app.py

import gradio as gr
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import io
import os
import logging
from forecast import train_and_forecast
from datetime import datetime

# Configure logging directly in the code
logger = logging.getLogger(__name__)
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('app.log'),
        logging.StreamHandler()
    ]
)

# List to store notification history
notification_history = []

# Global variable to store the latest forecast data
latest_forecast_df = None

def process_data(file, trade_selection, filter_date):
    global latest_forecast_df
    logger.info(f"Processing data: Trade selection={trade_selection}, File={file.name if file else 'None'}, Filter date={filter_date}")
    try:
        if not trade_selection:
            logger.error("No trade selected")
            return "Error: Please select a trade", None, None, None, None, None

        if not file:
            logger.error("No file uploaded")
            return "Error: No file uploaded", None, None, None, None, None
        
        # Read CSV with encoding handling
        logger.debug("Reading CSV file")
        try:
            df = pd.read_csv(file.name, encoding='utf-8')
        except UnicodeDecodeError:
            logger.debug("UTF-8 encoding failed, trying utf-8-sig for BOM")
            df = pd.read_csv(file.name, encoding='utf-8-sig')
        logger.info(f"CSV loaded with {len(df)} rows")
        
        # Trim whitespace from all string columns
        logger.debug("Trimming whitespace from string columns")
        for col in df.select_dtypes(include=['object']).columns:
            df[col] = df[col].str.strip()
        
        # Check for required columns
        required_cols = ['Trade', 'Date', 'Attendance', 'Weather']
        if not all(col in df.columns for col in required_cols):
            missing_cols = [col for col in required_cols if col not in df.columns]
            logger.error(f"Missing required columns: {missing_cols}")
            return f"Error: CSV is missing required columns: {', '.join(missing_cols)}", None, None, None, None, None
        
        # Check for empty or whitespace values
        for col in required_cols:
            if df[col].isna().any() or (df[col].astype(str).str.strip() == '').any():
                logger.error(f"Column '{col}' contains empty or whitespace values")
                return f"Error: Column '{col}' contains empty or whitespace values", None, None, None, None, None
        
        # Parse dates in DD-MM-YYYY format
        logger.debug("Parsing dates")
        df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y', errors='coerce')
        if df['Date'].isna().any():
            logger.error("Invalid date format in CSV")
            return "Error: Invalid date format in CSV. Use DD-MM-YYYY (e.g., 01-04-2025)", None, None, None, None, None
        
        # Validate Attendance
        logger.debug("Validating Attendance")
        df['Attendance'] = pd.to_numeric(df['Attendance'], errors='coerce')
        if df['Attendance'].isna().any():
            logger.error("Non-numeric values in Attendance column")
            return "Error: Attendance column contains non-numeric values", None, None, None, None, None
        
        # Ensure Attendance is positive
        df['Attendance'] = df['Attendance'].clip(lower=0)
        if (df['Attendance'] <= 0).any():
            logger.error("Attendance contains non-positive values")
            return "Error: Attendance must contain only positive values", None, None, None, None, None
        
        # Normalize Weather values to title case for consistency
        df['Weather'] = df['Weather'].str.title()
        
        # Get all trades from the CSV
        available_trades = df['Trade'].unique().tolist()
        logger.debug(f"Available trades in CSV: {available_trades}")
        
        data = df.to_dict('records')
        logger.debug(f"Input data for forecasting: {data}")
        
        # Handle "All" trades or a single trade
        trades_to_process = available_trades if trade_selection == "All" else [trade_selection]
        logger.info(f"Trades to process: {trades_to_process}")
        
        # Validate selected trade if not "All"
        if trade_selection != "All" and trade_selection not in available_trades:
            logger.error(f"Selected trade '{trade_selection}' not in CSV")
            return f"Error: Selected trade '{trade_selection}' not found in CSV. Available trades: {', '.join(available_trades)}", None, None, None, None, None
        
        # Check for duplicate dates and sufficient data for each trade
        for trade in trades_to_process:
            trade_data = df[df['Trade'] == trade]
            duplicate_dates = trade_data[trade_data.duplicated(subset=['Date'], keep=False)]
            if not duplicate_dates.empty:
                logger.error(f"Duplicate dates found for trade '{trade}': {duplicate_dates['Date'].tolist()}")
                return f"Error: Duplicate dates found for trade '{trade}': {duplicate_dates['Date'].tolist()}. Each date must be unique.", None, None, None, None, None
            
            if len(trade_data) < 2:
                logger.error(f"Insufficient data for trade '{trade}': only {len(trade_data)} rows")
                return f"Error: Insufficient data for trade '{trade}'. At least 2 data points are required, found {len(trade_data)}", None, None, None, None, None
        
        # Run forecast for each trade
        all_forecasts = []
        for trade in trades_to_process:
            logger.debug(f"Running forecast for trade: {trade}")
            result = train_and_forecast(data, trade)
            if 'error' in result:
                logger.error(f"Forecast failed for trade '{trade}': {result['error']}")
                return f"Error in forecasting for trade '{trade}': {result['error']}", None, None, None, None, None
            all_forecasts.append(pd.DataFrame(result['forecast']))
        
        logger.info("All forecasts generated successfully")
        forecast_df = pd.concat(all_forecasts, ignore_index=True)
        forecast_3_days_json = None  # Not used for "All" trades
        
        # Parse forecast dates
        forecast_df['Date'] = pd.to_datetime(forecast_df['Date'])
        
        # Ensure only positive values for Risk and Attendance in forecast data
        forecast_df['Risk'] = forecast_df['Risk'].clip(lower=0)
        forecast_df['Attendance'] = forecast_df['Attendance'].clip(lower=0)
        
        # Apply date filtering based on filter_date (daily view only)
        if filter_date:
            selected_date = pd.to_datetime(filter_date)
            forecast_df = forecast_df[forecast_df['Date'].dt.date == selected_date.date()]
            date_range_str = selected_date.strftime('%Y-%m-%d')
        else:
            date_range_str = "All dates"
        
        if forecast_df.empty:
            logger.warning("No data available for the selected date")
            return f"Error: No forecast data available for the selected date ({date_range_str})", None, None, None, None, None
        
        # Store the forecast data globally
        latest_forecast_df = forecast_df
        
        # Create bar graph for risk with enhanced layout
        logger.debug("Generating enhanced risk bar graph")
        risk_bar_fig = go.Figure()
        colors = px.colors.qualitative.Plotly  # Consistent color palette for trades
        
        for idx, trade in enumerate(trades_to_process):
            trade_forecast = forecast_df[forecast_df['Trade'] == trade]
            risk_bar_fig.add_trace(go.Bar(
                x=trade_forecast['Date'],
                y=trade_forecast['Risk'].clip(lower=0),  # Cap risk values to zero or above
                name=trade,
                marker_color=colors[idx % len(colors)],
                hovertemplate=(
                    f"Trade: {trade}<br>" +
                    "Date: %{x}<br>" +
                    "Risk: %{y}%"
                )
            ))
        
        risk_bar_fig.update_layout(
            title=f"Shortage Risk Bar Graph ({date_range_str}, Risk in %, {len(trades_to_process)} Trade{'s' if len(trades_to_process) != 1 else ''})",
            xaxis_title="Date",
            yaxis_title="Risk (%)",
            barmode='group',  # Group bars to avoid overlap
            showlegend=True,
            hovermode="closest",
            legend=dict(
                title="Trades",
                traceorder="normal",
                itemsizing="constant",
                orientation="h",
                yanchor="bottom",
                y=1.02,
                xanchor="right",
                x=1
            ),
            xaxis=dict(
                tickangle=45,  # Rotate for better readability
                tickformat="%Y-%m-%d",
                tickmode="linear",  # Ensure even spacing
                nticks=10,  # Adjust number of ticks for clarity
                gridcolor='lightgrey'
            ),
            yaxis=dict(
                range=[0, 100],  # Set a reasonable range starting from 0
                gridcolor='lightgrey',
                zeroline=True,
                zerolinewidth=2,
                zerolinecolor='black'
            ),
            plot_bgcolor='white',
            margin=dict(t=80, b=80, l=60, r=60)
        )
        
        # Add annotation for capped values
        max_risk = forecast_df['Risk'].max()
        min_risk = forecast_df['Risk'].min()
        if max_risk > 100:
            risk_bar_fig.add_annotation(
                text=f"Note: Risk values capped at 100% (Max: {max_risk:.1f}%)",
                xref="paper", yref="paper",
                x=0.5, y=-0.15,
                showarrow=False,
                font=dict(size=10)
            )
        
        logger.info("Enhanced risk bar graph generated")
        
        # Create line graph for attendance
        logger.debug("Generating attendance line graph")
        line_fig = go.Figure()
        alert_colors = {'Normal': 'blue', 'Warning': 'yellow', 'Critical': 'red'}
        
        for idx, trade in enumerate(trades_to_process):
            trade_forecast = forecast_df[forecast_df['Trade'] == trade]
            # Add line for attendance
            line_fig.add_trace(go.Scatter(
                x=trade_forecast['Date'],
                y=trade_forecast['Attendance'].clip(lower=0),
                mode='lines+markers',
                name=trade,
                line=dict(color=colors[idx % len(colors)]),
                marker=dict(size=8),
                hovertemplate=(
                    f"Trade: {trade}<br>" +
                    "Date: %{x}<br>" +
                    "Attendance: %{y}<br>" +
                    "Risk: %{customdata[0]}%<br>" +
                    "Alert: %{customdata[1]}<extra></extra>"
                ),
                customdata=trade_forecast[['Risk', 'Alert']].values
            ))
            
            # Add markers for Warning and Critical alerts
            for alert_type in ['Warning', 'Critical']:
                alert_data = trade_forecast[trade_forecast['Alert'] == alert_type]
                if not alert_data.empty:
                    line_fig.add_trace(go.Scatter(
                        x=alert_data['Date'],
                        y=alert_data['Attendance'].clip(lower=0),
                        mode='markers',
                        name=f"{trade} {alert_type}",
                        marker=dict(
                            color=alert_colors[alert_type],
                            size=12,
                            symbol='diamond',
                            line=dict(width=2, color='black')
                        ),
                        hovertemplate=(
                            f"Trade: {trade}<br>" +
                            "Date: %{x}<br>" +
                            "Attendance: %{y}<br>" +
                            "Risk: %{customdata[0]}%<br>" +
                            "Alert: {alert_type}<extra></extra>"
                        ),
                        customdata=alert_data[['Risk']].values
                    ))
        
        line_fig.update_layout(
            title=f"Attendance Forecast Line Graph ({date_range_str}, {len(trades_to_process)} Trade{'s' if len(trades_to_process) != 1 else ''})",
            xaxis_title="Date",
            yaxis_title="Attendance",
            showlegend=True,
            hovermode="closest",
            legend=dict(
                title="Trades & Alerts",
                traceorder="normal",
                itemsizing="constant"
            ),
            xaxis=dict(
                tickangle=45,
                gridcolor='lightgrey'
            ),
            yaxis=dict(
                range=[0, forecast_df['Attendance'].max() * 1.2],  # Dynamic range starting from 0
                gridcolor='lightgrey'
            ),
            plot_bgcolor='white',
            margin=dict(t=50, b=50)
        )
        logger.info("Attendance line graph generated")
        
        # Create summary statistics for filtered data
        logger.debug("Generating summary statistics")
        summary_df = forecast_df.groupby('Trade').agg({
            'Attendance': 'mean',
            'Risk': 'mean'
        }).reset_index()
        summary_df['Attendance'] = summary_df['Attendance'].round(1)
        summary_df['Risk'] = summary_df['Risk'].round(1)
        summary_df = summary_df.rename(columns={'Attendance': 'Average Attendance', 'Risk': 'Average Risk (%)'})
        logger.info(f"Summary statistics generated: {summary_df.to_dict()}")
        
        # Create PDF report with enhanced layout and small gap between trade and alerts
        logger.debug("Generating enhanced PDF report with small gap between trade and alerts")
        fig = plt.figure(figsize=(12, 20))
        gs = fig.add_gridspec(5, 1, height_ratios=[3, 3, 0.2, 1.5, 0.5], hspace=0.8)
        
        # Plot bar graph for risk with improved text alignment and spacing
        ax1 = fig.add_subplot(gs[0])
        width = 0.35  # Adjusted width for even spacing
        unique_dates = forecast_df['Date'].unique()
        x_positions = range(len(unique_dates))
        
        for idx, trade in enumerate(trades_to_process):
            trade_forecast = forecast_df[forecast_df['Trade'] == trade]
            trade_x = [pos + idx * width for pos in x_positions]
            bars = ax1.bar(
                trade_x,
                trade_forecast['Risk'].clip(lower=0),
                width,
                label=trade,
                color=colors[idx % len(colors)]
            )
            for bar, x, height in zip(bars, trade_x, trade_forecast['Risk'].clip(lower=0)):
                # Dynamic vertical offset based on bar height with adjusted padding
                offset = 5 if abs(height) < 10 else 10  # Adjusted for positive values only
                ax1.text(
                    x + width / 2,  # Center horizontally
                    height + offset,  # Dynamic vertical offset
                    f'{height:.1f}%',
                    ha='center',
                    va='bottom',  # Align text appropriately for positive values
                    fontsize=8
                )
        
        ax1.set_title(f'Shortage Risk Bar Graph ({date_range_str}, Risk in %)', fontsize=14, pad=10)
        ax1.set_xlabel('Date', fontsize=12)
        ax1.set_ylabel('Risk (%)', fontsize=12)
        ax1.set_xticks([pos + (len(trades_to_process) - 1) * width / 2 for pos in x_positions])
        ax1.set_xticklabels([date.strftime('%Y-%m-%d') for date in unique_dates], rotation=45, ha='right', fontsize=8)
        ax1.set_yticks(range(0, 125, 25))  # Balanced y-axis scale with 25% increments starting from 0
        ax1.grid(True, axis='y', linestyle='--', alpha=0.7)
        ax1.legend(title="Trades", fontsize=10, loc='upper right', bbox_to_anchor=(1.15, 1))  # Moved legend outside
        ax1.set_ylim(0, 100)  # Cap y-axis to positive values only
        ax1.tick_params(axis='x', pad=10)  # Increase padding to prevent overlap
        
        # Plot line graph for attendance
        ax2 = fig.add_subplot(gs[1])
        for idx, trade in enumerate(trades_to_process):
            trade_forecast = forecast_df[forecast_df['Trade'] == trade]
            ax2.plot(
                trade_forecast['Date'],
                trade_forecast['Attendance'].clip(lower=0),
                label=trade,
                color=colors[idx % len(colors)],
                marker='o',
                markersize=8,
                linewidth=2
            )
            for alert_type in ['Warning', 'Critical']:
                alert_data = trade_forecast[trade_forecast['Alert'] == alert_type]
                if not alert_data.empty:
                    ax2.scatter(
                        alert_data['Date'],
                        alert_data['Attendance'].clip(lower=0),
                        label=f"{trade} {alert_type}",
                        color=alert_colors[alert_type],
                        marker='D',
                        s=100,
                        edgecolors='black',
                        linewidths=1.5
                    )
        
        ax2.set_title(f'Attendance Forecast Line Graph ({date_range_str})', fontsize=14, pad=10)
        ax2.set_xlabel('Date', fontsize=12)
        ax2.set_ylabel('Attendance', fontsize=12)
        ax2.grid(True, linestyle='--', alpha=0.7)
        ax2.legend(title="Trades & Alerts", fontsize=10, loc='upper left', bbox_to_anchor=(0, -0.1), ncol=2)
        ax2.tick_params(axis='x', rotation=45, labelsize=8)
        ax2.set_ylim(bottom=0)  # Ensure y-axis starts at 0
        
        # Empty spacer subplot for small gap
        ax_spacer = fig.add_subplot(gs[2])
        ax_spacer.axis('off')
        
        # Add summary table to the plot
        ax3 = fig.add_subplot(gs[3])
        ax3.axis('off')
        table_data = [summary_df.columns.tolist()] + summary_df.values.tolist()
        table = ax3.table(cellText=table_data, cellLoc='center', loc='center', colWidths=[0.3, 0.35, 0.35])
        table.set_fontsize(12)  # Set font size manually
        table.scale(1.2, 1.2)  # Keep the scaling
        ax3.set_title('Summary Statistics (Risk in %)', fontsize=14, pad=10)
        
        # Add legend for alerts
        ax4 = fig.add_subplot(gs[4])
        ax4.axis('off')
        legend_elements = [
            mpatches.Patch(color='red', label='Critical Alert'),
            mpatches.Patch(color='yellow', label='Warning Alert'),
            mpatches.Patch(color='blue', label='Normal')
        ]
        ax4.legend(handles=legend_elements, loc='center', ncol=3, title='Alert Types', fontsize=12, title_fontsize=12)
        
        plt.subplots_adjust(top=0.9, bottom=0.1, left=0.1, right=1.2, hspace=0.6)  # Adjusted right margin for legend
        buf = io.BytesIO()
        plt.savefig(buf, format='pdf', dpi=300, bbox_inches='tight')
        plt.close()
        buf.seek(0)
        pdf_path = f"forecast_report_{'all_trades' if trade_selection == 'All' else trade_selection}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
        with open(pdf_path, 'wb') as f:
            f.write(buf.read())
        logger.info(f"Enhanced PDF report saved: {pdf_path}")
        
        # Alerts table with color-coded column and date only
        logger.debug("Generating alerts table")
        alerts_df = forecast_df[forecast_df['Alert'].isin(['Warning', 'Critical'])][['Date', 'Trade', 'Attendance', 'Risk', 'Alert']].copy()
        alerts_df['Risk'] = alerts_df['Risk'].apply(lambda x: f"{x}%")
        alerts_df['Alert Color'] = alerts_df['Alert'].apply(lambda x: 'Red' if x == 'Critical' else 'Yellow')
        alerts_df['Date'] = alerts_df['Date'].dt.strftime('%Y-%m-%d')
        logger.info(f"Alerts generated with date only: {len(alerts_df)} alerts")
        
        # Status message
        status_message = f"Forecast generated successfully for {len(trades_to_process)} trade{'s' if len(trades_to_process) != 1 else ''} (Risk values are in percentages, Date: {date_range_str}, Visualization: Bar Graph (Risk), Line Graph (Attendance))"
        
        return (
            status_message,
            risk_bar_fig,
            line_fig,
            alerts_df,
            summary_df,
            pdf_path
        )
    except Exception as e:
        logger.error(f"Processing failed: {str(e)}", exc_info=True)
        return f"Error: {str(e)}", None, None, None, None, None

def notify_contractor():
    global latest_forecast_df
    logger.info("Notify contractor button clicked")
    
    if latest_forecast_df is None:
        message = "No forecast data available to generate notifications."
        timestamp = datetime.now().strftime("%Y-%m-%d %I:%M:%S %p")
        notification_entry = f"**{timestamp}**: {message}"
        notification_history.append(notification_entry)
        history_display = "## Notification History\n" + "\n".join([f"- {entry}" for entry in notification_history])
        logger.warning("No forecast data for notifications")
        return message, history_display
    
    # Calculate average risk per trade
    risk_summary = latest_forecast_df.groupby('Trade')['Risk'].mean().round(1).to_dict()
    
    # Generate notifications for each trade
    timestamp = datetime.now().strftime("%Y-%m-%d %I:%M:%S %p")
    notifications = []
    for trade, avg_risk in risk_summary.items():
        if avg_risk < 50:
            risk_message = "Monitor, shortage risk is low"
        elif 50 <= avg_risk <= 75:
            risk_message = "Caution, shortage risk is moderate"
        else:
            risk_message = "Alert, shortage risk is high"
        
        message = f"Notice for {trade}: {risk_message} at {avg_risk}%."
        notifications.append(message)
        notification_entry = f"**{timestamp}**: {message}"
        notification_history.append(notification_entry)
        logger.info(f"Notification generated for {trade}: {message}")
    
    combined_message = "\n".join(notifications) if notifications else "No trades to notify."
    history_display = "## Notification History\n" + "\n".join([f"- {entry}" for entry in notification_history])
    
    return combined_message, history_display

def update_trades(file):
    try:
        if file:
            df = pd.read_csv(file.name, encoding='utf-8')
            trades = df['Trade'].unique().tolist()
            trades.insert(0, "All")
            logger.info(f"Trades updated from CSV with 'All' option: {trades}")
            return gr.update(choices=trades)
        logger.warning("No file provided for trade update")
        return gr.update(choices=["All"])
    except UnicodeDecodeError:
        logger.debug("UTF-8 encoding failed in update_trades, trying utf-8-sig")
        df = pd.read_csv(file.name, encoding='utf-8-sig')
        trades = df['Trade'].unique().tolist()
        trades.insert(0, "All")
        logger.info(f"Trades updated from CSV with 'All' option: {trades}")
        return gr.update(choices=trades)
    except Exception as e:
        logger.error(f"Failed to update trades: {str(e)}", exc_info=True)
        return gr.update(choices=["All"])

with gr.Blocks() as demo:
    gr.Markdown("# Labour Attendance Forecasting App")
    gr.Markdown("Upload a CSV with columns: Trade, Date, Attendance, Weather")
    
    with gr.Row():
        file_input = gr.File(label="Upload CSV")
        trade_dropdown = gr.Dropdown(label="Select Trade", choices=["All"])
    
    with gr.Row():
        filter_date = gr.DateTime(label="Site Calendar Date", value=None)
    
    with gr.Row():
        submit_button = gr.Button("Generate Forecast")
    
    notification_display = gr.Markdown("## Notification History\n*No notifications yet.*", label="Notifications")
    
    output_text = gr.Textbox(label="Status")
    heatmap_output = gr.Plot(label="Shortage Risk Bar Graph (Risk in %)")
    bar_output = gr.Plot(label="Attendance Forecast Line Graph")
    alerts_output = gr.DataFrame(label="Alerts")
    summary_output = gr.DataFrame(label="Summary Statistics (Average Attendance and Risk in %)")

    notify_button = gr.Button("Notify Contractor")
    notify_output = gr.Textbox(label="Notification Status")
    download_button = gr.File(label="Download PDF Report")
    
    file_input.change(update_trades, inputs=file_input, outputs=trade_dropdown)
    submit_button.click(
        process_data,
        inputs=[file_input, trade_dropdown, filter_date],
        outputs=[output_text, heatmap_output, bar_output, alerts_output, summary_output, download_button]
    )
    notify_button.click(
        notify_contractor,
        outputs=[notify_output, notification_display]
    )

try:
    logger.info("Launching Gradio app")
    demo.launch()
except Exception as e:
    logger.error(f"Failed to launch Gradio app: {str(e)}", exc_info=True)
    raise