Code Editor for app.py

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

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

st.title("🌍 Carbon Credit Transaction Dashboard")
st.markdown("Upload your CSV file to explore carbon credit transaction data")

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

# Sample data for demonstration
sample_data = """transaction_id,date,project_type,project_id,seller,buyer,quantity,price_per_ton,total_value,standard,vintage_year,location
TR-12493,2024-01-15,Reforestation,PRJ-4587,Carbon Forest Partners,GreenInvest Group,5000,12.50,62500.00,Gold Standard,2023,Brazil
TR-12494,2024-01-17,Renewable Energy,PRJ-2839,SunPower Solutions,Eco Capital Fund,3200,9.75,31200.00,VCS,2023,India
TR-12495,2024-01-21,Methane Capture,PRJ-6721,AgriTech Emissions,Climate Forward,2800,14.25,39900.00,CAR,2023,United States
TR-12496,2024-01-23,Reforestation,PRJ-4588,Forest Guard Corp,Blue Sky Ventures,4500,13.00,58500.00,Gold Standard,2023,Indonesia
TR-12497,2024-01-25,Solar Energy,PRJ-2840,Solar Dynamics,EcoFund International,6000,8.90,53400.00,VCS,2023,Morocco
TR-12498,2024-01-28,Wind Energy,PRJ-2841,WindTech Solutions,Green Capital Partners,7200,10.20,73440.00,VCS,2023,Denmark
TR-12499,2024-02-01,Reforestation,PRJ-4589,Amazon Forestry,Carbon Offset Inc,3800,12.75,48450.00,Gold Standard,2023,Peru
TR-12500,2024-02-03,Methane Capture,PRJ-6722,Waste Management Co,Climate Solutions,2100,15.50,32550.00,CAR,2023,Canada
TR-12501,2024-02-05,Renewable Energy,PRJ-2842,HydroClean Energy,Sustainable Investments,5500,11.30,62150.00,VCS,2023,Norway
TR-12502,2024-02-08,Reforestation,PRJ-4590,Tropical Forest Alliance,Green Future Fund,4200,13.25,55650.00,Gold Standard,2023,Costa Rica"""

def load_data(file_content):
    """Load and process the CSV data"""
    try:
        df = pd.read_csv(io.StringIO(file_content))
        
        # Convert date column to datetime
        if 'date' in df.columns:
            df['date'] = pd.to_datetime(df['date'])
        
        # Ensure numeric columns are properly typed
        numeric_columns = ['quantity', 'price_per_ton', 'total_value', 'vintage_year']
        for col in numeric_columns:
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors='coerce')
        
        return df
    except Exception as e:
        st.error(f"Error loading data: {str(e)}")
        return None

def create_charts(df):
    """Create interactive charts for the dashboard"""
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.subheader("📈 Transaction Trends Over Time")
        
        # Daily transaction volume
        daily_stats = df.groupby('date').agg({
            'total_value': 'sum',
            'quantity': 'sum',
            'transaction_id': 'count'
        }).reset_index()
        
        fig_trend = px.line(
            daily_stats, 
            x='date', 
            y='total_value',
            title='Total Transaction Value Over Time',
            labels={'total_value': 'Total Value ($)', 'date': 'Date'}
        )
        fig_trend.update_layout(height=400)
        st.plotly_chart(fig_trend, use_container_width=True)
    
    with col2:
        st.subheader("🏭 Project Type Distribution")
        
        # Project type distribution
        project_stats = df.groupby('project_type').agg({
            'total_value': 'sum',
            'quantity': 'sum',
            'transaction_id': 'count'
        }).reset_index()
        
        fig_pie = px.pie(
            project_stats, 
            values='total_value', 
            names='project_type',
            title='Total Value by Project Type'
        )
        fig_pie.update_layout(height=400)
        st.plotly_chart(fig_pie, use_container_width=True)
    
    col3, col4 = st.columns(2)
    
    with col3:
        st.subheader("🌍 Geographic Distribution")
        
        # Geographic breakdown
        location_stats = df.groupby('location').agg({
            'total_value': 'sum',
            'quantity': 'sum',
            'transaction_id': 'count'
        }).reset_index().sort_values('total_value', ascending=False)
        
        fig_geo = px.bar(
            location_stats.head(10), 
            x='location', 
            y='total_value',
            title='Transaction Value by Location (Top 10)',
            labels={'total_value': 'Total Value ($)', 'location': 'Location'}
        )
        fig_geo.update_xaxes(tickangle=45)
        fig_geo.update_layout(height=400)
        st.plotly_chart(fig_geo, use_container_width=True)
    
    with col4:
        st.subheader("💰 Price per Ton Analysis")
        
        # Price distribution by project type
        fig_box = px.box(
            df, 
            x='project_type', 
            y='price_per_ton',
            title='Price per Ton Distribution by Project Type'
        )
        fig_box.update_xaxes(tickangle=45)
        fig_box.update_layout(height=400)
        st.plotly_chart(fig_box, use_container_width=True)

def show_summary_metrics(df):
    """Display key summary metrics"""
    st.subheader("📊 Key Metrics")
    
    col1, col2, col3, col4, col5 = st.columns(5)
    
    with col1:
        total_transactions = len(df)
        st.metric("Total Transactions", f"{total_transactions:,}")
    
    with col2:
        total_value = df['total_value'].sum()
        st.metric("Total Value", f"${total_value:,.2f}")
    
    with col3:
        total_quantity = df['quantity'].sum()
        st.metric("Total Quantity", f"{total_quantity:,} tons")
    
    with col4:
        avg_price = df['price_per_ton'].mean()
        st.metric("Avg Price/Ton", f"${avg_price:.2f}")
    
    with col5:
        unique_projects = df['project_type'].nunique()
        st.metric("Project Types", unique_projects)

# Main app logic
if uploaded_file is not None:
    # Read the uploaded file
    file_content = uploaded_file.read().decode('utf-8')
    df = load_data(file_content)
    
    if df is not None:
        st.success(f"Successfully loaded {len(df)} transactions!")
        
        # Show summary metrics
        show_summary_metrics(df)
        
        # Create tabs for different views
        tab1, tab2, tab3 = st.tabs(["📊 Dashboard", "📋 Data Table", "🔍 Data Info"])
        
        with tab1:
            create_charts(df)
        
        with tab2:
            st.subheader("Transaction Data")
            st.dataframe(df, use_container_width=True)
            
            # Download filtered data
            csv = df.to_csv(index=False)
            st.download_button(
                label="Download data as CSV",
                data=csv,
                file_name='filtered_transactions.csv',
                mime='text/csv',
            )
        
        with tab3:
            st.subheader("Dataset Information")
            
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Dataset Shape:**", df.shape)
                st.write("**Date Range:**", f"{df['date'].min().strftime('%Y-%m-%d')} to {df['date'].max().strftime('%Y-%m-%d')}")
                
                st.write("**Column Info:**")
                buffer = io.StringIO()
                df.info(buf=buffer)
                st.text(buffer.getvalue())
            
            with col2:
                st.write("**Summary Statistics:**")
                st.dataframe(df.describe())
else:
    st.info("👆 Please upload a CSV file to get started")
    
    # Show sample data preview
    with st.expander("📋 View Sample Data Format"):
        st.markdown("Here's an example of the expected CSV format:")
        sample_df = load_data(sample_data)
        if sample_df is not None:
            st.dataframe(sample_df)
            
            st.markdown("**Expected columns:**")
            st.write("- `transaction_id`: Unique identifier for each transaction")
            st.write("- `date`: Transaction date (YYYY-MM-DD format)")
            st.write("- `project_type`: Type of carbon credit project")
            st.write("- `project_id`: Project identifier")
            st.write("- `seller`: Selling organization")
            st.write("- `buyer`: Buying organization")
            st.write("- `quantity`: Quantity of carbon credits (tons)")
            st.write("- `price_per_ton`: Price per ton of CO2")
            st.write("- `total_value`: Total transaction value")
            st.write("- `standard`: Carbon credit standard")
            st.write("- `vintage_year`: Year of carbon credit vintage")
            st.write("- `location`: Geographic location of project")
    
    # Test with sample data button
    if st.button("🧪 Try with Sample Data"):
        df = load_data(sample_data)
        if df is not None:
            st.success("Loaded sample data!")
            
            # Show summary metrics
            show_summary_metrics(df)
            
            # Create tabs for different views
            tab1, tab2, tab3 = st.tabs(["📊 Dashboard", "📋 Data Table", "🔍 Data Info"])
            
            with tab1:
                create_charts(df)
            
            with tab2:
                st.subheader("Sample Transaction Data")
                st.dataframe(df, use_container_width=True)
            
            with tab3:
                st.subheader("Sample Dataset Information")
                
                col1, col2 = st.columns(2)
                
                with col1:
                    st.write("**Dataset Shape:**", df.shape)
                    st.write("**Date Range:**", f"{df['date'].min().strftime('%Y-%m-%d')} to {df['date'].max().strftime('%Y-%m-%d')}")
                
                with col2:
                    st.write("**Summary Statistics:**")
                    st.dataframe(df.describe())