Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import pandas as pd
 import plotly.graph_objects as go
+import plotly.express as px
 
 # Set page configurations
 st.set_page_config(page_title="GreenLens-AI", layout="wide")
@@ -48,114 +49,111 @@ def process_excel(file):
 if uploaded_file:
     fiber_impact_data, transport_impact_data, washing_impact_data = process_excel(uploaded_file)
 
-# Sidebar settings
-st.sidebar.header("Optional: Enable Comparison")
+# Sidebar for product and fiber composition inputs
+st.sidebar.header("Step 2: Input Product Details")
+product_weight = st.sidebar.number_input("Product Weight (kg)", min_value=0.01, step=0.01, value=0.5)
+
+st.sidebar.header("Material Composition (%)")
+cotton = st.sidebar.number_input("Conventional Cotton (%)", min_value=0, max_value=100, value=50, step=1, key="cotton")
+polyester = st.sidebar.number_input("Polyester (%)", min_value=0, max_value=100, value=30, step=1, key="polyester")
+nylon = st.sidebar.number_input("Nylon 6 (%)", min_value=0, max_value=100, value=10, step=1, key="nylon")
+acrylic = st.sidebar.number_input("Acrylic (%)", min_value=0, max_value=100, value=5, step=1, key="acrylic")
+viscose = st.sidebar.number_input("Viscose (%)", min_value=0, max_value=100, value=5, step=1, key="viscose")
+
+total_percentage = cotton + polyester + nylon + acrylic + viscose
+if total_percentage != 100:
+    st.sidebar.error("The total of all fiber percentages must equal 100%!")
+
+composition = {
+    "Conventional Cotton": cotton,
+    "Polyester": polyester,
+    "Nylon 6": nylon,
+    "Acrylic": acrylic,
+    "Viscose": viscose,
+}
+
+# Sidebar for lifecycle inputs
+st.sidebar.header("Step 3: Input Lifecycle Details")
 comparison_mode = st.sidebar.checkbox("Enable Comparison Mode")
+washing_cycles = st.sidebar.number_input("Number of Washing Cycles", min_value=0, step=1, value=30)
+washing_temperature = None
+use_dryer = False
+transport_mode = None
+transport_distance = 0
+
+if washing_impact_data:
+    washing_temperature = st.sidebar.selectbox("Washing Temperature", list(washing_impact_data.keys()))
+    use_dryer = st.sidebar.checkbox("Use Tumble Dryer?")
+
+if transport_impact_data:
+    transport_mode = st.sidebar.selectbox("Transport Mode", list(transport_impact_data.keys()))
+    transport_distance = st.sidebar.number_input("Transport Distance (km)", min_value=0, step=10, value=100)
 
 # Function to calculate footprints
 def calculate_footprints(weight, composition, lifecycle_inputs):
-    water_footprint = 0
-    energy_footprint = 0
-    carbon_footprint = 0
-    
+    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_footprint += data["Water (L/kg)"] * weight * fraction
-            energy_footprint += data["Energy (MJ/kg)"] * weight * fraction
-            carbon_footprint += data["Carbon (kg CO2e/kg)"] * weight * fraction
-    
-    transport_factor = transport_impact_data.get(lifecycle_inputs["transport_mode"], 0)
-    carbon_footprint += transport_factor * lifecycle_inputs["transport_distance"] * weight
-
-    washing_data = washing_impact_data.get(lifecycle_inputs["washing_temperature"], {})
-    washing_water = washing_data.get("Water (L/kg)", 0) * lifecycle_inputs["washing_cycles"]
-    washing_energy = washing_data.get("Energy Use (MJ/wash)", 0) * lifecycle_inputs["washing_cycles"]
-    washing_carbon = washing_data.get("Carbon (kg CO2e/wash)", 0) * lifecycle_inputs["washing_cycles"]
-    dryer_carbon = washing_data.get("Dryer CFP (kg CO2e/cycle)", 0) if lifecycle_inputs["use_dryer"] else 0
-    
-    water_footprint += washing_water
-    energy_footprint += washing_energy
-    carbon_footprint += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])
-
-    return water_footprint, energy_footprint, carbon_footprint
-
-# Composition input function
-def composition_input():
-    st.subheader("Material Composition (%)")
-    cotton = st.number_input("Conventional Cotton (%)", min_value=0, max_value=100, value=50, step=1)
-    polyester = st.number_input("Polyester (%)", min_value=0, max_value=100, value=30, step=1)
-    nylon = st.number_input("Nylon 6 (%)", min_value=0, max_value=100, value=10, step=1)
-    acrylic = st.number_input("Acrylic (%)", min_value=0, max_value=100, value=5, step=1)
-    viscose = st.number_input("Viscose (%)", min_value=0, max_value=100, value=5, step=1)
-
-    total = cotton + polyester + nylon + acrylic + viscose
-    if total != 100:
-        st.error("Total fiber composition must equal 100%!")
-    return {"Conventional Cotton": cotton, "Polyester": polyester, "Nylon 6": nylon, "Acrylic": acrylic, "Viscose": viscose} if total == 100 else None
-
-# Inputs for lifecycle details
-def lifecycle_input():
-    st.subheader("Lifecycle Details")
-    washing_cycles = st.number_input("Number of Washing Cycles", min_value=0, value=30, step=1)
-    washing_temperature = st.selectbox("Washing Temperature", list(washing_impact_data.keys()))
-    use_dryer = st.checkbox("Use Tumble Dryer?")
-    transport_mode = st.selectbox("Transport Mode", list(transport_impact_data.keys()))
-    transport_distance = st.number_input("Transport Distance (km)", min_value=0, step=10, value=100)
-    return {
-        "washing_cycles": washing_cycles,
-        "washing_temperature": washing_temperature,
-        "use_dryer": use_dryer,
+            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"])
+
+    return water_fp, energy_fp, carbon_fp
+
+# Results display area
+st.header("Results")
+
+if uploaded_file and total_percentage == 100:
+    lifecycle_inputs = {
         "transport_mode": transport_mode,
         "transport_distance": transport_distance,
+        "washing_temperature": washing_temperature,
+        "washing_cycles": washing_cycles,
+        "use_dryer": use_dryer,
     }
 
-# Main calculation logic
-if uploaded_file and fiber_impact_data and transport_impact_data and washing_impact_data:
+    water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition, lifecycle_inputs)
+
+    st.subheader("Calculated Footprints")
+    st.markdown(f"- **Water Footprint**: {water_fp:.2f} liters")
+    st.markdown(f"- **Energy Footprint**: {energy_fp:.2f} MJ")
+    st.markdown(f"- **Carbon Footprint**: {carbon_fp:.2f} kg CO2e")
+
+    # Visualization
     if comparison_mode:
-        # Comparison mode enabled
-        st.header("Scenario 1")
-        composition1 = composition_input()
-        lifecycle1 = lifecycle_input()
-        
-        st.header("Scenario 2")
-        composition2 = composition_input()
-        lifecycle2 = lifecycle_input()
-        
-        if composition1 and composition2:
-            water_fp1, energy_fp1, carbon_fp1 = calculate_footprints(1, composition1, lifecycle1)
-            water_fp2, energy_fp2, carbon_fp2 = calculate_footprints(1, composition2, lifecycle2)
-
-            # Display results side by side
-            col1, col2 = st.columns(2)
-            with col1:
-                st.subheader("Scenario 1 Results")
-                st.markdown(f"- **Water Footprint**: {water_fp1:.2f} liters")
-                st.markdown(f"- **Energy Footprint**: {energy_fp1:.2f} MJ")
-                st.markdown(f"- **Carbon Footprint**: {carbon_fp1:.2f} kg CO2e")
-            with col2:
-                st.subheader("Scenario 2 Results")
-                st.markdown(f"- **Water Footprint**: {water_fp2:.2f} liters")
-                st.markdown(f"- **Energy Footprint**: {energy_fp2:.2f} MJ")
-                st.markdown(f"- **Carbon Footprint**: {carbon_fp2:.2f} kg CO2e")
-
-            # Visual comparison
-            fig = go.Figure()
-            fig.add_trace(go.Bar(name="Scenario 1", x=["Water", "Energy", "Carbon"], y=[water_fp1, energy_fp1, carbon_fp1]))
-            fig.add_trace(go.Bar(name="Scenario 2", x=["Water", "Energy", "Carbon"], y=[water_fp2, energy_fp2, carbon_fp2]))
-            fig.update_layout(barmode="group", title="Comparison of Scenarios")
-            st.plotly_chart(fig)
+        st.info("Comparison mode enabled. Results for two scenarios can be added here if required.")
     else:
-        # Single calculation
-        composition = composition_input()
-        lifecycle = lifecycle_input()
-
-        if composition:
-            water_fp, energy_fp, carbon_fp = calculate_footprints(1, composition, lifecycle)
-            st.subheader("Results")
-            st.markdown(f"- **Water Footprint**: {water_fp:.2f} liters")
-            st.markdown(f"- **Energy Footprint**: {energy_fp:.2f} MJ")
-            st.markdown(f"- **Carbon Footprint**: {carbon_fp:.2f} kg CO2e")
+        # Line chart visualization
+        footprint_data = pd.DataFrame({
+            "Footprint Type": ["Water", "Energy", "Carbon"],
+            "Value": [water_fp, energy_fp, carbon_fp],
+        })
+
+        fig = px.line(
+            footprint_data,
+            x="Footprint Type",
+            y="Value",
+            title="Footprint Trends",
+            markers=True,
+        )
+        st.plotly_chart(fig)
+
 else:
-    st.info("Please upload a dataset to proceed.")
+    st.info("Please upload a dataset and ensure all inputs are valid.")