report_generator.py

"""

Report generator for YourCarbonFootprint application.

Generates PDF reports and visualizations.

"""

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from fpdf import FPDF
import os
from datetime import datetime
import base64
from io import BytesIO

class ReportGenerator:
    def __init__(self, data_handler, translations=None):
        """Initialize the ReportGenerator class."""
        self.data_handler = data_handler
        # Use provided translations or default
        self.translations = translations or {
            'English': {
                'title': 'Carbon Emissions Report',
                'company': 'Company',
                'industry': 'Industry',
                'location': 'Location',
                'reporting_period': 'Reporting Period',
                'generated_on': 'Generated on',
                'summary': 'Summary',
                'total_emissions': 'Total Emissions',
                'emissions_by_scope': 'Emissions by Scope:',
                'top_categories': 'Top Categories:',
                'emissions_data': 'Emissions Data',
                'date': 'Date',
                'scope': 'Scope',
                'category': 'Category',
                'activity': 'Activity',
                'quantity': 'Quantity',
                'unit': 'Unit',
                'factor': 'Factor',
                'emissions_kgco2e': 'Emissions (kgCO2e)',
                'reg_compliance': 'Regulatory Compliance',
                'cbam': 'EU CBAM: This report can be used as supporting documentation for EU CBAM compliance.',
                'gx_league': 'Japan GX League: This report follows the GX League reporting format.',
                'ets': 'Indonesia ETS/ETP: This report can be used for Indonesia ETS/ETP compliance.',
                'recommendations': 'Recommendations',
                'rec1': '1. Focus on reducing emissions from the top categories identified in this report.',
                'rec2': '2. Consider implementing energy efficiency measures for Scope 2 emissions.',
                'rec3': '3. Explore renewable energy options to reduce your carbon footprint.',
                'rec4': '4. Engage with suppliers to address Scope 3 emissions in your value chain.',
            },
            'Vietnamese': {
                'title': 'Báo cáo Phát thải Carbon',
                'company': 'Công ty',
                'industry': 'Ngành nghề',
                'location': 'Địa điểm',
                'reporting_period': 'Kỳ báo cáo',
                'generated_on': 'Ngày tạo',
                'summary': 'Tóm tắt',
                'total_emissions': 'Tổng phát thải',
                'emissions_by_scope': 'Phát thải theo phạm vi:',
                'top_categories': 'Danh mục hàng đầu:',
                'emissions_data': 'Dữ liệu phát thải',
                'date': 'Ngày',
                'scope': 'Phạm vi',
                'category': 'Danh mục',
                'activity': 'Hoạt động',
                'quantity': 'Số lượng',
                'unit': 'Đơn vị',
                'factor': 'Hệ số',
                'emissions_kgco2e': 'Phát thải (kgCO2e)',
                'reg_compliance': 'Tuân thủ quy định',
                'cbam': 'EU CBAM: Báo cáo này có thể dùng làm tài liệu hỗ trợ tuân thủ EU CBAM.',
                'gx_league': 'Japan GX League: Báo cáo này tuân theo định dạng báo cáo GX League.',
                'ets': 'Indonesia ETS/ETP: Báo cáo này có thể dùng cho tuân thủ Indonesia ETS/ETP.',
                'recommendations': 'Khuyến nghị',
                'rec1': '1. Tập trung giảm phát thải từ các danh mục hàng đầu trong báo cáo này.',
                'rec2': '2. Xem xét thực hiện các biện pháp tiết kiệm năng lượng cho phát thải phạm vi 2.',
                'rec3': '3. Khám phá các lựa chọn năng lượng tái tạo để giảm dấu chân carbon.',
                'rec4': '4. Hợp tác với nhà cung cấp để giải quyết phát thải phạm vi 3 trong chuỗi giá trị.',
            }
        }

    def t(self, key, language):
        return self.translations.get(language, self.translations['English']).get(key, key)

    def generate_pdf_report(self, file_path=None, start_date=None, end_date=None, company_info=None, language='English'):
        """

        Generate PDF report.

        

        Args:

            file_path (str, optional): Path to save PDF file

            start_date (datetime, optional): Start date for filtering

            end_date (datetime, optional): End date for filtering

            company_info (dict, optional): Company information

            language (str, optional): Language for the report text



        Returns:

            bytes or bool: PDF bytes if file_path is None, otherwise True if successful

        """
        try:
            # Get filtered data
            data = self.data_handler.get_filtered_data(start_date, end_date)
            
            if len(data) == 0:
                return False, "No data available for the selected period."
            
            # Create PDF
            pdf = FPDF()
            pdf.add_page()
            pdf.set_font("Arial", "B", 16)
            pdf.cell(0, 10, self.t('title', language), 0, 1, "C")
            pdf.set_font("Arial", "", 12)
            if company_info:
                pdf.cell(0, 10, f"{self.t('company', language)}: {company_info.get('name', 'N/A')}", 0, 1)
                pdf.cell(0, 10, f"{self.t('industry', language)}: {company_info.get('industry', 'N/A')}", 0, 1)
                pdf.cell(0, 10, f"{self.t('location', language)}: {company_info.get('location', 'N/A')}", 0, 1)
            pdf.cell(0, 10, f"{self.t('reporting_period', language)}: {start_date.strftime('%Y-%m-%d') if start_date else 'All'} to {end_date.strftime('%Y-%m-%d') if end_date else 'All'}", 0, 1)
            pdf.cell(0, 10, f"{self.t('generated_on', language)}: {datetime.now().strftime('%Y-%m-%d')}", 0, 1)
            pdf.ln(10)
            pdf.set_font("Arial", "B", 14)
            pdf.cell(0, 10, self.t('summary', language), 0, 1)
            pdf.set_font("Arial", "", 12)
            total_emissions = data['emissions_kgCO2e'].sum()
            pdf.cell(0, 10, f"{self.t('total_emissions', language)}: {total_emissions:.2f} kgCO2e", 0, 1)
            scope_data = data.groupby('scope')['emissions_kgCO2e'].sum().reset_index()
            pdf.ln(5)
            pdf.cell(0, 10, self.t('emissions_by_scope', language), 0, 1)
            for _, row in scope_data.iterrows():
                pdf.cell(0, 10, f"{row['scope']}: {row['emissions_kgCO2e']:.2f} kgCO2e ({row['emissions_kgCO2e'] / total_emissions * 100:.1f}%)", 0, 1)
            category_data = data.groupby('category')['emissions_kgCO2e'].sum().reset_index()
            pdf.ln(5)
            pdf.cell(0, 10, self.t('top_categories', language), 0, 1)
            for _, row in category_data.nlargest(5, 'emissions_kgCO2e').iterrows():
                pdf.cell(0, 10, f"{row['category']}: {row['emissions_kgCO2e']:.2f} kgCO2e ({row['emissions_kgCO2e'] / total_emissions * 100:.1f}%)", 0, 1)
            pdf.ln(10)
            pdf.set_font("Arial", "B", 14)
            pdf.cell(0, 10, self.t('emissions_data', language), 0, 1)
            pdf.set_font("Arial", "B", 10)
            col_widths = [25, 25, 30, 30, 20, 15, 25, 30]
            headers = [self.t('date', language), self.t('scope', language), self.t('category', language), self.t('activity', language), self.t('quantity', language), self.t('unit', language), self.t('factor', language), self.t('emissions_kgco2e', language)]
            for i, header in enumerate(headers):
                pdf.cell(col_widths[i], 10, header, 1)
            pdf.ln()
            pdf.set_font("Arial", "", 8)
            for _, row in data.iterrows():
                pdf.cell(col_widths[0], 10, row['date'].strftime('%Y-%m-%d') if isinstance(row['date'], pd.Timestamp) else str(row['date']), 1)
                pdf.cell(col_widths[1], 10, str(row['scope']), 1)
                pdf.cell(col_widths[2], 10, str(row['category']), 1)
                pdf.cell(col_widths[3], 10, str(row['activity']), 1)
                pdf.cell(col_widths[4], 10, f"{row['quantity']:.2f}", 1)
                pdf.cell(col_widths[5], 10, str(row['unit']), 1)
                pdf.cell(col_widths[6], 10, f"{row['emission_factor']:.4f}", 1)
                pdf.cell(col_widths[7], 10, f"{row['emissions_kgCO2e']:.2f}", 1)
                pdf.ln()
            pdf.ln(10)
            pdf.set_font("Arial", "B", 14)
            pdf.cell(0, 10, self.t('reg_compliance', language), 0, 1)
            pdf.set_font("Arial", "", 12)
            pdf.cell(0, 10, self.t('cbam', language), 0, 1)
            pdf.cell(0, 10, self.t('gx_league', language), 0, 1)
            pdf.cell(0, 10, self.t('ets', language), 0, 1)
            pdf.ln(10)
            pdf.set_font("Arial", "B", 14)
            pdf.cell(0, 10, self.t('recommendations', language), 0, 1)
            pdf.set_font("Arial", "", 12)
            pdf.cell(0, 10, self.t('rec1', language), 0, 1)
            pdf.cell(0, 10, self.t('rec2', language), 0, 1)
            pdf.cell(0, 10, self.t('rec3', language), 0, 1)
            pdf.cell(0, 10, self.t('rec4', language), 0, 1)
            if file_path:
                pdf.output(file_path)
                return True, self.t('title', language) + ' generated successfully.'
            else:
                return pdf.output(dest='S').encode('latin1'), self.t('title', language) + ' generated successfully.'
        except Exception as e:
            return False, f"Error generating PDF report: {str(e)}"
    
    def create_scope_pie_chart(self, data):
        """

        Create pie chart of emissions by scope.

        

        Args:

            data (pandas.DataFrame): Emissions data

            

        Returns:

            plotly.graph_objects.Figure: Pie chart figure

        """
        scope_data = data.groupby('scope')['emissions_kgCO2e'].sum().reset_index()
        fig = px.pie(
            scope_data, 
            values='emissions_kgCO2e', 
            names='scope',
            color='scope',
            color_discrete_map={
                'Scope 1': '#4CAF50', 
                'Scope 2': '#2196F3', 
                'Scope 3': '#FFC107'
            },
            title='Emissions by Scope'
        )
        fig.update_layout(
            legend_title="Scope",
            font=dict(size=12),
            margin=dict(t=50, b=20, l=20, r=20)
        )
        return fig
    
    def create_category_bar_chart(self, data):
        """

        Create bar chart of emissions by category.

        

        Args:

            data (pandas.DataFrame): Emissions data

            

        Returns:

            plotly.graph_objects.Figure: Bar chart figure

        """
        category_data = data.groupby('category')['emissions_kgCO2e'].sum().reset_index()
        category_data = category_data.sort_values('emissions_kgCO2e', ascending=False)
        fig = px.bar(
            category_data, 
            x='category', 
            y='emissions_kgCO2e',
            color='category',
            title='Emissions by Category'
        )
        fig.update_layout(
            xaxis_title="Category",
            yaxis_title="Emissions (kgCO2e)",
            legend_title="Category",
            font=dict(size=12),
            margin=dict(t=50, b=100, l=50, r=20),
            xaxis_tickangle=-45
        )
        return fig
    
    def create_time_series_chart(self, data):
        """

        Create time series chart of emissions over time.

        

        Args:

            data (pandas.DataFrame): Emissions data

            

        Returns:

            plotly.graph_objects.Figure: Line chart figure

        """
        if 'date' not in data.columns or len(data) == 0:
            # Create empty figure if no data
            fig = go.Figure()
            fig.update_layout(
                title='Emissions Over Time',
                xaxis_title="Month",
                yaxis_title="Emissions (kgCO2e)",
                font=dict(size=12),
                margin=dict(t=50, b=50, l=50, r=20)
            )
            return fig
        
        # Group by month and scope
        time_data = data.copy()
        time_data['month'] = pd.to_datetime(time_data['date']).dt.strftime('%Y-%m')
        time_data = time_data.groupby(['month', 'scope'])['emissions_kgCO2e'].sum().reset_index()
        
        fig = px.line(
            time_data, 
            x='month', 
            y='emissions_kgCO2e',
            color='scope',
            markers=True,
            title='Emissions Over Time'
        )
        fig.update_layout(
            xaxis_title="Month",
            yaxis_title="Emissions (kgCO2e)",
            legend_title="Scope",
            font=dict(size=12),
            margin=dict(t=50, b=50, l=50, r=20)
        )
        return fig
    
    def create_activity_treemap(self, data):
        """

        Create treemap of emissions by scope, category, and activity.

        

        Args:

            data (pandas.DataFrame): Emissions data

            

        Returns:

            plotly.graph_objects.Figure: Treemap figure

        """
        fig = px.treemap(
            data,
            path=['scope', 'category', 'activity'],
            values='emissions_kgCO2e',
            color='scope',
            color_discrete_map={
                'Scope 1': '#4CAF50', 
                'Scope 2': '#2196F3', 
                'Scope 3': '#FFC107'
            },
            title='Emissions Breakdown'
        )
        fig.update_layout(
            margin=dict(t=50, b=20, l=20, r=20),
            font=dict(size=12)
        )
        return fig
    
    def create_monthly_comparison_chart(self, data):
        """

        Create bar chart comparing emissions by month.

        

        Args:

            data (pandas.DataFrame): Emissions data

            

        Returns:

            plotly.graph_objects.Figure: Bar chart figure

        """
        if 'date' not in data.columns or len(data) == 0:
            # Create empty figure if no data
            fig = go.Figure()
            fig.update_layout(
                title='Monthly Emissions Comparison',
                xaxis_title="Month",
                yaxis_title="Emissions (kgCO2e)",
                font=dict(size=12),
                margin=dict(t=50, b=50, l=50, r=20)
            )
            return fig
        
        # Group by month
        monthly_data = data.copy()
        monthly_data['month'] = pd.to_datetime(monthly_data['date']).dt.strftime('%Y-%m')
        monthly_data = monthly_data.groupby('month')['emissions_kgCO2e'].sum().reset_index()
        
        fig = px.bar(
            monthly_data,
            x='month',
            y='emissions_kgCO2e',
            title='Monthly Emissions Comparison'
        )
        fig.update_layout(
            xaxis_title="Month",
            yaxis_title="Emissions (kgCO2e)",
            font=dict(size=12),
            margin=dict(t=50, b=50, l=50, r=20)
        )
        return fig