Update app.py

app.py

@@ -2,56 +2,35 @@ import streamlit as st
 import pandas as pd
 import plotly.express as px
 
-# Set page configurations
+# Set page configuration (must be the first Streamlit command)
 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
+# Dataset URL from Hugging Face Spaces
+DATASET_URL = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/DataSet01.xlsx"
+
+
+# Process dataset from Hugging Face Spaces
 @st.cache_data
-def process_excel(file):
+def process_dataset(url):
     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
+        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 into 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 processing the file: {e}")
+        st.error(f"Error accessing the dataset: {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
+# 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]
@@ -69,106 +48,93 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
         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%!")
-    
+    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=0.01, value=0.5, step=0.01, key=f"{prefix}_weight")
+    st.sidebar.subheader(f"{prefix} Material Composition (%)")
+    cotton = st.sidebar.number_input("Conventional Cotton (%)", min_value=0, max_value=100, value=50, step=1, key=f"{prefix}_cotton")
+    polyester = st.sidebar.number_input("Polyester (%)", min_value=0, max_value=100, value=30, step=1, key=f"{prefix}_polyester")
+    nylon = st.sidebar.number_input("Nylon 6 (%)", min_value=0, max_value=100, value=10, step=1, key=f"{prefix}_nylon")
+    acrylic = st.sidebar.number_input("Acrylic (%)", min_value=0, max_value=100, value=5, step=1, key=f"{prefix}_acrylic")
+    viscose = st.sidebar.number_input("Viscose (%)", min_value=0, max_value=100, value=5, 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=30, 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=100, step=10, key=f"{prefix}_transport_distance")
+    }
+
     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,
+        "Viscose": viscose
     }
-    
-    return product_weight, composition, lifecycle_inputs
+    return weight, composition, lifecycle_inputs
 
-# Main interface
-if uploaded_file and fiber_impact_data and transport_impact_data and washing_impact_data:
+
+# Main App Logic
+st.sidebar.header("Step 1: Configuration")
+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:
             st.subheader("Assessment 1")
-            product_weight_1, composition_1, lifecycle_inputs_1 = get_inputs("Assessment 1")
+            weight1, composition1, lifecycle1 = 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
+            weight2, composition2, lifecycle2 = get_inputs("Assessment 2")
+
+        water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1)
+        water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2)
+
         st.subheader("Comparison of Assessments")
-        assessment_data = pd.DataFrame({
+        comparison_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],
+            "Assessment 1": [water1, energy1, carbon1],
+            "Assessment 2": [water2, energy2, carbon2]
         })
-        fig = px.line(
-            assessment_data.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"),
+        fig = px.bar(
+            comparison_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"
+            title="Comparison of Assessments"
         )
         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
+        weight, composition, lifecycle = get_inputs("Single")
+        water, energy, carbon = calculate_footprints(weight, composition, lifecycle)
+
         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
+        st.markdown(f"- **Water Footprint**: {water:.2f} kL\n"
+                    f"- **Energy Footprint**: {energy:.2f} MJ\n"
+                    f"- **Carbon Footprint**: {carbon:.2f} kg CO2e")
+
         result_data = pd.DataFrame({
             "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
-            "Value": [water_fp, energy_fp, carbon_fp],
+            "Value": [water, energy, carbon]
         })
-        fig = px.line(result_data, x="Footprint Type", y="Value", markers=True, title="Footprint Trends")
+        fig = px.bar(result_data, x="Footprint Type", y="Value", title="Footprint Breakdown")
         st.plotly_chart(fig)
 else:
-    st.info("Please upload a dataset to proceed.")
+    st.error("Failed to load dataset.")