A Comprehensive Tool for Assessing Water, Energy, and Carbon Footprints of Textile Products 🌍
""", unsafe_allow_html=True, ) # Dataset URL DATASET_URL = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/DataSet01.xlsx" # Load dataset from Hugging Face Spaces @st.cache_data def process_dataset(url): try: excel_content = pd.ExcelFile(url) fiber_data = pd.read_excel(excel_content, sheet_name="Fiber Impact Data") transport_data = pd.read_excel(excel_content, sheet_name="Transport Impact Data") washing_data = pd.read_excel(excel_content, sheet_name="Washing Data") # Convert data to dictionaries for 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 loading dataset: {e}") return None, None, None # 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"]) water_fp /= 1000 # Convert water from liters to kiloliters return water_fp, energy_fp, carbon_fp # Sidebar inputs def get_inputs(prefix): weight = st.sidebar.number_input(f"{prefix} Product Weight (kg)", min_value=0.0, value=0.0, step=0.01, key=f"{prefix}_weight") st.sidebar.subheader(f"{prefix} Material Composition (%)") cotton = st.sidebar.number_input("Conventional Cotton (%)", 0, 100, 0, step=1, key=f"{prefix}_cotton") polyester = st.sidebar.number_input("Polyester (%)", 0, 100, 0, step=1, key=f"{prefix}_polyester") nylon = st.sidebar.number_input("Nylon 6 (%)", 0, 100, 0, step=1, key=f"{prefix}_nylon") acrylic = st.sidebar.number_input("Acrylic (%)", 0, 100, 0, step=1, key=f"{prefix}_acrylic") viscose = st.sidebar.number_input("Viscose (%)", 0, 100, 0, step=1, key=f"{prefix}_viscose") if cotton + polyester + nylon + acrylic + viscose != 100: st.sidebar.error("Fiber composition must sum to 100%!") lifecycle_inputs = { "washing_cycles": st.sidebar.number_input(f"{prefix} Washing Cycles", min_value=0, value=0, key=f"{prefix}_wash_cycles"), "washing_temperature": st.sidebar.selectbox(f"{prefix} Washing Temperature", list(washing_impact_data.keys()), key=f"{prefix}_wash_temp"), "use_dryer": st.sidebar.checkbox(f"{prefix} Use Dryer?", key=f"{prefix}_use_dryer"), "transport_mode": st.sidebar.selectbox(f"{prefix} Transport Mode", list(transport_impact_data.keys()), key=f"{prefix}_transport_mode"), "transport_distance": st.sidebar.number_input(f"{prefix} Transport Distance (km)", min_value=0, value=0, step=10, key=f"{prefix}_transport_distance"), } composition = { "Conventional Cotton": cotton, "Polyester": polyester, "Nylon 6": nylon, "Acrylic": acrylic, "Viscose": viscose, } return weight, composition, lifecycle_inputs # Main application logic fiber_impact_data, transport_impact_data, washing_impact_data = process_dataset(DATASET_URL) if 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: weight1, composition1, lifecycle1 = get_inputs("Assessment 1") with col2: weight2, composition2, lifecycle2 = get_inputs("Assessment 2") # Calculate footprints for both assessments water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1) water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2) # Display numerical comparison st.subheader("Numerical Comparison") st.write(f"**Assessment 1**: Water: {water1:.2f} kL, Energy: {energy1:.2f} MJ, Carbon: {carbon1:.2f} kg CO2e") st.write(f"**Assessment 2**: Water: {water2:.2f} kL, Energy: {energy2:.2f} MJ, Carbon: {carbon2:.2f} kg CO2e") # Bar chart comparison comparison_data = pd.DataFrame({ "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"], "Assessment 1": [water1, energy1, carbon1], "Assessment 2": [water2, energy2, carbon2], }) fig = px.bar( comparison_data.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"), x="Footprint Type", y="Value", color="Assessment", title="Comparison of Assessments" ) st.plotly_chart(fig) else: # Input for a single assessment weight, composition, lifecycle = get_inputs("Single") water, energy, carbon = calculate_footprints(weight, composition, lifecycle) # Display results st.subheader("Single Assessment Results") st.markdown(f"- **Water Footprint**: {water:.2f} kL") st.markdown(f"- **Energy Footprint**: {energy:.2f} MJ") st.markdown(f"- **Carbon Footprint**: {carbon:.2f} kg CO2e") # Bar chart for single assessment result_data = pd.DataFrame({ "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"], "Value": [water, energy, carbon] }) fig = px.bar(result_data, x="Footprint Type", y="Value", title="Single Assessment Footprint Breakdown") st.plotly_chart(fig) else: st.error("Failed to load dataset.")