app.py

import streamlit as st
import pandas as pd
import plotly.express as px

# Set page configurations
st.set_page_config(page_title="GreenLens-AI", layout="wide")

# Page title and description
st.markdown("<h1 style='text-align: center; color: #4CAF50;'>GreenLens-AI</h1>", unsafe_allow_html=True)
st.markdown(
    """
    <p style='text-align: center; color: #4CAF50;'> 
    A Tool for Calculating Water, Energy, and Carbon Footprints of Textile Products 🌍 
    </p>
    """,
    unsafe_allow_html=True,
)

# Sidebar for file upload
st.sidebar.header("Step 1: Upload Dataset")
uploaded_file = st.sidebar.file_uploader("Upload your Excel file (.xlsx)", type=["xlsx"])

# Initialize data containers
fiber_impact_data = None
transport_impact_data = None
washing_impact_data = None

# Function to process the uploaded Excel file
@st.cache_data
def process_excel(file):
    try:
        excel_content = pd.ExcelFile(file)
        fiber_data = pd.read_excel(file, sheet_name="Fiber Impact Data")
        transport_data = pd.read_excel(file, sheet_name="Transport Impact Data")
        washing_data = pd.read_excel(file, sheet_name="Washing Data")
        
        # Convert into dictionaries for dynamic calculations
        fiber_impact_data = fiber_data.set_index("Fiber Type")[["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]].to_dict(orient="index")
        transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
        washing_impact_data = washing_data.set_index("Washing Temperature")[["Water (L/kg)", "Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"]].to_dict(orient="index")
        
        return fiber_impact_data, transport_impact_data, washing_impact_data
    except Exception as e:
        st.error(f"Error processing the file: {e}")
        return None, None, None

# Process uploaded file
if uploaded_file:
    fiber_impact_data, transport_impact_data, washing_impact_data = process_excel(uploaded_file)

# Function to calculate footprints
def calculate_footprints(weight, composition, lifecycle_inputs):
    water_fp, energy_fp, carbon_fp = 0, 0, 0

    for fiber, percentage in composition.items():
        if fiber in fiber_impact_data:
            data = fiber_impact_data[fiber]
            fraction = percentage / 100
            water_fp += data["Water (L/kg)"] * weight * fraction
            energy_fp += data["Energy (MJ/kg)"] * weight * fraction
            carbon_fp += data["Carbon (kg CO2e/kg)"] * weight * fraction

    if lifecycle_inputs["transport_mode"] in transport_impact_data:
        carbon_fp += transport_impact_data[lifecycle_inputs["transport_mode"]] * lifecycle_inputs["transport_distance"] * weight

    if lifecycle_inputs["washing_temperature"] in washing_impact_data:
        washing_data = washing_impact_data[lifecycle_inputs["washing_temperature"]]
        washing_water = washing_data["Water (L/kg)"] * lifecycle_inputs["washing_cycles"]
        washing_energy = washing_data["Energy Use (MJ/wash)"] * lifecycle_inputs["washing_cycles"]
        washing_carbon = washing_data["Carbon (kg CO2e/wash)"] * lifecycle_inputs["washing_cycles"]
        dryer_carbon = washing_data["Dryer CFP (kg CO2e/cycle)"] if lifecycle_inputs["use_dryer"] else 0

        water_fp += washing_water
        energy_fp += washing_energy
        carbon_fp += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])

    # Convert water footprint from liters to kiloliters for visualization
    water_fp_kL = water_fp / 1000  # Convert liters to kiloliters
    return water_fp_kL, energy_fp, carbon_fp

# Sidebar inputs for all scenarios
def get_inputs(key_prefix):
    product_weight = st.sidebar.number_input(f"{key_prefix} - Product Weight (kg)", min_value=0.01, step=0.01, value=0.5, key=f"{key_prefix}_weight")
    
    st.sidebar.subheader(f"{key_prefix} - Material Composition (%)")
    cotton = st.sidebar.number_input("Conventional Cotton (%)", min_value=0, max_value=100, value=50, step=1, key=f"{key_prefix}_cotton")
    polyester = st.sidebar.number_input("Polyester (%)", min_value=0, max_value=100, value=30, step=1, key=f"{key_prefix}_polyester")
    nylon = st.sidebar.number_input("Nylon 6 (%)", min_value=0, max_value=100, value=10, step=1, key=f"{key_prefix}_nylon")
    acrylic = st.sidebar.number_input("Acrylic (%)", min_value=0, max_value=100, value=5, step=1, key=f"{key_prefix}_acrylic")
    viscose = st.sidebar.number_input("Viscose (%)", min_value=0, max_value=100, value=5, step=1, key=f"{key_prefix}_viscose")
    
    total_percentage = cotton + polyester + nylon + acrylic + viscose
    if total_percentage != 100:
        st.sidebar.error(f"Total composition for {key_prefix} must be 100%!")
    
    composition = {
        "Conventional Cotton": cotton,
        "Polyester": polyester,
        "Nylon 6": nylon,
        "Acrylic": acrylic,
        "Viscose": viscose,
    }
    
    st.sidebar.subheader(f"{key_prefix} - Lifecycle Inputs")
    washing_cycles = st.sidebar.number_input(f"{key_prefix} - Washing Cycles", min_value=0, step=1, value=30, key=f"{key_prefix}_wash_cycles")
    washing_temperature = st.sidebar.selectbox(f"{key_prefix} - Washing Temperature", list(washing_impact_data.keys()), key=f"{key_prefix}_wash_temp")
    use_dryer = st.sidebar.checkbox(f"{key_prefix} - Use Tumble Dryer?", key=f"{key_prefix}_use_dryer")
    transport_mode = st.sidebar.selectbox(f"{key_prefix} - Transport Mode", list(transport_impact_data.keys()), key=f"{key_prefix}_transport_mode")
    transport_distance = st.sidebar.number_input(f"{key_prefix} - Transport Distance (km)", min_value=0, step=10, value=100, key=f"{key_prefix}_transport_distance")
    
    lifecycle_inputs = {
        "washing_temperature": washing_temperature,
        "washing_cycles": washing_cycles,
        "use_dryer": use_dryer,
        "transport_mode": transport_mode,
        "transport_distance": transport_distance,
    }
    
    return product_weight, composition, lifecycle_inputs

# Main interface
if uploaded_file and fiber_impact_data and transport_impact_data and washing_impact_data:
    comparison_mode = st.sidebar.checkbox("Enable Comparison Mode")
    
    if comparison_mode:
        # Input for two assessments
        col1, col2 = st.columns(2)
        with col1:
            st.subheader("Assessment 1")
            product_weight_1, composition_1, lifecycle_inputs_1 = get_inputs("Assessment 1")
        with col2:
            st.subheader("Assessment 2")
            product_weight_2, composition_2, lifecycle_inputs_2 = get_inputs("Assessment 2")
        
        # Calculations for both assessments
        water_fp_1, energy_fp_1, carbon_fp_1 = calculate_footprints(product_weight_1, composition_1, lifecycle_inputs_1)
        water_fp_2, energy_fp_2, carbon_fp_2 = calculate_footprints(product_weight_2, composition_2, lifecycle_inputs_2)
        
        # Combined visualization with line chart
        st.subheader("Comparison of Assessments")
        assessment_data = pd.DataFrame({
            "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
            "Assessment 1": [water_fp_1, energy_fp_1, carbon_fp_1],
            "Assessment 2": [water_fp_2, energy_fp_2, carbon_fp_2],
        })
        fig = px.line(
            assessment_data.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"),
            x="Footprint Type",
            y="Value",
            color="Assessment",
            markers=True,
            title="Footprint Trends: Assessment 1 vs. Assessment 2"
        )
        st.plotly_chart(fig)
    else:
        # Input for single calculation
        product_weight, composition, lifecycle_inputs = get_inputs("")
        water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition, lifecycle_inputs)
        
        # Display results
        st.subheader("Results")
        st.markdown(f"- **Water Footprint**: {water_fp:.2f} kL")
        st.markdown(f"- **Energy Footprint**: {energy_fp:.2f} MJ")
        st.markdown(f"- **Carbon Footprint**: {carbon_fp:.2f} kg CO2e")
        
        # Visualization for single scenario
        result_data = pd.DataFrame({
            "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
            "Value": [water_fp, energy_fp, carbon_fp],
        })
        fig = px.line(result_data, x="Footprint Type", y="Value", markers=True, title="Footprint Trends")
        st.plotly_chart(fig)
else:
    st.info("Please upload a dataset to proceed.")