updated

app-versions/v6.py

@@ -0,0 +1,284 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import requests  # Import the requests library
+
+
+# Path to the data file relative to the app.py file
+DATA_PATH = "https://huggingface.co/spaces/danielrosehill/Monetised-GHG-Emissions-Calculator/raw/main/calculator-app/data.csv"
+INSTRUCTIONS_PATH = "https://huggingface.co/spaces/danielrosehill/Monetised-GHG-Emissions-Calculator/raw/main/calculator-app/instructions.md"
+
+GITHUB_LINK = "https://github.com/danielrosehill/Emissions-Monetisation-Calculator"
+
+
+def load_data():
+    try:
+        df = pd.read_csv(DATA_PATH)
+        return df
+    except FileNotFoundError:
+        st.error(
+            f"Error: Could not find the data file at {DATA_PATH}. Please ensure the file exists."
+        )
+        return None
+
+
+def load_instructions():
+    try:
+        response = requests.get(INSTRUCTIONS_PATH)  # Fetch the content of the URL
+        response.raise_for_status()  # Raise an error for bad status codes
+        return response.text  # return the text of the response
+    except requests.exceptions.RequestException as e:
+        return f"Error: Could not fetch instructions file: {e}"
+
+
+def format_currency(value, display_unit):
+    if display_unit == "Millions":
+        formatted_value = f"${value / 1_000_000:.2f} MN"
+    elif display_unit == "Billions":
+        formatted_value = f"${value / 1_000_000_000:.2f} BN"
+    return formatted_value
+
+
+def main():
+    st.set_page_config(layout="wide")
+    st.markdown("""
+    <style>
+    [data-testid="stHorizontalBlock"] {
+        border: 1px solid #e6e6e6;
+        border-radius: 5px;
+        padding: 1em;
+        margin-bottom: 1em;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    st.title("GHG Emissions Monetization Calculator")
+    st.markdown(
+        "This tool explores the potential financial implications of proposed greenhouse gas emissions costs. It accompanies a repository on Github and Hugging Face that aggregates proposals for the social cost of carbon."
+    )
+    st.markdown(
+        "The social cost of carbon represents the economic damages associated with emitting one additional ton of carbon dioxide into the atmosphere."
+    )
+    st.markdown(
+        "Detailed notes and instructions about the use of this calculator can be found in the Instructions tab."
+    )
+    st.markdown(
+        "This calculator was developed by Daniel Rosehill in December 2024 (danielrosehill.com)."
+    )
+
+
+    # Load the data and instructions
+    df = load_data()
+    instructions = load_instructions()
+    if df is None:
+        return  # Don't proceed if data can't be loaded
+
+    # Tabs for calculator, instructions and SCC proposals
+    tabs = st.tabs(["Calculator", "Instructions", "SCC Chart", "SCC Details", "Data"])
+
+    with tabs[0]:  # Calculator tab
+        with st.container():
+            st.markdown("### Input your emissions and proposal of interest")
+            left, right = st.columns(2)
+
+            with left:
+                st.subheader("Input Values")
+                st.markdown("Enter your company's greenhouse gas emissions:")
+                scope1_emissions = st.number_input("Scope 1 Emissions", value=0.0)
+                st.markdown("*(Direct emissions from owned or controlled sources)*")
+                scope2_emissions = st.number_input("Scope 2 Emissions", value=0.0)
+                st.markdown("*(Indirect emissions from the generation of purchased energy)*")
+                scope3_emissions = st.number_input("Scope 3 Emissions", value=0.0)
+                st.markdown("*(All other indirect emissions that occur in a company's value chain)*")
+                unit_of_reporting = st.selectbox("Unit of Reporting", ["TCO2E", "MTCO2E"])
+                proposal_names = df['proposal_with_date'].tolist()
+                selected_proposal = st.selectbox("Social cost of carbon proposal", proposal_names)
+                calculate_button = st.button("Calculate Monetized Emissions")
+                
+            with right:
+                st.subheader("Calculated Values")
+                if calculate_button:
+                # Calculated emissions
+                    scope1_2_emissions = scope1_emissions + scope2_emissions
+                    all_scopes_emissions = (
+                        scope1_emissions + scope2_emissions + scope3_emissions
+                    )
+                    st.markdown(
+                        f"Scope 1 and 2 Emissions: {scope1_2_emissions:.2f} {unit_of_reporting}"
+                    )
+                    st.markdown(
+                        f"All Scopes Emissions: {all_scopes_emissions:.2f} {unit_of_reporting}"
+                    )
+
+                # Find the value in USD per ton
+                    selected_row = df[df['proposal_with_date'] == selected_proposal].iloc[0]
+                    multiplier = selected_row['usd_proposed_value']
+
+                    st.subheader("Monetized Emissions")
+                    display_unit = st.radio("Display units", ["Millions", "Billions"])
+
+                    if unit_of_reporting == "MTCO2E":
+                        scope1_emissions = scope1_emissions * 1_000_000
+                        scope2_emissions = scope2_emissions * 1_000_000
+                        scope3_emissions = scope3_emissions * 1_000_000
+                        all_scopes_emissions = all_scopes_emissions * 1_000_000
+
+                # Monetization calculations
+                    monetized_scope1 = scope1_emissions * multiplier
+                    monetized_scope2 = scope2_emissions * multiplier
+                    monetized_scope3 = scope3_emissions * multiplier
+                    monetized_all_scopes = all_scopes_emissions * multiplier
+
+                    st.markdown(f"Scope 1: {format_currency(monetized_scope1, display_unit)}")
+                    st.markdown(f"Scope 2: {format_currency(monetized_scope2, display_unit)}")
+                    st.markdown(f"Scope 3: {format_currency(monetized_scope3, display_unit)}")
+                    st.markdown(
+                        f"All Scopes: {format_currency(monetized_all_scopes, display_unit)}"
+                    )
+
+    with tabs[1]:  # Instructions tab
+        st.markdown(instructions)
+
+    with tabs[2]:  # SCC Chart tab
+        st.subheader("Social Cost of Carbon Proposals")
+        
+        # Convert the 'date' column to datetime objects for proper sorting
+        df['date'] = pd.to_datetime(df['date'])
+        # Sort by value
+        df_sorted = df.sort_values(by='usd_proposed_value', ascending=True)
+
+        # Create horizontal bar chart
+        bar_fig = px.bar(
+            df_sorted,
+            x="usd_proposed_value",
+            y="proposal_with_date",
+            title="Social Cost of Carbon Proposals",
+            labels={
+                "usd_proposed_value": "USD Proposed Value",
+                "proposal_with_date": "Proposal",
+            },
+            orientation='h',  # Set orientation to horizontal
+             hover_data={
+                "usd_proposed_value": True,
+             }
+        )
+        bar_fig.update_traces(texttemplate='%{x:.2f}', textposition='outside')
+        st.plotly_chart(bar_fig)
+
+        # Organization filter
+        organizations = df['organization_name'].unique().tolist()
+        selected_org = st.selectbox("Filter by organization", ["All"] + organizations)
+
+        # Filter data
+        if selected_org != "All":
+            filtered_df = df[df['organization_name'] == selected_org]
+        else:
+            filtered_df = df
+
+        # Sort the data by date before creating the line chart
+        filtered_df = filtered_df.sort_values(by='date')
+
+        # Create line chart with data points and custom hover text
+        show_points = st.checkbox("Display Data Points", value=True)
+        line_fig = px.line(
+            filtered_df,
+            x="date",
+            y="usd_proposed_value",
+            title="Trend of Social Cost of Carbon Proposals Over Time",
+            labels={
+                "usd_proposed_value": "USD Proposed Value",
+                "date": "Date",
+            },
+            hover_data={
+                "usd_proposed_value": True,
+                "proposal_with_date": True,
+                "organization_name": True,
+             },
+        )
+
+        line_fig.update_traces(
+            mode="lines+markers" if show_points else "lines",
+            hovertemplate="USD Value: %{y:.2f}<br>Proposal: %{customdata[0]}<br>Organization: %{customdata[1]}",
+            text=filtered_df["proposal_with_date"],
+            marker=dict(size=6),
+            customdata=filtered_df[["proposal_with_date","organization_name"]]
+        )
+
+        st.plotly_chart(line_fig)
+
+
+    with tabs[3]:  # SCC Details tab
+        st.subheader("Social Cost of Carbon Proposal Details")
+        proposal_names = df["proposal_with_date"].tolist()
+        selected_proposal = st.selectbox("Select a proposal", proposal_names)
+
+        if selected_proposal:
+            selected_row = df[df["proposal_with_date"] == selected_proposal].iloc[0]
+
+            # Prepare data for the table
+            col1, col2 = st.columns(2)
+            with col1:
+                st.markdown(" **Organization Name:**")
+                st.markdown(" **Organization Description:**")
+                st.markdown(" **Date:**")
+                st.markdown(" **Country:**")
+                st.markdown(" **ISO3:**")
+                st.markdown(" **ISO2:**")
+                st.markdown(" **HDI Value:**")
+                st.markdown(" **HDI Category:**")
+                st.markdown(" **Details:**")
+            with col2:
+                st.markdown(selected_row["organization_name"])
+                st.markdown(selected_row["organization_description"])
+                st.markdown(selected_row["date"].strftime('%Y-%m-%d'))
+                st.markdown(selected_row["country"])
+                st.markdown(selected_row["iso3"])
+                st.markdown(selected_row["iso2"])
+                st.markdown(str(selected_row["hdi_value"]))
+                st.markdown(selected_row["hdi_category"])
+                st.markdown(selected_row["details"])
+
+            col3, col4 = st.columns(2)
+            with col3:
+                 st.markdown(" **Original Proposed Value:**")
+                 st.markdown(" **Average Value:**")
+                 st.markdown(" **USD Proposed Value:**")
+                 st.markdown(" **USD Proposed Value (Empty CO2e):**")
+                 st.markdown(" **USD Conversion Date:**")
+                 st.markdown(" **Value Units:**")
+                 st.markdown(" **Environmental Units:**")
+                 st.markdown(" **Methodologies Used:**")
+                 st.markdown(" **Calculation Scope:**")
+                 st.markdown(" **Is Range:**")
+            with col4:
+                st.markdown(f"{selected_row['original_proposed_value']} {selected_row['original_currency_name']}")
+                st.markdown(str(selected_row["average_value"]))
+                st.markdown(str(selected_row["usd_proposed_value"]))
+                st.markdown(str(selected_row['use_proposed_value_mtco2e']))
+                st.markdown(str(selected_row["usd_conversion_date"]))
+                st.markdown(selected_row["value_units"])
+                st.markdown(selected_row["environmental_units"])
+                st.markdown(selected_row["methodologies_used"])
+                st.markdown(selected_row["calculation_scope"])
+                st.markdown(str(selected_row["is_range"]))
+
+    with tabs[4]: # Data Tab
+        st.subheader("Data")
+        st.dataframe(df)
+        st.markdown("#### Download Data")
+        
+        def convert_df(df):
+            return df.to_csv().encode('utf-8')
+        
+        csv = convert_df(df)
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='scc_data.csv',
+            mime='text/csv',
+        )
+    
+    st.markdown(f'<a href="{GITHUB_LINK}"><img src="https://img.shields.io/badge/View%20on%20GitHub-blue?logo=github"></a>', unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

calculator-app/app.py

@@ -1,6 +1,8 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
+import requests  # Import the requests library
+
 
 # Path to the data file relative to the app.py file
 DATA_PATH = "https://huggingface.co/spaces/danielrosehill/Monetised-GHG-Emissions-Calculator/raw/main/calculator-app/data.csv"
@@ -22,10 +24,11 @@ def load_data():
 
 def load_instructions():
     try:
-        with open(INSTRUCTIONS_PATH, "r") as f:
-            return f.read()
-    except FileNotFoundError:
-        return "Error: Instructions file not found."
+        response = requests.get(INSTRUCTIONS_PATH)  # Fetch the content of the URL
+        response.raise_for_status()  # Raise an error for bad status codes
+        return response.text  # return the text of the response
+    except requests.exceptions.RequestException as e:
+        return f"Error: Could not fetch instructions file: {e}"
 
 
 def format_currency(value, display_unit):