Update app.py

app.py

@@ -1,8 +1,8 @@
-import os
-from groq import Groq
 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
 def add_background():
@@ -64,23 +64,6 @@ def process_dataset(url):
         st.error(f"Error loading dataset: {e}")
         return None, None, None
 
-# Function to get recommendations from Groq
-def get_groq_recommendations(water_fp, energy_fp, carbon_fp):
-    if water_fp == 0 and energy_fp == 0 and carbon_fp == 0:
-        return None
-
-    # Send the query to Groq
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": f"Based on the environmental impact data: Water: {water_fp:.2f} kL, Energy: {energy_fp:.2f} MJ, Carbon: {carbon_fp:.2f} kg CO2e, what are the recommendations to lower the environmental impacts?"
-            }
-        ],
-        model="llama-3.3-70b-versatile",
-    )
-    return chat_completion.choices[0].message.content
-
 # Calculate footprints
 def calculate_footprints(weight, composition, lifecycle_inputs):
     water_fp, energy_fp, carbon_fp = 0, 0, 0
@@ -158,6 +141,27 @@ def style_figure(fig):
     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=os.environ.get("GROQ_API_KEY"))
+        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)
 
@@ -195,21 +199,10 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
             comparison_data.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"),
             x="Footprint Type",
             y="Value",
-            color_discrete_sequence=["white"],
+            color="Assessment",
             title="Comparison of Assessments"
         )
         st.plotly_chart(style_figure(fig))
-
-        # Get Groq recommendations if necessary
-        recommendations1 = get_groq_recommendations(water1, energy1, carbon1)
-        recommendations2 = get_groq_recommendations(water2, energy2, carbon2)
-        if recommendations1:
-            st.subheader("Recommendations for Assessment 1")
-            st.write(recommendations1)
-        if recommendations2:
-            st.subheader("Recommendations for Assessment 2")
-            st.write(recommendations2)
-
     else:
         # Input for a single assessment
         weight, composition, lifecycle = get_inputs("Single")
@@ -230,11 +223,17 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
             "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
             "Value": [water, energy, carbon]
         })
-        fig = px.bar(result_data, x="Footprint Type", y="Value", title="Environmental Footprint")
+        fig = px.bar(result_data, x="Footprint Type", y="Value", title="Single Assessment Footprint Breakdown")
         st.plotly_chart(style_figure(fig))
 
-        # Get Groq recommendations if necessary
-        recommendations = get_groq_recommendations(water, energy, carbon)
-        if recommendations:
-            st.subheader("Recommendations")
-            st.write(recommendations)
+        # Generate recommendations if impact values are not zero
+        if water > 0 or energy > 0 or carbon > 0:
+            recommendations = generate_recommendations(water, energy, carbon)
+            st.markdown(f"""
+            <div class="highlight">
+            <h2>Recommendations to Lower Environmental Impacts</h2>
+            <p>{recommendations}</p>
+            </div>
+            """, unsafe_allow_html=True)
+else:
+    st.error("Failed to load dataset.")