Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import pandas as pd
-import plotly.graph_objects as go
 import plotly.express as px
 
 # Set page configurations
@@ -76,12 +75,11 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
         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
+    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):
-    st.sidebar.subheader(f"Inputs for {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 (%)")
@@ -125,38 +123,38 @@ if uploaded_file and fiber_impact_data and transport_impact_data and washing_imp
     comparison_mode = st.sidebar.checkbox("Enable Comparison Mode")
     
     if comparison_mode:
-        # Input for two scenarios
+        # Input for two assessments
         col1, col2 = st.columns(2)
         with col1:
-            st.subheader("Scenario 1")
-            product_weight_1, composition_1, lifecycle_inputs_1 = get_inputs("Scenario 1")
+            st.subheader("Assessment 1")
+            product_weight_1, composition_1, lifecycle_inputs_1 = get_inputs("Assessment 1")
         with col2:
-            st.subheader("Scenario 2")
-            product_weight_2, composition_2, lifecycle_inputs_2 = get_inputs("Scenario 2")
+            st.subheader("Assessment 2")
+            product_weight_2, composition_2, lifecycle_inputs_2 = get_inputs("Assessment 2")
         
-        # Calculations for both scenarios
+        # 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
-        st.subheader("Comparison Results")
-        comparison_data = pd.DataFrame({
+        # Combined visualization with line chart
+        st.subheader("Comparison of Assessments")
+        assessment_data = pd.DataFrame({
             "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
-            "Scenario 1": [water_fp_1, energy_fp_1, carbon_fp_1],
-            "Scenario 2": [water_fp_2, energy_fp_2, carbon_fp_2],
+            "Assessment 1": [water_fp_1, energy_fp_1, carbon_fp_1],
+            "Assessment 2": [water_fp_2, energy_fp_2, carbon_fp_2],
         })
-        fig = px.bar(
-            comparison_data, 
-            x="Footprint Type", 
-            y=["Scenario 1", "Scenario 2"], 
-            barmode="group",
-            title="Comparison of Footprints"
+        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 scenario
-        st.subheader("Single Scenario")
-        product_weight, composition, lifecycle_inputs = get_inputs("Single Scenario")
+        # 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
@@ -165,7 +163,7 @@ if uploaded_file and fiber_impact_data and transport_impact_data and washing_imp
         st.markdown(f"- **Energy Footprint**: {energy_fp:.2f} MJ")
         st.markdown(f"- **Carbon Footprint**: {carbon_fp:.2f} kg CO2e")
         
-        # Visualization
+        # Visualization for single scenario
         result_data = pd.DataFrame({
             "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
             "Value": [water_fp, energy_fp, carbon_fp],