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")

st.title("Carbon Credits Dashboard")

# File upload
uploaded_file = st.file_uploader("Drop your CSV file here", type=['csv'])

if uploaded_file is not None:
    # Read CSV
    df = pd.read_csv(uploaded_file)
    
    # Convert date to datetime
    df['date'] = pd.to_datetime(df['date'])
    
    # Display raw data in an expandable section
    with st.expander("Raw Data"):
        st.dataframe(df)
    
    # Create three columns for the metrics
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Total Transactions", len(df))
    with col2:
        st.metric("Total Volume (tons)", df['quantity'].sum())
    with col3:
        st.metric("Total Value ($)", f"{df['total_value'].sum():,.2f}")
    
    # Create two columns for charts
    col1, col2 = st.columns(2)
    
    with col1:
        # Transaction trends over time
        fig_trends = px.line(
            df.groupby('date').agg({
                'total_value': 'sum',
                'quantity': 'sum'
            }).reset_index(),
            x='date',
            y=['total_value', 'quantity'],
            title='Transaction Trends Over Time'
        )
        st.plotly_chart(fig_trends, use_container_width=True)
        
        # Geographic distribution
        fig_geo = px.pie(
            df.groupby('location').agg({
                'total_value': 'sum'
            }).reset_index(),
            values='total_value',
            names='location',
            title='Geographic Distribution of Transactions'
        )
        st.plotly_chart(fig_geo, use_container_width=True)
    
    with col2:
        # Project type distribution
        fig_project = px.bar(
            df.groupby('project_type').agg({
                'quantity': 'sum',
                'total_value': 'sum'
            }).reset_index(),
            x='project_type',
            y=['quantity', 'total_value'],
            title='Project Type Distribution',
            barmode='group'
        )
        st.plotly_chart(fig_project, use_container_width=True)
        
        # Price trends by project type
        fig_price = px.box(
            df,
            x='project_type',
            y='price_per_ton',
            title='Price Distribution by Project Type'
        )
        st.plotly_chart(fig_price, use_container_width=True)
    
    # Additional analysis section
    st.header("Detailed Analysis")
    
    # Standard distribution
    col1, col2 = st.columns(2)
    
    with col1:
        standard_dist = df.groupby('standard')['quantity'].sum().reset_index()
        fig_standard = px.pie(
            standard_dist,
            values='quantity',
            names='standard',
            title='Distribution by Standard'
        )
        st.plotly_chart(fig_standard, use_container_width=True)
    
    with col2:
        # Top sellers and buyers
        top_sellers = df.groupby('seller')['total_value'].sum().sort_values(ascending=False).head(5)
        fig_sellers = px.bar(
            top_sellers,
            title='Top 5 Sellers by Total Value'
        )
        st.plotly_chart(fig_sellers, use_container_width=True)
else:
    st.info("Please upload a CSV file to begin the analysis.")