import streamlit as st
import pandas as pd

complaints_count = st.container() # contains the number of complaints in each bucket
graphs = st.container() # contains the graphs for the complaints
dataset = st.container() # shows the recent complaints


# TOTAL COUNT SECTION
with complaints_count:
    st.header("Complaints counts")
    data = "./data/complaints_v1.csv"
    complaints_df = pd.read_csv(data,sep=",")
    total_counts = len(complaints_df.index)
    service_issues_counts = complaints_df['sub_cat'].value_counts()['service_issues']
    product_issues_counts = complaints_df['sub_cat'].value_counts()['product_issues']
    billing_issues_counts = complaints_df['sub_cat'].value_counts()['billing_issues']

    col1,col2,col3,col4 = st.columns(4)
    col1.metric(label="Total Complaints", value=total_counts, delta="1.2 %")
    col2.metric(label="Total Billing Issues", value=service_issues_counts, delta="-1 %")
    col3.metric(label="Total Product Issues", value=product_issues_counts, delta="-1.3 %")
    col4.metric(label="Total Service Issues   ", value=billing_issues_counts, delta="+1.2 ")


#Graphs SECTION
with graphs:
    st.header("Gprahs")


# RECENT COMPLAINTS SECTION
with dataset:
    st.header("Recent Complaints")
    ground_truth_data = pd.read_csv("./data/ground_truth.csv")
    ground_truth_data.rename(columns= {'audio_id':'Audio ID','file_name':'File Name', 'transcription':'Complaints', 'sub_cat':'Complaint Category'}, inplace = True)
    columns = ['Audio ID','File Name', 'Complaints', 'Complaint Category']
    st.dataframe(ground_truth_data[columns].iloc[15:23], 
                 hide_index=True
                 )