Code Editor for app.py

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

# Sample data generation
def generate_sample_data():
    np.random.seed(42)
    current_date = datetime.now()
    last_month = current_date - timedelta(days=30)
    
    data = {
        'total_articles': 1250,
        'kba_views_last_month': 78.5,  # percentage
        'kba_ticket_attachments': 342,
        'article_quality_index': 8.7,  # out of 10
        'contribution_index': 72.3,  # percentage
        'fcr_rate': 85.2,  # percentage
        'helpful_rate': 91.4,  # percentage
        'deflection_rate': 65.8,  # percentage
        'participation_change': 12.5,  # percentage
        'articles_updated': 89,
        'articles_retired': 15,
        'articles_from_tickets': 45,
        'articles_reviewed': 82.3,  # percentage
    }
    return data

# Page config
st.set_page_config(page_title="Knowledge Base Analytics", layout="wide")

# Title
st.title("Knowledge Base Analytics Dashboard")

# Get data
data = generate_sample_data()

# Create three columns for metrics
col1, col2, col3 = st.columns(3)

with col1:
    st.metric(
        label="Total Published Articles",
        value=data['total_articles'],
        delta=f"+{23} from last month"
    )
    st.metric(
        label="KBAs Viewed (Last Month)",
        value=f"{data['kba_views_last_month']}%",
        delta=f"+{5.2}%"
    )
    st.metric(
        label="Article Quality Index",
        value=f"{data['article_quality_index']}/10",
        delta="+0.5"
    )
    st.metric(
        label="Ticket Deflection Rate",
        value=f"{data['deflection_rate']}%",
        delta="+2.3%"
    )

with col2:
    st.metric(
        label="KBAs Attached to Tickets",
        value=data['kba_ticket_attachments'],
        delta="+45"
    )
    st.metric(
        label="FCR Rate",
        value=f"{data['fcr_rate']}%",
        delta="+3.2%"
    )
    st.metric(
        label="Articles Updated",
        value=data['articles_updated'],
        delta="+12"
    )
    st.metric(
        label="Articles from Tickets",
        value=data['articles_from_tickets'],
        delta="+8"
    )

with col3:
    st.metric(
        label="Contribution Index",
        value=f"{data['contribution_index']}%",
        delta="+5.5%"
    )
    st.metric(
        label="Articles Marked Helpful",
        value=f"{data['helpful_rate']}%",
        delta="+1.8%"
    )
    st.metric(
        label="Articles Retired",
        value=data['articles_retired'],
        delta="-3",
        delta_color="inverse"
    )
    st.metric(
        label="Articles Reviewed",
        value=f"{data['articles_reviewed']}%",
        delta="+4.5%"
    )

# Add trend charts
st.subheader("Monthly Trends")

# Sample monthly data for trends
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
views_trend = np.random.randint(1000, 1500, len(months))
quality_trend = np.random.uniform(8.0, 9.0, len(months))

# Create two columns for charts
chart_col1, chart_col2 = st.columns(2)

with chart_col1:
    fig1 = go.Figure()
    fig1.add_trace(go.Scatter(x=months, y=views_trend, mode='lines+markers', name='Views'))
    fig1.update_layout(title='Monthly Article Views', xaxis_title='Month', yaxis_title='Views')
    st.plotly_chart(fig1, use_container_width=True)

with chart_col2:
    fig2 = go.Figure()
    fig2.add_trace(go.Scatter(x=months, y=quality_trend, mode='lines+markers', name='Quality'))
    fig2.update_layout(title='Article Quality Trend', xaxis_title='Month', yaxis_title='Quality Score')
    st.plotly_chart(fig2, use_container_width=True)

# Additional insights
st.subheader("Key Insights")
st.markdown("""
- Article quality has shown consistent improvement over the last 6 months
- Ticket deflection rate has increased, indicating better knowledge base effectiveness
- Contribution from support teams has grown significantly
- Article review process is maintaining high standards with 82.3% review rate
""")