Update app.py

app.py

@@ -2,14 +2,24 @@ import streamlit as st
 import pandas as pd
 import plotly.express as px
 
-# Set page configuration (must be the first Streamlit command)
-st.set_page_config(page_title="GreenLens-AI", layout="wide")
-
-# Dataset URL from Hugging Face Spaces
+# Set page configuration
+st.set_page_config(page_title="GreenLens AI", layout="wide")
+
+# Title and subtitle
+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; font-size: 18px;'>
+    A Comprehensive Tool for Assessing Water, Energy, and Carbon Footprints of Textile Products 🌍
+    </p>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Dataset URL
 DATASET_URL = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/DataSet01.xlsx"
 
-
-# Process dataset from Hugging Face Spaces
+# Load dataset from Hugging Face Spaces
 @st.cache_data
 def process_dataset(url):
     try:
@@ -18,13 +28,13 @@ def process_dataset(url):
         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
+        # 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 accessing the dataset: {e}")
+        st.error(f"Error loading dataset: {e}")
         return None, None, None
 
 
@@ -58,23 +68,23 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
 
 # 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")
+    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 (%)", 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")
+    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=30, key=f"{prefix}_wash_cycles"),
+        "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=100, step=10, key=f"{prefix}_transport_distance")
+        "transport_distance": st.sidebar.number_input(f"{prefix} Transport Distance (km)", min_value=0, value=0, step=10, key=f"{prefix}_transport_distance"),
     }
 
     composition = {
@@ -82,19 +92,19 @@ def get_inputs(prefix):
         "Polyester": polyester,
         "Nylon 6": nylon,
         "Acrylic": acrylic,
-        "Viscose": viscose
+        "Viscose": viscose,
     }
     return weight, composition, lifecycle_inputs
 
 
-# Main App Logic
-st.sidebar.header("Step 1: Configuration")
+# 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:
             st.subheader("Assessment 1")
@@ -103,14 +113,20 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
             st.subheader("Assessment 2")
             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)
 
-        st.subheader("Comparison of Assessments")
+        # 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]
+            "Assessment 2": [water2, energy2, carbon2],
         })
         fig = px.bar(
             comparison_data.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"),
@@ -122,19 +138,22 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         st.plotly_chart(fig)
 
     else:
+        # Input for a single assessment
         weight, composition, lifecycle = get_inputs("Single")
         water, energy, carbon = calculate_footprints(weight, composition, lifecycle)
 
-        st.subheader("Results")
-        st.markdown(f"- **Water Footprint**: {water:.2f} kL\n"
-                    f"- **Energy Footprint**: {energy:.2f} MJ\n"
-                    f"- **Carbon Footprint**: {carbon:.2f} kg CO2e")
+        # 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="Footprint Breakdown")
+        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.")