main.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np

# Set page config
st.set_page_config(layout="wide", page_title="ERP Implementation Analytics", page_icon="📊")

# Load and process data
@st.cache_data
def load_data():
    df = pd.read_csv('ERP Priortization.csv', encoding='utf-8')
    df = df.dropna()  # Remove any empty rows
    return df

df = load_data()

# Main header with styling
st.markdown("""
    <style>
    .main-header {
        font-size: 2.5rem;
        font-weight: bold;
        text-align: center;
        margin-bottom: 2rem;
    }
    </style>
""", unsafe_allow_html=True)
st.markdown('<p class="main-header">ERP Implementation Analytics Dashboard</p>', unsafe_allow_html=True)

# Sidebar navigation
analysis_type = st.sidebar.selectbox(
    "Select Analysis View",
    ["Requirements Analysis", "Integration & Risk", "Implementation Planning", "Technical & AI", "Cost & ROI"]
)

if analysis_type == "Requirements Analysis":
    col1, col2 = st.columns(2)
    
    with col1:
        # Priority Distribution Chart
        moscow_counts = df['MoSCoW'].value_counts()
        fig_priority = px.pie(
            values=moscow_counts.values,
            names=moscow_counts.index,
            title="Requirements Priority Distribution",
            color_discrete_sequence=px.colors.qualitative.Set3
        )
        st.plotly_chart(fig_priority, use_container_width=True)

    with col2:
        # Departmental Requirements Breakdown
        dept_counts = df['Category'].value_counts()
        moscow_by_dept = pd.crosstab(df['Category'], df['MoSCoW'])
        fig_dept = px.bar(
            moscow_by_dept,
            title="Requirements by Department and Priority",
            barmode='stack'
        )
        fig_dept.update_layout(xaxis_tickangle=-45)
        st.plotly_chart(fig_dept, use_container_width=True)

elif analysis_type == "Integration & Risk":
    col1, col2 = st.columns(2)
    
    with col1:
        # Integration Complexity Heatmap
        integration_data = {
            'System': ['ERP Core', 'Financial', 'Project Mgmt', 'ClickUp', 'SharePoint', 'Office 365', 'Reporting', 'Mobile Apps', 'Email'],
            'Complexity': ['High', 'High', 'High', 'Medium', 'Medium', 'Medium', 'Low', 'Low', 'Low'],
            'Value': [9, 8, 8, 5, 5, 5, 3, 3, 3]
        }
        df_integration = pd.DataFrame(integration_data)
        fig_integration = px.treemap(
            df_integration,
            path=['Complexity', 'System'],
            values='Value',
            color='Complexity',
            color_discrete_map={'High': 'red', 'Medium': 'yellow', 'Low': 'green'}
        )
        st.plotly_chart(fig_integration, use_container_width=True)

    with col2:
        # Risk Assessment Matrix
        risk_data = {
            'Risk': ['Technical', 'Operational', 'Integration', 'User Adoption', 'Data Migration'],
            'Impact': [8, 7, 9, 6, 8],
            'Probability': [7, 6, 8, 7, 7],
            'Size': [50, 45, 55, 40, 45]
        }
        df_risk = pd.DataFrame(risk_data)
        fig_risk = px.scatter(
            df_risk,
            x='Probability',
            y='Impact',
            size='Size',
            text='Risk',
            title='Risk Assessment Matrix'
        )
        st.plotly_chart(fig_risk, use_container_width=True)


elif analysis_type == "Implementation Planning":
    col1, col2 = st.columns(2)
    
    with col1:
        # Timeline/Gantt Chart
        timeline_data = {
            'Task': ['Requirements Gathering', 'System Design', 'Development', 'Testing', 'Deployment'],
            'Start': ['2024-01-01', '2024-03-01', '2024-05-01', '2024-08-01', '2024-10-01'],
            'End': ['2024-02-28', '2024-04-30', '2024-07-31', '2024-09-30', '2024-12-31'],
            'Phase': ['Phase 1', 'Phase 1', 'Phase 2', 'Phase 2', 'Phase 3']
        }
        df_timeline = pd.DataFrame(timeline_data)
        fig_timeline = px.timeline(
            df_timeline,
            x_start='Start',
            x_end='End',
            y='Task',
            color='Phase',
            title='Implementation Timeline'
        )
        st.plotly_chart(fig_timeline, use_container_width=True)

    with col2:
        # Resource Allocation
        resource_data = {
            'Department': ['IT', 'Finance', 'Operations', 'Training', 'Support'],
            'Allocation': [80, 60, 40, 30, 50]
        }
        fig_resource = px.bar(
            resource_data,
            x='Department',
            y='Allocation',
            title='Resource Allocation (%)',
            color='Department'
        )
        st.plotly_chart(fig_resource, use_container_width=True)

elif analysis_type == "Technical & AI":
    col1, col2 = st.columns(2)
    
    with col1:
        # Technical Architecture Components
        tech_data = {
            'Component': ['Cloud Infrastructure', 'Security', 'Integration', 'Data Management', 'User Access'],
            'Complexity': [8, 9, 7, 6, 5],
            'Priority': ['High', 'High', 'Medium', 'Medium', 'Low']
        }
        fig_tech = px.scatter(
            tech_data,
            x='Complexity',
            y='Component',
            color='Priority',
            size=[40]*5,
            title='Technical Components Analysis'
        )
        st.plotly_chart(fig_tech, use_container_width=True)

    with col2:
        # AI Capabilities
        ai_data = {
            'Capability': ['Process Automation', 'Predictive Analytics', 'NLP', 'Security', 'Reporting'],
            'Readiness': [90, 70, 50, 80, 85]
        }
        fig_ai = px.bar(
            ai_data,
            x='Capability',
            y='Readiness',
            title='AI Capabilities Readiness (%)',
            color='Capability'
        )
        st.plotly_chart(fig_ai, use_container_width=True)

elif analysis_type == "Cost & ROI":
    col1, col2 = st.columns(2)
    
    with col1:
        # Cost-Benefit Matrix
        cost_benefit_data = {
            'Initiative': ['Core ERP', 'AI Features', 'Mobile App', 'Integration', 'Training'],
            'Cost': [90, 70, 40, 60, 30],
            'Benefit': [95, 75, 45, 70, 40],
            'Category': ['High-High', 'High-High', 'Low-Low', 'High-Low', 'Low-High']
        }
        fig_cost = px.scatter(
            cost_benefit_data,
            x='Cost',
            y='Benefit',
            text='Initiative',
            color='Category',
            title='Cost-Benefit Analysis'
        )
        st.plotly_chart(fig_cost, use_container_width=True)

    with col2:
        # ROI Projection
        roi_data = {
            'Month': list(range(1, 13)),
            'Investment': [100, 150, 180, 200, 210, 220, 225, 230, 235, 240, 245, 250],
            'Returns': [0, 20, 50, 90, 140, 200, 270, 350, 440, 540, 650, 770]
        }
        df_roi = pd.DataFrame(roi_data)
        fig_roi = px.line(
            df_roi,
            x='Month',
            y=['Investment', 'Returns'],
            title='ROI Projection (12 Months)',
            labels={'value': 'Amount (₦M)', 'variable': 'Type'}
        )
        st.plotly_chart(fig_roi, use_container_width=True)

# Footer
st.markdown("---")
st.markdown("Dashboard created for Enikkom ERP Implementation")