Update app.py

app.py

@@ -17,25 +17,37 @@ from xgboost import XGBRegressor
 # Configure Gemini API
 GEMINI_API_KEY = os.getenv("gemini_api")
 
-
-
 genai.configure(api_key=GEMINI_API_KEY)
 generation_config = {
-  "temperature": 1,
-  "top_p": 0.95,
-  "top_k": 64,
-  "max_output_tokens": 8192,
-  "response_mime_type": "text/plain",
+    "temperature": 1,
+    "top_p": 0.95,
+    "top_k": 64,
+    "max_output_tokens": 8192,
+    "response_mime_type": "text/plain",
 }
 
 model = genai.GenerativeModel(
-  model_name="gemini-2.0-pro-exp-02-05",
-  generation_config=generation_config,
+    model_name="gemini-2.0-pro-exp-02-05",
+    generation_config=generation_config,
 )
 
 chat_model = genai.GenerativeModel('"gemini-2.0-pro-exp-02-05"')
 
-# Create and save a simple model on first run
+class SupplyChainState:
+    def __init__(self):
+        self.sales_df = None
+        self.supplier_df = None
+        self.text_data = None
+        self.chat_history = []
+        self.analysis_results = {}
+        self.freight_predictions = []
+        
+        try:
+            self.freight_model = create_initial_model()
+        except Exception as e:
+            print(f"Warning: Could not create freight prediction model: {e}")
+            self.freight_model = None
+
 def create_initial_model():
     n_samples = 1000
     np.random.seed(42)
@@ -86,245 +98,9 @@ def create_initial_model():
     
     return model
 
-# Enhanced CSS styling
-CUSTOM_CSS = '''
-.gradio-container {
-    max-width: 1200px !important;
-    margin: auto !important;
-    padding: 20px !important;
-    background-color: #1a1a1a !important;
-    color: #ffffff !important;
-}
-
-.main-header {
-    background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%) !important;
-    color: white !important;
-    padding: 30px !important;
-    border-radius: 15px !important;
-    margin-bottom: 30px !important;
-    text-align: center !important;
-    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2) !important;
-}
-
-.app-title {
-    font-size: 2.5em !important;
-    font-weight: bold !important;
-    margin-bottom: 10px !important;
-    background: linear-gradient(90deg, #ffffff, #3498DB) !important;
-    -webkit-background-clip: text !important;
-    -webkit-text-fill-color: transparent !important;
-    text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3) !important;
-}
-
-.app-subtitle {
-    font-size: 1.3em !important;
-    color: #89CFF0 !important;
-    margin-bottom: 15px !important;
-    font-weight: 500 !important;
-}
-
-.app-description {
-    font-size: 1.1em !important;
-    color: #B0C4DE !important;
-    margin-bottom: 20px !important;
-    line-height: 1.5 !important;
-}
-
-.creator-info {
-    font-size: 1.2em !important;
-    color: #3498DB !important;
-    margin-top: 15px !important;
-    padding: 10px !important;
-    border-top: 2px solid rgba(52, 152, 219, 0.3) !important;
-    font-style: italic !important;
-}
-
-# Add this to your CUSTOM_CSS string
-
-.gr-checkbox-group {
-    background: #363636 !important;
-    padding: 15px !important;
-    border-radius: 10px !important;
-    margin: 10px 0 !important;
-}
-
-.gr-checkbox {
-    margin: 10px 0 !important;
-    cursor: pointer !important;
-}
-
-.gr-checkbox input[type="checkbox"] {
-    width: 20px !important;
-    height: 20px !important;
-    margin-right: 10px !important;
-    cursor: pointer !important;
-}
-
-.gr-checkbox label {
-    color: #ffffff !important;
-    font-size: 1.1em !important;
-    cursor: pointer !important;
-}
-
-.gr-checkbox:hover {
-    background-color: #404040 !important;
-    border-radius: 5px !important;
-    transition: background-color 0.3s ease !important;
-}
-
-.gr-checkbox input[type="checkbox"]:checked + label {
-    color: #3498DB !important;
-    font-weight: bold !important;
-}
-
-
-
-
-
-.status-box {
-    background: #363636 !important;
-    border-left: 4px solid #3498DB !important;
-    padding: 15px !important;
-    margin: 10px 0 !important;
-    border-radius: 0 5px 5px 0 !important;
-    color: #ffffff !important;
-}
-
-.chart-container {
-    background: #2d2d2d !important;
-    padding: 20px !important;
-    border-radius: 10px !important;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important;
-    color: #ffffff !important;
-}
-
-.chat-container {
-    height: 400px !important;
-    overflow-y: auto !important;
-    border: 1px solid #404040 !important;
-    border-radius: 10px !important;
-    padding: 15px !important;
-    background: #2d2d2d !important;
-    color: #ffffff !important;
-}
-
-.file-format-help {
-    background: #363636 !important;
-    padding: 15px !important;
-    border-radius: 10px !important;
-    margin-top: 20px !important;
-    border-left: 4px solid #3498DB !important;
-}
-
-.file-instructions {
-    color: #89CFF0 !important;
-    font-size: 0.9em !important;
-    margin-top: 5px !important;
-    font-style: italic !important;
-    line-height: 1.4 !important;
-}
-
-.file-upload {
-    border: 2px dashed #404040 !important;
-    border-radius: 10px !important;
-    padding: 20px !important;
-    text-align: center !important;
-    background: #2d2d2d !important;
-    color: #ffffff !important;
-    transition: all 0.3s ease !important;
-    margin-bottom: 10px !important;
-}
-
-.file-upload:hover {
-    border-color: #3498DB !important;
-    background: #363636 !important;
-}
-
-.file-upload.drag-enter {
-    border-color: #3498DB !important;
-    background: #363636 !important;
-    transform: scale(1.02) !important;
-}
-
-.file-upload .upload-label {
-    font-size: 1.1em !important;
-    font-weight: 500 !important;
-    margin-bottom: 10px !important;
-}
-
-.result-box {
-    background: #363636 !important;
-    border: 1px solid #404040 !important;
-    border-radius: 10px !important;
-    padding: 20px !important;
-    margin-top: 15px !important;
-    color: #ffffff !important;
-}
-
-.tab-content {
-    background: #2d2d2d !important;
-    padding: 20px !important;
-    border-radius: 10px !important;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important;
-    color: #ffffff !important;
-}
-
-input, select, textarea {
-    background: #363636 !important;
-    color: #ffffff !important;
-    border: 1px solid #404040 !important;
-}
-
-input:focus, select:focus, textarea:focus {
-    border-color: #3498DB !important;
-    box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.2) !important;
-}
-
-.action-button {
-    background: #3498DB !important;
-    color: white !important;
-    border: none !important;
-    padding: 10px 20px !important;
-    border-radius: 5px !important;
-    cursor: pointer !important;
-    transition: all 0.3s ease !important;
-}
-
-.action-button:hover {
-    background: #2980B9 !important;
-    transform: translateY(-2px) !important;
-    box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important;
-}
-
-.footer {
-    text-align: center !important;
-    padding: 20px !important;
-    margin-top: 40px !important;
-    border-top: 1px solid #404040 !important;
-    color: #888888 !important;
-}
-'''
-
-class SupplyChainState:
-    def __init__(self):
-        self.sales_df = None
-        self.supplier_df = None
-        self.text_data = None
-        self.chat_history = []
-        self.analysis_results = {}
-        self.freight_predictions = []
-        
-        try:
-            self.freight_model = create_initial_model()
-        except Exception as e:
-            print(f"Warning: Could not create freight prediction model: {e}")
-            self.freight_model = None
-
 def process_uploaded_data(state, sales_file, supplier_file, text_data):
-    """Process uploaded files and store in state"""
     try:
         if sales_file is not None:
-            # Check sales file extension
             file_ext = os.path.splitext(sales_file.name)[1].lower()
             if file_ext not in ['.xlsx', '.xls', '.csv']:
                 return '❌ Error: Sales data must be in Excel (.xlsx, .xls) or CSV format'
@@ -338,7 +114,6 @@ def process_uploaded_data(state, sales_file, supplier_file, text_data):
                 return f'❌ Error reading sales data: {str(e)}'
             
         if supplier_file is not None:
-            # Check supplier file extension
             file_ext = os.path.splitext(supplier_file.name)[1].lower()
             if file_ext not in ['.xlsx', '.xls', '.csv']:
                 return '❌ Error: Supplier data must be in Excel (.xlsx, .xls) or CSV format'
@@ -355,10 +130,8 @@ def process_uploaded_data(state, sales_file, supplier_file, text_data):
         return "✅ Data processed successfully"
     except Exception as e:
         return f'❌ Error processing data: {str(e)}'
-        
 
 def perform_demand_forecasting(state):
-    """Perform demand forecasting using Gemini"""
     if state.sales_df is None:
         return "Error: No sales data provided", None, "Please upload sales data first"
     
@@ -394,7 +167,6 @@ def perform_demand_forecasting(state):
         return f"❌ Error in demand forecasting: {str(e)}", None, "Analysis failed"
 
 def perform_risk_assessment(state):
-    """Perform risk assessment using Gemini"""
     if state.supplier_df is None:
         return "Error: No supplier data provided", None, "Please upload supplier data first"
     
@@ -432,15 +204,10 @@ 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:
@@ -460,7 +227,6 @@ def perform_inventory_optimization(state):
         response = model.generate_content(prompt)
         analysis_text = response.text
 
-        # Create inventory level visualization
         fig = go.Figure()
         
         if 'quantity' in state.sales_df.columns:
@@ -487,13 +253,9 @@ def perform_inventory_optimization(state):
         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:
@@ -513,12 +275,10 @@ def perform_supplier_performance(state):
         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]),
@@ -541,15 +301,10 @@ def perform_supplier_performance(state):
         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"
@@ -574,10 +329,8 @@ def perform_sustainability_analysis(state):
         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]
@@ -615,12 +368,10 @@ def perform_sustainability_analysis(state):
         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):
-    """Predict freight cost using the model"""
+                       shipment_mode, air_charter_weight=0, ocean_weight=0, truck_weight=0,
+                       air_charter_value=0, ocean_value=0, truck_value=0):
     if state.freight_model is None:
         return "Error: Freight prediction model not loaded"
         
@@ -644,7 +395,6 @@ def predict_freight_cost(state, weight, line_item_value, cost_per_kg,
         return f"Error making prediction: {str(e)}"
 
 def chat_with_navigator(state, message):
-    """Handle chat interactions"""
     try:
         context = "Available data and analysis:\n"
         if state.sales_df is not None:
@@ -684,7 +434,6 @@ def chat_with_navigator(state, message):
         return [{"role": "assistant", "content": f"Error: {str(e)}"}]
 
 def generate_pdf_report(state, analysis_options):
-    """Generate PDF report with analysis results"""
     try:
         temp_dir = tempfile.mkdtemp()
         pdf_path = os.path.join(temp_dir, "supply_chain_report.pdf")
@@ -693,6 +442,7 @@ def generate_pdf_report(state, analysis_options):
         styles = getSampleStyleSheet()
         story = []
         
+        # Create custom title style
         title_style = ParagraphStyle(
             'CustomTitle',
             parent=styles['Heading1'],
@@ -708,11 +458,6 @@ def generate_pdf_report(state, analysis_options):
         story.append(Paragraph(f"Generated on: {timestamp}", styles['Normal']))
         story.append(Spacer(1, 20))
         
-        story.append(Paragraph("Executive Summary", styles['Heading2']))
-        summary_text = "This report provides a comprehensive analysis of supply chain data, including demand forecasting, risk assessment, and optimization recommendations."
-        story.append(Paragraph(summary_text, styles['Normal']))
-        story.append(Spacer(1, 20))
-        
         if state.analysis_results:
             for analysis_type, results in state.analysis_results.items():
                 if analysis_type in analysis_options:
@@ -760,12 +505,10 @@ def generate_pdf_report(state, analysis_options):
         return None
 
 def run_analyses(state, choices, sales_file, supplier_file, text_data):
-    """Run selected analyses"""
     results = []
     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
@@ -819,106 +562,12 @@ def run_analyses(state, choices, sales_file, supplier_file, text_data):
     return combined_results, final_figure, combined_status
 
 def predict_and_store_freight(state, *args):
-    """Predict freight cost and store the result"""
     result = predict_freight_cost(state, *args)
     if isinstance(result, (int, float)):
         state.freight_predictions.append(result)
     return result
 
-
-CUSTOM_CSS = """
-/* Horizontal tabs layout */
-.tabs {
-    display: flex !important;
-    flex-direction: column !important;
-    gap: 1rem !important;
-}
-
-.tabs > .tab-nav {
-    display: flex !important;
-    flex-direction: row !important;
-    gap: 0.5rem !important;
-    padding: 0.5rem !important;
-    background: #f8f9fa !important;
-    border-radius: 8px !important;
-    border-bottom: 2px solid #e9ecef !important;
-}
-
-.tabs > .tab-nav > button {
-    flex: 1 !important;
-    padding: 0.75rem 1rem !important;
-    border-radius: 6px !important;
-    border: none !important;
-    background: transparent !important;
-    color: #495057 !important;
-    font-weight: 500 !important;
-    transition: all 0.2s ease !important;
-}
-
-.tabs > .tab-nav > button.selected {
-    background: white !important;
-    color: #228be6 !important;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important;
-}
-
-.tab-content {
-    padding: 1.5rem !important;
-    background: white !important;
-    border-radius: 8px !important;
-    box-shadow: 0 2px 8px rgba(0,0,0,0.05) !important;
-}
-
-/* Additional styling */
-.main-header {
-    background: linear-gradient(135deg, #0061a8 0%, #003459 100%);
-    padding: 2rem;
-    color: white;
-    border-radius: 8px;
-    margin-bottom: 2rem;
-}
-
-.app-title {
-    font-size: 2.5rem !important;
-    margin-bottom: 0.5rem !important;
-}
-
-.app-subtitle {
-    opacity: 0.9;
-    margin-bottom: 1rem !important;
-}
-
-.action-button {
-    background: #228be6 !important;
-    border-radius: 6px !important;
-    transition: all 0.2s ease !important;
-}
-
-.action-button:hover {
-    transform: translateY(-2px) !important;
-    box-shadow: 0 4px 8px rgba(34, 139, 230, 0.2) !important;
-}
-
-.file-upload {
-    border: 2px dashed #e9ecef !important;
-    border-radius: 8px !important;
-    padding: 1rem !important;
-}
-
-.result-box {
-    background: #f8f9fa !important;
-    border-radius: 6px !important;
-    padding: 1rem !important;
-}
-
-.chart-container {
-    background: black !important;
-    border-radius: 8px !important;
-    box-shadow: 0 2px 8px rgba(0,0,0,0.05) !important;
-}
-"""
-
 def create_interface():
-    """Create Gradio interface with enhanced UI"""
     state = SupplyChainState()
     
     with gr.Blocks(css=CUSTOM_CSS, title="SupplyChainAI Navigator") as demo:
@@ -1001,10 +650,10 @@ def create_interface():
                             label="Visualization",
                             elem_classes="chart-container"
                         )
-                        processing_status = gr.Textbox(  # Changed from raw_output
-                        label="Processing Status",
-                        elem_classes="status-box"
-                    )
+                        processing_status = gr.Textbox(
+                            label="Processing Status",
+                            elem_classes="status-box"
+                        )
 
             # Cost Prediction Tab
             with gr.Tab("💰 Cost Prediction", elem_classes="tab-content"):
@@ -1065,6 +714,17 @@ def create_interface():
 
             # Report Tab
             with gr.Tab("📑 Report", elem_classes="tab-content"):
+                report_options = gr.CheckboxGroup(
+                    choices=[
+                        "📈 Demand Forecasting",
+                        "⚠️ Risk Assessment",
+                        "📦 Inventory Optimization",
+                        "🤝 Supplier Performance",
+                        "🌿 Sustainability Analysis"
+                    ],
+                    label="Select sections to include",
+                    value=[]
+                )
                 report_button = gr.Button(
                     "📄 Generate Report",
                     variant="primary",
@@ -1075,28 +735,19 @@ def create_interface():
                 )
 
         # Event Handlers
-        def update_mode_inputs(mode):
-            return {
-                air_inputs: gr.update(visible=mode=="✈️ Air"),
-                ocean_inputs: gr.update(visible=mode=="🚢 Ocean"),
-                truck_inputs: gr.update(visible=mode=="🚛 Truck")
-            }
-        
         upload_button.click(
             fn=lambda *args: process_uploaded_data(state, *args),
             inputs=[sales_data_upload, supplier_data_upload, text_input_area],
-            outputs=[upload_status]
-        )
+            outputs=[upload_status])
         
         analyze_button.click(
             fn=lambda choices, sales, supplier, text: run_analyses(state, choices, sales, supplier, text),
             inputs=[analysis_options, sales_data_upload, supplier_data_upload, text_input_area],
-            outputs=[analysis_output, plot_output, processing_status]  # Updated output
+            outputs=[analysis_output, plot_output, processing_status]
         )
 
-
         predict_button.click(
-            fn=lambda *args: predict_freight_cost(state, *args),
+            fn=lambda *args: predict_and_store_freight(state, *args),
             inputs=[weight, line_item_value, shipment_mode],
             outputs=[freight_result]
         )
@@ -1108,17 +759,17 @@ def create_interface():
         )
         
         report_button.click(
-            fn=lambda: generate_report(state),
+            fn=lambda options: generate_pdf_report(state, options),
+            inputs=[report_options],
             outputs=[report_download]
         )
 
     return demo
 
-# Update the launch parameters in __main__:
 if __name__ == "__main__":
     demo = create_interface()
     demo.launch(
-        server_name="0.0.0.0",  # Add this line
-        server_port=7860,       # Add this line
+        server_name="0.0.0.0",
+        server_port=7860,
         debug=True
     )