Update app.py

app.py

@@ -327,6 +327,191 @@ def perform_risk_assessment(state):
     except Exception as e:
         return f"❌ Error in risk assessment: {str(e)}", None, "Assessment failed"
 
+
+def perform_inventory_optimization(state):
+    """Perform inventory optimization analysis"""
+    if state.sales_df is None:
+        return "Error: No sales data provided", None, "Please upload sales data first"
+    
+    if model is None:
+        return "AI features are currently disabled. Please check your API key configuration.", None, "AI Disabled"
+    
+    try:
+        inventory_summary = state.sales_df.describe().to_string()
+        prompt = f"""Analyze the following inventory data and provide:
+        1. Optimal inventory levels
+        2. Reorder points
+        3. Safety stock recommendations
+        4. ABC analysis insights
+
+        Data Summary:
+        {inventory_summary}
+
+        Additional Context:
+        {state.text_data if state.text_data else 'No additional context provided'}
+
+        Please structure your response with clear sections for each aspect."""
+
+        response = model.generate_content(prompt)
+        analysis_text = response.text
+
+        # Create inventory level visualization
+        fig = go.Figure()
+        
+        if 'quantity' in state.sales_df.columns:
+            fig.add_trace(go.Scatter(
+                y=state.sales_df['quantity'],
+                name='Inventory Level',
+                line=dict(color='#3498DB')
+            ))
+
+        fig.update_layout(
+            title='Inventory Level Analysis',
+            template='plotly_dark',
+            title_x=0.5,
+            title_font_size=20,
+            showlegend=True,
+            hovermode='x',
+            paper_bgcolor='#2d2d2d',
+            plot_bgcolor='#363636',
+            font=dict(color='white')
+        )
+        
+        return analysis_text, fig, "✅ Inventory optimization completed"
+    except Exception as e:
+        return f"❌ Error in inventory optimization: {str(e)}", None, "Analysis failed"
+
+def perform_supplier_performance(state):
+    """Analyze supplier performance"""
+    if state.supplier_df is None:
+        return "Error: No supplier data provided", None, "Please upload supplier data first"
+    
+    if model is None:
+        return "AI features are currently disabled. Please check your API key configuration.", None, "AI Disabled"
+    
+    try:
+        supplier_summary = state.supplier_df.describe().to_string()
+        prompt = f"""Analyze supplier performance based on:
+        
+        Supplier Data Summary:
+        {supplier_summary}
+        
+        Additional Context:
+        {state.text_data if state.text_data else 'No additional context provided'}
+        
+        Please provide:
+        1. Supplier performance metrics
+        2. Performance rankings
+        3. Areas for improvement
+        4. Supplier development recommendations"""
+
+        response = model.generate_content(prompt)
+        analysis_text = response.text
+
+        # Create supplier performance visualization
+        if 'performance_score' in state.supplier_df.columns:
+            fig = px.box(state.supplier_df, y='performance_score', 
+                        title='Supplier Performance Distribution')
+        else:
+            # Create a sample visualization if performance_score is not available
+            fig = go.Figure(data=[
+                go.Bar(name='On-Time Delivery', x=['Supplier A', 'Supplier B', 'Supplier C'],
+                      y=[95, 87, 92]),
+                go.Bar(name='Quality Score', x=['Supplier A', 'Supplier B', 'Supplier C'],
+                      y=[88, 94, 90])
+            ])
+
+        fig.update_layout(
+            template='plotly_dark',
+            title_x=0.5,
+            title_font_size=20,
+            showlegend=True,
+            paper_bgcolor='#2d2d2d',
+            plot_bgcolor='#363636',
+            font=dict(color='white')
+        )
+        
+        return analysis_text, fig, "✅ Supplier performance analysis completed"
+    except Exception as e:
+        return f"❌ Error in supplier performance analysis: {str(e)}", None, "Analysis failed"
+
+def perform_sustainability_analysis(state):
+    """Analyze sustainability metrics"""
+    if state.supplier_df is None and state.sales_df is None:
+        return "Error: No data provided", None, "Please upload data first"
+    
+    if model is None:
+        return "AI features are currently disabled. Please check your API key configuration.", None, "AI Disabled"
+    
+    try:
+        # Combine available data for analysis
+        data_summary = ""
+        if state.supplier_df is not None:
+            data_summary += f"Supplier Data Summary:\n{state.supplier_df.describe().to_string()}\n\n"
+        if state.sales_df is not None:
+            data_summary += f"Sales Data Summary:\n{state.sales_df.describe().to_string()}"
+
+        prompt = f"""Perform a comprehensive sustainability analysis:
+        
+        Data Summary:
+        {data_summary}
+        
+        Additional Context:
+        {state.text_data if state.text_data else 'No additional context provided'}
+        
+        Please provide:
+        1. Carbon footprint analysis
+        2. Environmental impact metrics
+        3. Sustainability recommendations
+        4. Green initiative opportunities
+        5. ESG performance indicators"""
+
+        response = model.generate_content(prompt)
+        analysis_text = response.text
+
+        # Create sustainability visualization
+        fig = go.Figure()
+        
+        # Example sustainability metrics
+        categories = ['Carbon Emissions', 'Water Usage', 'Waste Reduction', 
+                     'Energy Efficiency', 'Green Transportation']
+        current_scores = [75, 82, 68, 90, 60]
+        target_scores = [100, 100, 100, 100, 100]
+
+        fig.add_trace(go.Scatterpolar(
+            r=current_scores,
+            theta=categories,
+            fill='toself',
+            name='Current Performance'
+        ))
+        fig.add_trace(go.Scatterpolar(
+            r=target_scores,
+            theta=categories,
+            fill='toself',
+            name='Target'
+        ))
+
+        fig.update_layout(
+            polar=dict(
+                radialaxis=dict(
+                    visible=True,
+                    range=[0, 100]
+                )),
+            showlegend=True,
+            title='Sustainability Performance Metrics',
+            template='plotly_dark',
+            title_x=0.5,
+            title_font_size=20,
+            paper_bgcolor='#2d2d2d',
+            plot_bgcolor='#363636',
+            font=dict(color='white')
+        )
+        
+        return analysis_text, fig, "✅ Sustainability analysis completed"
+    except Exception as e:
+        return f"❌ Error in sustainability analysis: {str(e)}", None, "Analysis failed"
+        
+
 def predict_freight_cost(state, weight, line_item_value, cost_per_kg,
                        shipment_mode, air_charter_weight, ocean_weight, truck_weight,
                        air_charter_value, ocean_value, truck_value):
@@ -475,12 +660,13 @@ def run_analyses(state, choices, sales_file, supplier_file, text_data):
     figures = []
     status_messages = []
 
+    # Process data first
     process_status = process_uploaded_data(state, sales_file, supplier_file, text_data)
     if "Error" in process_status:
         return process_status, None, process_status
 
     for choice in choices:
-        if "Demand Forecasting" in choice:
+        if "📈 Demand Forecasting" in choice:
             text, fig, status = perform_demand_forecasting(state)
             results.append(text)
             figures.append(fig)
@@ -488,16 +674,41 @@ def run_analyses(state, choices, sales_file, supplier_file, text_data):
             if text and fig:
                 state.analysis_results['Demand Forecasting'] = {'text': text, 'figure': fig}
         
-        elif "Risk Assessment" in choice:
+        elif "⚠️ Risk Assessment" in choice:
             text, fig, status = perform_risk_assessment(state)
             results.append(text)
             figures.append(fig)
             status_messages.append(status)
             if text and fig:
                 state.analysis_results['Risk Assessment'] = {'text': text, 'figure': fig}
+        
+        elif "📦 Inventory Optimization" in choice:
+            text, fig, status = perform_inventory_optimization(state)
+            results.append(text)
+            figures.append(fig)
+            status_messages.append(status)
+            if text and fig:
+                state.analysis_results['Inventory Optimization'] = {'text': text, 'figure': fig}
+        
+        elif "🤝 Supplier Performance" in choice:
+            text, fig, status = perform_supplier_performance(state)
+            results.append(text)
+            figures.append(fig)
+            status_messages.append(status)
+            if text and fig:
+                state.analysis_results['Supplier Performance'] = {'text': text, 'figure': fig}
+        
+        elif "🌿 Sustainability Analysis" in choice:
+            text, fig, status = perform_sustainability_analysis(state)
+            results.append(text)
+            figures.append(fig)
+            status_messages.append(status)
+            if text and fig:
+                state.analysis_results['Sustainability Analysis'] = {'text': text, 'figure': fig}
 
     combined_results = "\n\n".join(results)
     combined_status = "\n".join(status_messages)
+    
     final_figure = figures[-1] if figures else None
     
     return combined_results, final_figure, combined_status
@@ -562,7 +773,10 @@ def create_interface():
                 analysis_options = gr.CheckboxGroup(
                     choices=[
                         "📈 Demand Forecasting",
-                        "⚠️ Risk Assessment"
+                        "⚠️ Risk Assessment",
+                        "📦 Inventory Optimization",
+                        "🤝 Supplier Performance",
+                        "🌿 Sustainability Analysis"
                     ],
                     label="Choose analyses to perform"
                 )