Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta

# Set page config
st.set_page_config(page_title="IT Support Metrics", layout="wide")

# Generate sample data
def generate_sample_data(months=12):
    np.random.seed(42)
    dates = pd.date_range(end=datetime.now(), periods=months, freq='M')
    
    data = {
        'Date': dates,
        'Total_Tickets': np.random.randint(100, 500, months),
        'Resolved_Tickets': [],
        'Response_Time_Hrs': np.random.uniform(1, 24, months),
        'Customer_Satisfaction': np.random.uniform(3.5, 5, months),
    }
    
    # Calculate resolved tickets (80-95% of total tickets)
    for i in range(months):
        resolution_rate = np.random.uniform(0.80, 0.95)
        data['Resolved_Tickets'].append(int(data['Total_Tickets'][i] * resolution_rate))
    
    df = pd.DataFrame(data)
    
    # Calculate additional metrics
    df['Resolution_Rate'] = (df['Resolved_Tickets'] / df['Total_Tickets'] * 100).round(2)
    df['Open_Tickets'] = df['Total_Tickets'] - df['Resolved_Tickets']
    
    # Generate priority breakdown
    priorities = ['High', 'Medium', 'Low']
    for priority in priorities:
        df[f'{priority}_Priority'] = np.random.randint(10, 100, months)
    
    # Generate category breakdown
    categories = ['Hardware', 'Software', 'Network', 'Access', 'Other']
    for category in categories:
        df[f'{category}'] = np.random.randint(20, 100, months)
    
    return df

# Main app
st.title("IT Support Dashboard")
st.markdown("### Monthly Performance Metrics")

# Generate data
df = generate_sample_data()

# KPI Metrics for the latest month
latest_month = df.iloc[-1]

# Create three columns for KPIs
col1, col2, col3, col4 = st.columns(4)

with col1:
    st.metric(
        "Total Tickets",
        latest_month['Total_Tickets'],
        f"{latest_month['Total_Tickets'] - df.iloc[-2]['Total_Tickets']}"
    )

with col2:
    st.metric(
        "Resolution Rate",
        f"{latest_month['Resolution_Rate']}%",
        f"{(latest_month['Resolution_Rate'] - df.iloc[-2]['Resolution_Rate']).round(2)}%"
    )

with col3:
    st.metric(
        "Avg Response Time",
        f"{latest_month['Response_Time_Hrs']:.1f}h",
        f"{(df.iloc[-2]['Response_Time_Hrs'] - latest_month['Response_Time_Hrs']).round(1)}h"
    )

with col4:
    st.metric(
        "Customer Satisfaction",
        f"{latest_month['Customer_Satisfaction']:.2f}",
        f"{(latest_month['Customer_Satisfaction'] - df.iloc[-2]['Customer_Satisfaction']).round(2)}"
    )

st.markdown("---")

# Create two columns for charts
col1, col2 = st.columns(2)

with col1:
    st.markdown("### Ticket Volume Trend")
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=df['Date'], y=df['Total_Tickets'], name='Total Tickets', line=dict(color='#1f77b4')))
    fig.add_trace(go.Scatter(x=df['Date'], y=df['Resolved_Tickets'], name='Resolved Tickets', line=dict(color='#2ca02c')))
    fig.update_layout(height=400, margin=dict(l=20, r=20, t=20, b=20))
    st.plotly_chart(fig, use_container_width=True)

with col2:
    st.markdown("### Resolution Rate Trend")
    fig = px.line(df, x='Date', y='Resolution_Rate', 
                  line_shape='spline')
    fig.update_traces(line_color='#ff7f0e')
    fig.update_layout(height=400, margin=dict(l=20, r=20, t=20, b=20),
                     yaxis_title='Resolution Rate (%)')
    st.plotly_chart(fig, use_container_width=True)

# Create two columns for more charts
col1, col2 = st.columns(2)

with col1:
    st.markdown("### Ticket Priority Distribution")
    priority_data = df[['High_Priority', 'Medium_Priority', 'Low_Priority']].iloc[-1]
    fig = px.pie(values=priority_data, names=priority_data.index, 
                 title='Current Month Priority Distribution',
                 color_discrete_sequence=px.colors.qualitative.Set3)
    fig.update_layout(height=400, margin=dict(l=20, r=20, t=40, b=20))
    st.plotly_chart(fig, use_container_width=True)

with col2:
    st.markdown("### Ticket Category Distribution")
    category_data = df[['Hardware', 'Software', 'Network', 'Access', 'Other']].iloc[-1]
    fig = px.bar(x=category_data.index, y=category_data.values,
                 title='Current Month Category Distribution',
                 color_discrete_sequence=px.colors.qualitative.Set2)
    fig.update_layout(height=400, margin=dict(l=20, r=20, t=40, b=20),
                     xaxis_title='Category', yaxis_title='Number of Tickets')
    st.plotly_chart(fig, use_container_width=True)

# Detailed metrics table
st.markdown("### Monthly Details")
detailed_df = df[['Date', 'Total_Tickets', 'Resolved_Tickets', 'Resolution_Rate', 
                  'Response_Time_Hrs', 'Customer_Satisfaction']].copy()
detailed_df['Date'] = detailed_df['Date'].dt.strftime('%B %Y')
st.dataframe(detailed_df.style.format({
    'Resolution_Rate': '{:.2f}%',
    'Response_Time_Hrs': '{:.1f}',
    'Customer_Satisfaction': '{:.2f}'
}), use_container_width=True)