Initial commit

README.md

@@ -11,4 +11,25 @@ license: mit
 short_description: This project is a Carbon Footprint Calculator and Visualizer
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Carbon Footprint Visualizer 🌍
+
+## Overview
+
+A Python-based web application that helps organizations calculate, visualize, and analyze their carbon footprint through an intuitive Gradio interface.
+
+## 🚀 Features
+
+- **Comprehensive Carbon Emission Calculation**
+  - Analyze emissions across multiple dimensions:
+    - Electricity consumption
+    - Gas usage
+    - Transportation
+    - Waste management
+- **Interactive Visualization**
+  - Generate detailed pie and bar charts
+  - Visualize carbon impact across different categories
+- **Personalized Reporting**
+  - Export customized PDF reports
+  - Downloadable carbon footprint analysis
+- **Company Data Visualization**
+  - See all companies' emissions ranges

reports/historic_data.csv

@@ -0,0 +1,2 @@
+Name,Energy Usage,Waste Generated,Business Travel
+Company 1,1024699.9871999999,49929.88,44083.78333333334

requirements.txt

@@ -0,0 +1,8 @@
+plotly 
+"ipywidgets>=7.6"
+"jupyterlab>=3"
+notebook
+gradio
+kaleido
+chart_studio
+pandas

src/app.py

@@ -0,0 +1,277 @@
+import base64
+
+from csv import DictWriter
+import os.path as os_path
+
+from plotly.graph_objects import Figure
+import gradio as gr
+from pandas import read_csv, DataFrame
+import emission_calculator.calculator as ec
+
+DATA_PATH = "./reports/historic_data.csv"
+
+
+def compute_history() -> Figure:
+    if not os_path.exists(DATA_PATH):
+        f = open(DATA_PATH, "xt")
+        f.write("Name,Energy Usage,Waste Generated,Business Travel")
+        f.close()
+        df = DataFrame.from_dict({})
+    else:
+        df = read_csv(DATA_PATH)
+    return ec.draw_historic_figure(df)
+
+
+def validate_input(
+    company_name: str,
+    avg_electric_bill: float,
+    avg_gas_bill: float,
+    avg_transport_cost: float,
+    monthly_waste_generated: float,
+    recycled_waste_percent: float,
+    annual_travel_kms: float,
+    fuel_efficiency: float,
+) -> None:
+    """
+    Comprehensive validation for input parameters with non-zero requirements
+    """
+    # Company Name Validation
+    if not company_name or company_name.isspace():
+        raise gr.Error("Company name cannot be empty or just whitespace!")
+
+    if len(company_name) > 100:
+        raise gr.Error("Company name is too long (maximum 100 characters)!")
+
+    # Non-Zero Input Validation
+    non_zero_fields = [
+        ("Electricity Bill", avg_electric_bill),
+        ("Gas Bill", avg_gas_bill),
+        ("Transport Cost", avg_transport_cost),
+        ("Monthly Waste", monthly_waste_generated),
+        ("Annual Travel Distance", annual_travel_kms),
+        ("Fuel Efficiency", fuel_efficiency),
+    ]
+
+    for name, value in non_zero_fields:
+        try:
+            float_val = float(value)
+        except (TypeError, ValueError):
+            raise gr.Error(f"{name} must be a valid number!")
+
+        if float_val <= 0:
+            raise gr.Error(f"{name} must be a positive number greater than zero!")
+
+        # Additional realistic range checks
+        if name == "Electricity Bill" and float_val > 10000:
+            raise gr.Error(
+                "Electricity bill seems unrealistically high. Please check the amount!"
+            )
+
+        if name == "Monthly Waste" and float_val > 1000:
+            raise gr.Error(
+                "Monthly waste generation seems extremely high. Please verify!"
+            )
+
+    # Percentage-specific validation
+    try:
+        recycled_percent = float(recycled_waste_percent)
+    except (TypeError, ValueError):
+        raise gr.Error("Recycled waste percentage must be a valid number!")
+
+    if recycled_percent < 0 or recycled_percent > 100:
+        raise gr.Error("Recycled waste percentage must be between 0 and 100!")
+
+
+def compute(
+    company_name: str,
+    avg_electric_bill: float,
+    avg_gas_bill: float,
+    avg_transport_cost: float,
+    monthly_waste_generated: float,
+    recycled_waste_percent: float,
+    annual_travel_kms: float,
+    fuel_efficiency: float,
+) -> tuple[str, gr.Button]:
+    """
+    Compute carbon footprint with comprehensive input validation
+    Returns:
+        result (tuple)
+        of summary HTML (str)
+        and download_report button (Button)
+    """
+    # Validate inputs first
+    validate_input(
+        company_name,
+        avg_electric_bill,
+        avg_gas_bill,
+        avg_transport_cost,
+        monthly_waste_generated,
+        recycled_waste_percent,
+        annual_travel_kms,
+        fuel_efficiency,
+    )
+
+    # Proceed with calculation if validation passes
+    df = ec.make_dataframe(
+        company_name=company_name,
+        avg_electric_bill=avg_electric_bill,
+        avg_gas_bill=avg_gas_bill,
+        avg_transport_bill=avg_transport_cost,
+        monthly_waste_generated=monthly_waste_generated,
+        recycled_waste_percent=recycled_waste_percent,
+        annual_travel_kms=annual_travel_kms,
+        fuel_efficiency=fuel_efficiency,
+    )
+
+    try:
+        df_dump = ec.dataframe_to_dict(df=df)
+        with open(DATA_PATH, mode="a") as f:
+            w = DictWriter(f, fieldnames=df_dump.keys())
+            if not os_path.exists(DATA_PATH):
+                w.writeheader()
+            w.writerow(df_dump)
+            print("Saving is successful")
+    except Exception as e:
+        print(e)
+
+    plot = ec.draw_report_figure(df)
+
+    # Convert plot to base64 image
+    img_data = base64.b64encode(
+        plot.to_image(width=1400, height=800, format="png")
+    ).decode("utf-8")
+
+    # convert plot to pdf for downloading report
+    file_path = f"./reports/{company_name.lower().replace(' ', '_')[:10]}_report.pdf"
+    plot.write_image(file_path, width=1400, height=800)
+
+    # Generate a summary HTML with embedded image
+    summary = f"""
+    <div style="max-width: 1400px; margin: 0 auto; font-family: Arial, sans-serif;">
+        <h3 style="color: #ffffff;"> Carbon Footprint Summary for {company_name} </h3>
+        <ul style="color: #666;">
+            <li>🏭 <strong>Total Carbon Impact</strong>: Calculated based on your inputs</li>
+            <li>💡 <strong>Energy Consumption</strong>: €{avg_electric_bill + avg_gas_bill:.2f}</li>
+            <li>🚗 <strong>Transportation Emissions</strong>: {annual_travel_kms} km</li>
+            <li>🗑️ <strong>Waste Management</strong>: {monthly_waste_generated} kg (Recycled: {recycled_waste_percent}%)</li>
+        </ul>
+        <img src="data:image/png;base64,{img_data}" style="max-width: 100%; height: auto;" alt="Carbon Footprint Report"/>
+    </div>
+    """
+    download_button = gr.DownloadButton(
+        "Download Report", variant="secondary", visible=True, value=file_path
+    )
+    return summary, download_button
+
+
+def create_carbon_footprint_app() -> gr.Blocks:
+    with gr.Blocks(theme="soft") as demo:
+        with gr.Tab("Calculator 📱"):
+            gr.Markdown("# 🌍 Carbon Footprint Calculator")
+
+            # Hidden image download button
+            download_button = gr.File(
+                label="Download Carbon Footprint Report", type="binary", visible=False
+            )
+
+            with gr.Column():
+                with gr.Column(scale=2):
+                    with gr.Column(variant="compact"):
+                        company_name = gr.Textbox(
+                            label="Company Name",
+                            placeholder="Enter your company name",
+                            info="Required: Full legal company name",
+                        )
+                    with gr.Row():
+                        with gr.Column(variant="compact"):
+                            avg_electric_bill = gr.Number(
+                                value=1.0,
+                                label="Average Electricity Bill (€)",
+                                minimum=0.01,
+                                info="Monthly electricity expenses",
+                            )
+                            avg_gas_bill = gr.Number(
+                                value=1.0,
+                                label="Average Gas Bill (€)",
+                                minimum=0.01,
+                                info="Monthly natural gas expenses",
+                            )
+                            avg_transport_cost = gr.Number(
+                                value=1.0,
+                                label="Average Transport Cost (€)",
+                                info="Monthly Fuel bill for transport",
+                            )
+
+                        with gr.Column(variant="compact"):
+                            annual_travel_kms = gr.Number(
+                                value=1.0,
+                                label="Annual Business Travel (km)",
+                                minimum=0.01,
+                                info="Total kilometers traveled by employees",
+                            )
+                            fuel_efficiency = gr.Number(
+                                value=1.0,
+                                label="Vehicle Fuel Efficiency (L/100 km)",
+                                minimum=0.01,
+                                info="Average fleet fuel consumption",
+                            )
+
+                        with gr.Column(variant="compact"):
+                            monthly_waste_generated = gr.Number(
+                                value=1.0,
+                                label="Monthly Waste Generated (kg)",
+                                minimum=0.01,
+                                info="Total waste produced monthly",
+                            )
+                            recycled_waste_percent = gr.Number(
+                                value=0.0,
+                                label="Recycled Waste (%)",
+                                minimum=0.0,
+                                maximum=100.0,
+                                info="Percentage of waste recycled",
+                            )
+
+                with gr.Column(scale=1):
+                    output_plot = gr.HTML(label="Carbon Footprint Report")
+                    # Create a row for buttons
+                    with gr.Row():
+                        submit_button = gr.Button("Generate Report", variant="primary")
+                        download_button = gr.DownloadButton(
+                            "Download Report", variant="secondary", visible=False
+                        )
+
+            submit_button.click(
+                fn=compute,
+                inputs=[
+                    company_name,
+                    avg_electric_bill,
+                    avg_gas_bill,
+                    avg_transport_cost,
+                    monthly_waste_generated,
+                    recycled_waste_percent,
+                    annual_travel_kms,
+                    fuel_efficiency,
+                ],
+                outputs=[output_plot, download_button],
+            )
+
+        with gr.Tab("History 📊") as historic_tab:
+            gr.Markdown("# Historic Company Data")
+
+            plot = gr.Plot(value=compute_history(), label="Historic Data")
+            refresh = gr.Button("Refresh", variant="secondary")
+            refresh.click(
+                fn=compute_history,
+                outputs=[plot],
+            )
+            # auto-reload
+            historic_tab.select(
+                fn=compute_history,
+                outputs=[plot],
+            )
+
+    return demo
+
+
+if __name__ == "__main__":
+    create_carbon_footprint_app().launch()

src/emission_calculator/calculator.py

@@ -0,0 +1,257 @@
+from pandas import DataFrame
+
+from plotly.subplots import make_subplots
+from plotly.graph_objects import Figure, Pie, Bar, Scatter
+
+
+def draw_report_figure(df: DataFrame) -> Figure:
+    figure_specs = [
+        [{"type": "xy"}, {"type": "xy"}],
+        [{"type": "domain"}, None],
+    ]
+
+    fig = make_subplots(
+        rows=2,
+        cols=2,
+        specs=figure_specs,
+        subplot_titles=(
+            "Carbon Emission by Category",
+            "Cumulative Emission %",
+            "Emission Distribution",
+        ),
+    )
+
+    # Pie chart settings
+    pie_pull = [0.15 if x == min(df["Value"]) else 0.0 for x in df["Value"]]
+    fig.add_trace(
+        Pie(
+            values=df["Value"],
+            labels=df["Category"],
+            hole=0.3,
+            pull=pie_pull,
+            name="Emission Distribution",
+            marker={"colors": ["#6DA34D", "#81C3D7", "#FFC857"]},
+        ),
+        row=2,
+        col=1,
+    )
+
+    # Bar chart for emissions by category
+    fig.add_trace(
+        Bar(
+            x=df["Category"],
+            y=df["Value"],
+            name="Carbon Emission (kgCO2)",
+            marker_color=["#6DA34D", "#81C3D7", "#FFC857"],
+        ),
+        row=1,
+        col=1,
+    )
+
+    # Annotation for highest emission
+    fig.add_annotation(
+        x=df["Category"][df["Value"].idxmax()],
+        y=df["Value"].max(),
+        text="Highest Emission",
+        showarrow=True,
+        arrowhead=1,
+        ax=0,
+        ay=-40,
+        row=1,
+        col=1,
+    )
+
+    # Cumulative line chart
+    cumulative_percentage = (df["Value"].cumsum() / df["Value"].sum()) * 100
+    fig.add_trace(
+        Scatter(
+            x=df["Category"],
+            y=cumulative_percentage,
+            name="Cumulative %",
+            mode="lines+markers",
+            line=dict(color="#333333", dash="dash"),
+        ),
+        row=1,
+        col=2,
+    )
+
+    # Update layout for axes and overall layout
+    fig.update_layout(
+        title_text=f"Carbon Footprint of {df['Name'][0]}",
+        plot_bgcolor="white",
+        legend_title_text="Breakdown",
+        xaxis_title="Emission Category",
+        yaxis_title="Carbon Emission (kgCO2)",
+        yaxis=dict(
+            linecolor="black",
+            showline=True,
+            ticks="outside",
+            mirror=True,
+            gridcolor="lightgrey",
+        ),
+        yaxis2=dict(
+            title="Cumulative Percentage",
+            side="right",
+            showgrid=False,
+        ),
+        legend=dict(
+            x=1,  # Horizontal position (1 for right)
+            y=0,  # Vertical position (0 for bottom)
+            xanchor="right",
+            yanchor="bottom",
+            orientation="h",  # Horizontal layout for compactness
+        ),
+    )
+
+    fig.update_xaxes(
+        linecolor="black",
+        ticks="outside",
+        showline=True,
+        mirror=True,
+    )
+    fig.update_yaxes(
+        linecolor="black",
+        showline=True,
+        ticks="outside",
+        mirror=True,
+        gridcolor="lightgrey",
+    )
+
+    return fig
+
+
+def draw_historic_figure(df: DataFrame) -> Figure:
+    # Create subplots with 2 rows and 2 columns
+    fig = make_subplots(
+        rows=2,
+        cols=2,
+        subplot_titles=(
+            "Energy Usage by Company",
+            "Waste Generated by Company",
+            "Business Travel by Company",
+            "Total Carbon Footprint by Company",
+        ),
+    )
+
+    # Add gradient-filled area traces for each metric
+    fig.add_trace(
+        Scatter(
+            x=df["Name"],
+            y=df["Energy Usage"],
+            mode="lines",
+            fill="tozeroy",
+            line=dict(color="blue"),
+            fillcolor="rgba(31, 119, 180, 0.5)",  # Gradient fill for blue
+            name="Energy Usage",
+        ),
+        row=1,
+        col=1,
+    )
+
+    fig.add_trace(
+        Scatter(
+            x=df["Name"],
+            y=df["Waste Generated"],
+            mode="lines",
+            fill="tozeroy",
+            line=dict(color="orange"),
+            fillcolor="rgba(255, 127, 14, 0.5)",  # Gradient fill for orange
+            name="Waste Generated",
+        ),
+        row=1,
+        col=2,
+    )
+
+    fig.add_trace(
+        Scatter(
+            x=df["Name"],
+            y=df["Business Travel"],
+            mode="lines",
+            fill="tozeroy",
+            line=dict(color="green"),
+            fillcolor="rgba(44, 160, 44, 0.5)",  # Gradient fill for green
+            name="Business Travel",
+        ),
+        row=2,
+        col=1,
+    )
+
+    # Calculate each company's total carbon footprint as the sum of the three metrics
+    df["Carbon Footprint"] = (
+        df["Energy Usage"] + df["Waste Generated"] + df["Business Travel"]
+    )
+
+    # Add a line trace for the total sum of each metric
+    fig.add_trace(
+        Scatter(
+            x=df["Name"],
+            y=df["Carbon Footprint"],
+            mode="lines+markers",
+            line=dict(color="black", dash="dash"),
+            name="Total Carbon Footprint",
+        ),
+        row=2,
+        col=2,
+    )
+
+    # Update layout for titles, legends, and aesthetics
+    fig.update_layout(
+        title="Company Metrics with Total Carbon Footprint",
+        barmode="group",  # Group bars by category
+        template="plotly_white",
+        showlegend=True,
+        height=600,
+        width=1000,
+        legend=dict(x=1.05, y=1),  # Adjust legend position outside plot for clarity
+    )
+
+    # Add axis labels to each subplot
+    fig.update_xaxes(title_text="Company", row=1, col=1)
+    fig.update_yaxes(title_text="Energy Usage", row=1, col=1)
+
+    fig.update_xaxes(title_text="Company", row=1, col=2)
+    fig.update_yaxes(title_text="Waste Generated", row=1, col=2)
+
+    fig.update_xaxes(title_text="Company", row=2, col=1)
+    fig.update_yaxes(title_text="Business Travel", row=2, col=1)
+
+    fig.update_xaxes(title_text="Company", row=2, col=2)
+    fig.update_yaxes(title_text="Carbon Footprint (total)", row=2, col=2)
+
+    return fig
+
+
+def make_dataframe(
+    company_name: str,
+    avg_electric_bill: float,
+    avg_gas_bill: float,
+    avg_transport_bill: float,
+    monthly_waste_generated: float,
+    recycled_waste_percent: float,
+    annual_travel_kms: float,
+    fuel_efficiency: float,
+) -> DataFrame:
+    energy_usage = (
+        (avg_electric_bill * 12 * 5e-4)
+        + (avg_gas_bill * 12 * 5.3e-3)
+        + (avg_transport_bill * 12 * 2.32)
+    )
+    waste_generated = monthly_waste_generated * 12 * 0.57 - recycled_waste_percent
+    business_travel = annual_travel_kms * 1 / fuel_efficiency * 2.31
+
+    return DataFrame(
+        {
+            "Name": company_name,
+            "Category": ["Energy Usage", "Waste Generated", "Business Travel"],
+            "Value": [energy_usage, waste_generated, business_travel],
+        }
+    )
+
+
+def dataframe_to_dict(df: DataFrame) -> dict:
+    return {
+        "Name": df["Name"][0],
+        "Energy Usage": df["Value"][0],
+        "Waste Generated": df["Value"][1],
+        "Business Travel": df["Value"][2],
+    }