    tick=True
selected_options = []
for row in range(num_rows):
    cols = st.columns(num_columns)
    for col in cols:
        if options:
            option = options.pop(0) 
            selected = col.checkbox(option,value=tick)
            if selected:
                selected_options.append(option)
# Display selected options
#st.write('You selected:', selected_options)
st.pyplot(correlation_plot(df,selected_options,target_column))