Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime
import io

st.set_page_config(layout="wide", page_title="Carbon Credits Dashboard")

st.title("Carbon Credits Dashboard")
st.markdown("Drop your CSV file below to visualize carbon credit transaction data.")

# CSS to improve the appearance of the file uploader
st.markdown("""
<style>
.uploadedFile {
    border: 2px dashed #1f77b4;
    border-radius: 10px;
    padding: 30px;
    text-align: center;
    margin-bottom: 20px;
}
.stAlert {
    background-color: #f0f8ff;
}
</style>
""", unsafe_allow_html=True)

# Sample data for display when no file is uploaded
sample_data = pd.DataFrame({
    'transaction_id': ['TR-12493', 'TR-12494', 'TR-12495'],
    'date': ['2024-01-15', '2024-01-17', '2024-01-21'],
    'project_type': ['Reforestation', 'Renewable Energy', 'Methane Capture'],
    'project_id': ['PRJ-4587', 'PRJ-2839', 'PRJ-6721'],
    'seller': ['Carbon Forest Partners', 'SunPower Solutions', 'AgriTech Emissions'],
    'buyer': ['GreenInvest Group', 'Eco Capital Fund', 'Climate Forward'],
    'quantity': [5000, 3200, 2800],
    'price_per_ton': [12.50, 9.75, 14.25],
    'total_value': [62500.00, 31200.00, 39900.00],
    'standard': ['Gold Standard', 'VCS', 'CAR'],
    'vintage_year': [2023, 2023, 2023],
    'location': ['Brazil', 'India', 'United States']
})

# File uploader
uploaded_file = st.file_uploader("Upload your CSV file", type=['csv'], 
                                help="Upload a CSV file with carbon credit transaction data")

# Initialize df with sample data
df = sample_data

# If a file is uploaded, read it
if uploaded_file is not None:
    try:
        # Read the file
        df = pd.read_csv(uploaded_file)
        st.success("File successfully uploaded and processed!")
        
        # Convert date to datetime
        df['date'] = pd.to_datetime(df['date'])
        
        # Sort by date
        df = df.sort_values('date')
    except Exception as e:
        st.error(f"Error: {e}")
        st.info("Using sample data instead.")

# If we're using sample data and not a real upload
elif uploaded_file is None:
    st.info("No file uploaded. Displaying sample data for preview.")
    # Convert date to datetime for sample data too
    df['date'] = pd.to_datetime(df['date'])
    df = df.sort_values('date')

# Dashboard content
def create_dashboard(data):
    # Data overview section
    st.header("Data Overview")
    st.write(f"Total Transactions: {len(data)}")
    
    # Create tabs for different views
    tabs = st.tabs(["Transactions Table", "Trends Analysis", "Project Breakdown", "Geographic Distribution"])
    
    # Transactions Table tab
    with tabs[0]:
        st.subheader("Transactions Table")
        
        # Filters for the table
        col1, col2, col3 = st.columns(3)
        with col1:
            project_types = ['All'] + sorted(data['project_type'].unique().tolist())
            selected_project_type = st.selectbox("Filter by Project Type", project_types)
        
        with col2:
            standards = ['All'] + sorted(data['standard'].unique().tolist())
            selected_standard = st.selectbox("Filter by Standard", standards)
            
        with col3:
            locations = ['All'] + sorted(data['location'].unique().tolist())
            selected_location = st.selectbox("Filter by Location", locations)
        
        # Apply filters
        filtered_data = data.copy()
        if selected_project_type != 'All':
            filtered_data = filtered_data[filtered_data['project_type'] == selected_project_type]
        if selected_standard != 'All':
            filtered_data = filtered_data[filtered_data['standard'] == selected_standard]
        if selected_location != 'All':
            filtered_data = filtered_data[filtered_data['location'] == selected_location]
        
        # Display table
        st.dataframe(filtered_data, use_container_width=True)
        
        # Export filtered data
        if st.button("Export Filtered Data as CSV"):
            csv = filtered_data.to_csv(index=False)
            st.download_button(
                label="Download CSV",
                data=csv,
                file_name="carbon_credits_filtered.csv",
                mime="text/csv",
            )
    
    # Trends Analysis tab
    with tabs[1]:
        st.subheader("Transaction Trends")
        
        # Time period selector
        time_periods = ["Daily", "Weekly", "Monthly", "Quarterly", "Yearly"]
        selected_period = st.selectbox("Select Time Period", time_periods)
        
        # Prepare time-based data
        if selected_period == "Daily":
            df_time = data.copy()
            df_time['period'] = df_time['date']
        elif selected_period == "Weekly":
            df_time = data.copy()
            df_time['period'] = df_time['date'].dt.isocalendar().week.astype(str) + '-' + df_time['date'].dt.year.astype(str)
        elif selected_period == "Monthly":
            df_time = data.copy()
            df_time['period'] = df_time['date'].dt.strftime('%Y-%m')
        elif selected_period == "Quarterly":
            df_time = data.copy()
            df_time['period'] = 'Q' + (df_time['date'].dt.month // 3 + 1).astype(str) + '-' + df_time['date'].dt.year.astype(str)
        else:  # Yearly
            df_time = data.copy()
            df_time['period'] = df_time['date'].dt.year
        
        # Aggregate data by time period
        time_agg = df_time.groupby('period').agg({
            'transaction_id': 'count',
            'quantity': 'sum',
            'total_value': 'sum'
        }).reset_index()
        
        # Create metrics
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("Total Transactions", f"{time_agg['transaction_id'].sum():,}")
        with col2:
            st.metric("Total Quantity (tons)", f"{time_agg['quantity'].sum():,}")
        with col3:
            st.metric("Total Value ($)", f"${time_agg['total_value'].sum():,.2f}")
        
        # Create visualizations
        
        # Transaction volume over time
        fig1 = px.line(time_agg, x='period', y='transaction_id', 
                      title=f"Transaction Count by {selected_period} Period",
                      labels={'period': 'Time Period', 'transaction_id': 'Number of Transactions'})
        st.plotly_chart(fig1, use_container_width=True)
        
        # Create two columns for the charts
        col1, col2 = st.columns(2)
        
        # Quantity over time
        with col1:
            fig2 = px.bar(time_agg, x='period', y='quantity', 
                          title=f"Carbon Credits Quantity by {selected_period} Period",
                          labels={'period': 'Time Period', 'quantity': 'Quantity (tons)'})
            st.plotly_chart(fig2, use_container_width=True)
        
        # Value over time
        with col2:
            fig3 = px.bar(time_agg, x='period', y='total_value', 
                          title=f"Transaction Value by {selected_period} Period",
                          labels={'period': 'Time Period', 'total_value': 'Value ($)'})
            st.plotly_chart(fig3, use_container_width=True)
        
        # Price trends
        price_trends = data.groupby(data['date'].dt.to_period('M').astype(str)).agg({
            'price_per_ton': 'mean'
        }).reset_index()
        
        fig4 = px.line(price_trends, x='date', y='price_per_ton',
                      title="Average Price per Ton Over Time",
                      labels={'date': 'Month', 'price_per_ton': 'Avg Price per Ton ($)'})
        st.plotly_chart(fig4, use_container_width=True)

    # Project Breakdown tab
    with tabs[2]:
        st.subheader("Project Type Analysis")
        
        # Create project type breakdown
        project_agg = data.groupby('project_type').agg({
            'transaction_id': 'count',
            'quantity': 'sum',
            'total_value': 'sum'
        }).reset_index()
        
        # Create two columns for the charts
        col1, col2 = st.columns(2)
        
        # Project type by transaction count
        with col1:
            fig5 = px.pie(project_agg, values='transaction_id', names='project_type',
                         title="Transactions by Project Type",
                         hole=0.4)
            st.plotly_chart(fig5, use_container_width=True)
        
        # Project type by quantity
        with col2:
            fig6 = px.pie(project_agg, values='quantity', names='project_type',
                         title="Carbon Credit Quantity by Project Type",
                         hole=0.4)
            st.plotly_chart(fig6, use_container_width=True)
        
        # Project type and standard relationship
        standard_project = data.groupby(['standard', 'project_type']).agg({
            'quantity': 'sum'
        }).reset_index()
        
        fig7 = px.sunburst(standard_project, path=['standard', 'project_type'], values='quantity',
                          title="Standards and Project Types Relationship")
        st.plotly_chart(fig7, use_container_width=True)
        
        # Price comparison by project type
        fig8 = px.box(data, x='project_type', y='price_per_ton',
                     title="Price Distribution by Project Type",
                     labels={'project_type': 'Project Type', 'price_per_ton': 'Price per Ton ($)'})
        st.plotly_chart(fig8, use_container_width=True)
        
        # Vintage year analysis
        vintage_project = data.groupby(['vintage_year', 'project_type']).agg({
            'quantity': 'sum'
        }).reset_index()
        
        fig9 = px.bar(vintage_project, x='vintage_year', y='quantity', color='project_type',
                     title="Carbon Credits by Vintage Year and Project Type",
                     labels={'vintage_year': 'Vintage Year', 'quantity': 'Quantity (tons)', 'project_type': 'Project Type'})
        st.plotly_chart(fig9, use_container_width=True)

    # Geographic Distribution tab
    with tabs[3]:
        st.subheader("Geographic Analysis")
        
        # Geographic breakdown
        geo_agg = data.groupby('location').agg({
            'transaction_id': 'count',
            'quantity': 'sum',
            'total_value': 'sum',
            'price_per_ton': 'mean'
        }).reset_index()
        
        # Create map visualization
        fig10 = px.scatter_geo(geo_agg, locations='location', locationmode='country names',
                             size='quantity', color='price_per_ton',
                             hover_name='location',
                             hover_data=['transaction_id', 'quantity', 'total_value', 'price_per_ton'],
                             title="Carbon Credit Transactions by Location",
                             projection='natural earth',
                             color_continuous_scale=px.colors.sequential.Viridis)
        st.plotly_chart(fig10, use_container_width=True)
        
        # Geographic bar charts
        col1, col2 = st.columns(2)
        
        # Transactions by location
        with col1:
            fig11 = px.bar(geo_agg.sort_values('transaction_id', ascending=False), 
                          x='location', y='transaction_id',
                          title="Transaction Count by Location",
                          labels={'location': 'Country', 'transaction_id': 'Number of Transactions'})
            st.plotly_chart(fig11, use_container_width=True)
        
        # Quantity by location
        with col2:
            fig12 = px.bar(geo_agg.sort_values('quantity', ascending=False), 
                          x='location', y='quantity',
                          title="Carbon Credit Quantity by Location",
                          labels={'location': 'Country', 'quantity': 'Quantity (tons)'})
            st.plotly_chart(fig12, use_container_width=True)
        
        # Average price by location
        fig13 = px.bar(geo_agg.sort_values('price_per_ton', ascending=False), 
                      x='location', y='price_per_ton',
                      title="Average Price per Ton by Location",
                      labels={'location': 'Country', 'price_per_ton': 'Avg Price per Ton ($)'})
        st.plotly_chart(fig13, use_container_width=True)
        
        # Project types by location
        location_project = data.groupby(['location', 'project_type']).agg({
            'quantity': 'sum'
        }).reset_index()
        
        fig14 = px.treemap(location_project, path=['location', 'project_type'], values='quantity',
                          title="Project Types by Location")
        st.plotly_chart(fig14, use_container_width=True)

# Create the dashboard with the data
create_dashboard(df)

# Footer
st.markdown("---")
st.markdown("### About this Dashboard")
st.markdown("""
This dashboard visualizes carbon credit transaction data. Upload your own CSV file with the required columns to analyze your data.

Required columns:
- transaction_id: Unique identifier for each transaction
- date: Transaction date
- project_type: Type of carbon credit project
- project_id: Unique identifier for each project
- seller: Organization selling the credits
- buyer: Organization buying the credits
- quantity: Amount of carbon credits in tons
- price_per_ton: Price per ton of carbon credits
- total_value: Total transaction value
- standard: Certification standard
- vintage_year: Year the credits were generated
- location: Country where the project is located
""")