Create utils/charts.py

utils/charts.py

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+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import pandas as pd
+import streamlit as st
+
+class ProcurementCharts:
+    @staticmethod
+    def create_spend_trend_chart(df):
+        """Create spending trend line chart"""
+        df['PO_Date'] = pd.to_datetime(df['PO_Date'])
+        monthly_spend = df.groupby(df['PO_Date'].dt.to_period('M'))['Total_Value'].sum().reset_index()
+        monthly_spend['PO_Date'] = monthly_spend['PO_Date'].astype(str)
+        
+        fig = px.line(monthly_spend, x='PO_Date', y='Total_Value',
+                     title='📈 Monthly Spending Trend',
+                     labels={'Total_Value': 'Spend ($)', 'PO_Date': 'Month'})
+        
+        fig.update_layout(
+            plot_bgcolor='rgba(0,0,0,0)',
+            paper_bgcolor='rgba(0,0,0,0)',
+            font=dict(color='white'),
+            title_font_size=18,
+            height=400
+        )
+        fig.update_traces(line_color='#00D4AA', line_width=3)
+        
+        return fig
+
+    @staticmethod
+    def create_category_pie_chart(df):
+        """Create category spending pie chart"""
+        category_spend = df.groupby('Category')['Total_Value'].sum().reset_index()
+        
+        fig = px.pie(category_spend, values='Total_Value', names='Category',
+                    title='🍰 Spend by Category',
+                    color_discrete_sequence=px.colors.qualitative.Set3)
+        
+        fig.update_layout(
+            plot_bgcolor='rgba(0,0,0,0)',
+            paper_bgcolor='rgba(0,0,0,0)',
+            font=dict(color='white'),
+            title_font_size=18,
+            height=400
+        )
+        
+        return fig
+
+    @staticmethod
+    def create_supplier_performance_chart(df):
+        """Create supplier performance scatter plot"""
+        supplier_metrics = df.groupby('Supplier').agg({
+            'Total_Value': 'sum',
+            'Delivery_Performance': 'mean',
+            'PO_Number': 'count'
+        }).reset_index()
+        
+        fig = px.scatter(supplier_metrics, x='Total_Value', y='Delivery_Performance',
+                        size='PO_Number', hover_name='Supplier',
+                        title='🎯 Supplier Performance vs Spend',
+                        labels={'Total_Value': 'Total Spend ($)', 
+                               'Delivery_Performance': 'Delivery Performance (%)'})
+        
+        fig.update_layout(
+            plot_bgcolor='rgba(0,0,0,0)',
+            paper_bgcolor='rgba(0,0,0,0)',
+            font=dict(color='white'),
+            title_font_size=18,
+            height=400
+        )
+        
+        return fig
+
+    @staticmethod
+    def create_kpi_cards(total_spend, total_pos, avg_delivery, top_supplier):
+        """Create KPI cards using Plotly"""
+        kpis = [
+            {"title": "Total Spend", "value": f"${total_spend:,.0f}", "color": "#FF6B6B"},
+            {"title": "Purchase Orders", "value": f"{total_pos:,}", "color": "#4ECDC4"},
+            {"title": "Avg Delivery %", "value": f"{avg_delivery:.1f}%", "color": "#45B7D1"},
+            {"title": "Top Supplier", "value": top_supplier, "color": "#96CEB4"}
+        ]
+        
+        return kpis
+
+    @staticmethod
+    def create_status_donut_chart(df):
+        """Create PO status donut chart"""
+        status_counts = df['Status'].value_counts().reset_index()
+        
+        fig = go.Figure(data=[go.Pie(labels=status_counts['Status'], 
+                                    values=status_counts['count'],
+                                    hole=.5)])
+        
+        fig.update_layout(
+            title="📋 Purchase Order Status",
+            plot_bgcolor='rgba(0,0,0,0)',
+            paper_bgcolor='rgba(0,0,0,0)',
+            font=dict(color='white'),
+            title_font_size=18,
+            height=400,
+            annotations=[dict(text='PO Status', x=0.5, y=0.5, font_size=16, showarrow=False)]
+        )
+        
+        return fig