app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

def process_data(df):
    # Clean data and handle missing values
    df = df[['Project Category', 'Logged']].copy()
    df = df.dropna(subset=['Project Category'])  # Remove rows with missing categories
    
    # Convert to string and consolidate categories
    df['Category'] = df['Project Category'].apply(
        lambda x: 'Billable' if 'Billable' in str(x) else str(x).strip()
    )
    
    # Aggregate data
    summary = df.groupby('Category')['Logged'].sum().reset_index()
    total = summary['Logged'].sum()
    summary['Percentage'] = (summary['Logged'] / total * 100).round(1)
    
    return summary

def create_pie_chart(data):
    fig, ax = plt.subplots(figsize=(6, 6))
    wedges, texts, autotexts = ax.pie(
        data['Logged'],
        labels=data['Category'],
        autopct='%1.1f%%',
        colors=['#4CAF50', '#FFC107', '#9E9E9E'],
        startangle=90
    )
    plt.setp(autotexts, size=10, weight="bold", color='white')
    ax.set_title('Overall Utilization', pad=20)
    return fig

def create_bar_chart(df):
    # Filter and prepare non-billable data
    non_billable = df[df['Category'] == 'Non-Billable']
    non_billable = non_billable.groupby('Project Category')['Logged'].sum().reset_index()
    
    fig, ax = plt.subplots(figsize=(10, 4))
    non_billable.plot(
        kind='bar',
        x='Project Category',
        y='Logged',
        ax=ax,
        legend=False
    )
    ax.set_title('Non-Billable Details')
    ax.set_ylabel('Hours')
    plt.xticks(rotation=45)
    return fig

def main():
    st.title('QA Utilization Dashboard')
    
    uploaded_file = st.file_uploader("Upload Timesheet", type=['xls', 'xlsx'])
    
    if uploaded_file:
        try:
            df = pd.read_excel(uploaded_file, sheet_name='Report')
            processed_data = process_data(df)
            
            # Show main visualization
            st.header("Overall Utilization")
            col1, col2 = st.columns([2, 1])
            
            with col1:
                if not processed_data.empty:
                    st.pyplot(create_pie_chart(processed_data))
                else:
                    st.warning("No data available for visualization")
            
            with col2:
                st.dataframe(
                    processed_data[['Category', 'Logged', 'Percentage']],
                    hide_index=True,
                    column_config={
                        'Logged': 'Hours',
                        'Percentage': st.column_config.NumberColumn(format="%.1f%%")
                    }
                )
            
            # Show non-billable details
            st.header("Non-Billable Breakdown")
            st.pyplot(create_bar_chart(processed_data))
            
        except Exception as e:
            st.error(f"Error processing file: {str(e)}")

if __name__ == "__main__":
    main()