Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import os
 from groq import Groq
 
 # Add custom CSS for the app background and highlighted text
@@ -65,7 +64,7 @@ def process_dataset(url):
         return None, None, None
 
 # Calculate footprints
-def calculate_footprints(weight, composition, lifecycle_inputs):
+def calculate_footprints(weight, composition, lifecycle_inputs, fiber_impact_data, transport_impact_data, washing_impact_data):
     water_fp, energy_fp, carbon_fp = 0, 0, 0
     for fiber, percentage in composition.items():
         if fiber in fiber_impact_data:
@@ -94,8 +93,42 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
     water_fp /= 1000
     return water_fp, energy_fp, carbon_fp
 
-# Sidebar inputs
-def get_inputs(prefix):
+# Adjust graph styling
+def style_figure(fig):
+    fig.update_layout(
+        plot_bgcolor="rgba(27, 27, 27, 0.8)",
+        paper_bgcolor="rgba(27, 27, 27, 0.8)",
+        font=dict(color="white"),
+        title_font=dict(size=18, color="white"),
+        xaxis=dict(title_font=dict(color="white"), tickfont=dict(color="white")),
+        yaxis=dict(title_font=dict(color="white"), tickfont=dict(color="white")),
+    )
+    fig.update_traces(marker=dict(color="white", line=dict(color="gray", width=1)))
+    return fig
+
+# Generate recommendations using Groq API
+def generate_recommendations(water, energy, carbon):
+    try:
+        client = Groq(api_key="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")
+        prompt = (
+            f"The environmental impact values for a textile product are as follows:\n"
+            f"Water Footprint: {water:.2f} kL\n"
+            f"Energy Footprint: {energy:.2f} MJ\n"
+            f"Carbon Footprint: {carbon:.2f} kg CO2e\n"
+            f"Provide recommendations to lower these impacts."
+        )
+
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3.3-70b-versatile",
+        )
+
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error generating recommendations: {e}"
+
+# Sidebar inputs - FIXED: Now returns data dictionaries as parameters
+def get_inputs(prefix, transport_impact_data, washing_impact_data):
     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.markdown(f"<h3 style='color: green;'>{prefix} Material Composition (%)</h3>", unsafe_allow_html=True)
     cotton = st.sidebar.number_input("Conventional Cotton (%)", 0, 100, 0, step=1, key=f"{prefix}_cotton")
@@ -128,40 +161,6 @@ def get_inputs(prefix):
     }
     return weight, composition, lifecycle_inputs
 
-# Adjust graph styling
-def style_figure(fig):
-    fig.update_layout(
-        plot_bgcolor="rgba(27, 27, 27, 0.8)",  # 20% transparency
-        paper_bgcolor="rgba(27, 27, 27, 0.8)",  # 20% transparency
-        font=dict(color="white"),  # Font color set to white
-        title_font=dict(size=18, color="white"),  # Title font white
-        xaxis=dict(title_font=dict(color="white"), tickfont=dict(color="white")),
-        yaxis=dict(title_font=dict(color="white"), tickfont=dict(color="white")),
-    )
-    fig.update_traces(marker=dict(color="white", line=dict(color="gray", width=1)))  # Simulate 3D effect with border
-    return fig
-
-# Generate recommendations using Groq API
-def generate_recommendations(water, energy, carbon):
-    try:
-        client = Groq(api_key="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")
-        prompt = (
-            f"The environmental impact values for a textile product are as follows:\n"
-            f"Water Footprint: {water:.2f} kL\n"
-            f"Energy Footprint: {energy:.2f} MJ\n"
-            f"Carbon Footprint: {carbon:.2f} kg CO2e\n"
-            f"Provide recommendations to lower these impacts."
-        )
-
-        response = client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model="llama-3.3-70b-versatile",
-        )
-
-        return response.choices[0].message.content
-    except Exception as e:
-        return f"Error generating recommendations: {e}"
-
 # Main application logic
 fiber_impact_data, transport_impact_data, washing_impact_data = process_dataset(DATASET_URL)
 
@@ -169,16 +168,16 @@ 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
+        # Input for two assessments - FIXED: Pass data dictionaries
         col1, col2 = st.columns(2)
         with col1:
-            weight1, composition1, lifecycle1 = get_inputs("Assessment 1")
+            weight1, composition1, lifecycle1 = get_inputs("Assessment 1", transport_impact_data, washing_impact_data)
         with col2:
-            weight2, composition2, lifecycle2 = get_inputs("Assessment 2")
+            weight2, composition2, lifecycle2 = get_inputs("Assessment 2", transport_impact_data, washing_impact_data)
 
-        # Calculate footprints for both assessments
-        water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1)
-        water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2)
+        # Calculate footprints for both assessments - FIXED: Pass data dictionaries
+        water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1, fiber_impact_data, transport_impact_data, washing_impact_data)
+        water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2, fiber_impact_data, transport_impact_data, washing_impact_data)
 
         # Display numerical comparison
         st.markdown(f"""
@@ -204,9 +203,9 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         )
         st.plotly_chart(style_figure(fig))
     else:
-        # Input for a single assessment
-        weight, composition, lifecycle = get_inputs("Single")
-        water, energy, carbon = calculate_footprints(weight, composition, lifecycle)
+        # Input for a single assessment - FIXED: Pass data dictionaries
+        weight, composition, lifecycle = get_inputs("Single", transport_impact_data, washing_impact_data)
+        water, energy, carbon = calculate_footprints(weight, composition, lifecycle, fiber_impact_data, transport_impact_data, washing_impact_data)
 
         # Display results
         st.markdown(f"""
@@ -236,4 +235,4 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
             </div>
             """, unsafe_allow_html=True)
 else:
-    st.error("Failed to load dataset.")
+    st.error("Failed to load dataset.")