Update app.py

app.py

@@ -1,10 +1,8 @@
-from groq import Groq
-import os
 import streamlit as st
 import pandas as pd
 import plotly.express as px
 
-# Add custom CSS for the app background and recommendations styling
+# Add custom CSS for the app background and highlighted text
 def add_background():
     background_url = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/BCK2.jpg"
     css = f"""
@@ -16,19 +14,12 @@ def add_background():
         background-attachment: fixed;
     }}
     .highlight {{
-        background-color: rgba(27, 27, 27, 0.8); /* 80% opaque black */
+        background-color: rgba(27, 27, 27, 0.7); /* 70% opaque black */
         padding: 10px;
         border-radius: 5px;
         margin-bottom: 15px;
         color: white;
     }}
-    .recommendation {{
-        background-color: rgba(27, 27, 27, 0.8); /* 80% opaque black */
-        padding: 10px;
-        border-radius: 5px;
-        margin-top: 15px;
-        color: white;
-    }}
     </style>
     """
     st.markdown(css, unsafe_allow_html=True)
@@ -71,49 +62,91 @@ def process_dataset(url):
         st.error(f"Error loading dataset: {e}")
         return None, None, None
 
-# Initialize Groq API client
-client = Groq(
-    api_key="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"
-)
-
-# Function to get concise 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"Given water usage: {water_fp:.2f} kL, energy use: {energy_fp:.2f} MJ, and carbon emissions: {carbon_fp:.2f} kg CO2e, provide concise recommendations to reduce these impacts."
-            }
-        ],
-        model="llama-3.3-70b-versatile",
+# Calculate footprints
+def calculate_footprints(weight, composition, lifecycle_inputs):
+    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_fp += data["Water (L/kg)"] * weight * fraction
+            energy_fp += data["Energy (MJ/kg)"] * weight * fraction
+            carbon_fp += data["Carbon (kg CO2e/kg)"] * weight * fraction
+
+    # Add transport impact
+    if lifecycle_inputs["transport_mode"] in transport_impact_data:
+        carbon_fp += transport_impact_data[lifecycle_inputs["transport_mode"]] * lifecycle_inputs["transport_distance"] * weight
+
+    # Add washing impact
+    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"])
+
+    # Convert water from liters to kiloliters
+    water_fp /= 1000
+    return water_fp, energy_fp, carbon_fp
+
+# Sidebar inputs
+def get_inputs(prefix):
+    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: white;'>{prefix} Material Composition (%)</h3>", unsafe_allow_html=True)
+    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%!")
+
+    st.sidebar.markdown(f"<h3 style='color: white;'>{prefix} Transport Inputs</h3>", unsafe_allow_html=True)
+    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=0, step=10, key=f"{prefix}_transport_distance")
+
+    lifecycle_inputs = {
+        "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": transport_mode,
+        "transport_distance": transport_distance,
+    }
+
+    composition = {
+        "Conventional Cotton": cotton,
+        "Polyester": polyester,
+        "Nylon 6": nylon,
+        "Acrylic": acrylic,
+        "Viscose": viscose,
+    }
+    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")),
     )
-    return chat_completion.choices[0].message.content
-
-# Display recommendations with specified styling
-def display_recommendations(recommendations):
-    if recommendations:
-        st.markdown(
-            f"""
-            <div class="recommendation">
-            <h3>Recommendations</h3>
-            <p>{recommendations}</p>
-            </div>
-            """,
-            unsafe_allow_html=True,
-        )
+    fig.update_traces(marker=dict(color="white", line=dict(color="gray", width=1)))  # Simulate 3D effect with border
+    return fig
 
-# Sidebar inputs and calculations remain unchanged
-# ...
+# Main application logic
+fiber_impact_data, transport_impact_data, washing_impact_data = process_dataset(DATASET_URL)
 
-# Main application logic: Ensure all recommendations are displayed with correct styling
 if fiber_impact_data and transport_impact_data and washing_impact_data:
     comparison_mode = st.sidebar.checkbox("Enable Comparison Mode")
 
     if comparison_mode:
-        # Comparison mode (for two assessments)
+        # Input for two assessments
         col1, col2 = st.columns(2)
         with col1:
             weight1, composition1, lifecycle1 = get_inputs("Assessment 1")
@@ -124,10 +157,7 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1)
         water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2)
 
-        # Get and display recommendations
-        recommendations1 = get_groq_recommendations(water1, energy1, carbon1)
-        recommendations2 = get_groq_recommendations(water2, energy2, carbon2)
-
+        # Display numerical comparison
         st.markdown(f"""
         <div class="highlight">
         <h2>Numerical Comparison</h2>
@@ -136,19 +166,27 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         </div>
         """, unsafe_allow_html=True)
 
-        if recommendations1:
-            display_recommendations(recommendations1)
-        if recommendations2:
-            display_recommendations(recommendations2)
+        # 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],
+        })
+        fig = px.bar(
+            comparison_data.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"),
+            x="Footprint Type",
+            y="Value",
+            color_discrete_sequence=["white"],
+            title="Comparison of Assessments"
+        )
+        st.plotly_chart(style_figure(fig))
 
     else:
-        # Single assessment mode
+        # Input for a single assessment
         weight, composition, lifecycle = get_inputs("Single")
         water, energy, carbon = calculate_footprints(weight, composition, lifecycle)
 
-        # Get and display recommendations
-        recommendations = get_groq_recommendations(water, energy, carbon)
-
+        # Display results
         st.markdown(f"""
         <div class="highlight">
         <h2>Single Assessment Results</h2>
@@ -158,8 +196,12 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         </div>
         """, unsafe_allow_html=True)
 
-        if recommendations:
-            display_recommendations(recommendations)
-
+        # 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="Single Assessment Footprint Breakdown")
+        st.plotly_chart(style_figure(fig))
 else:
     st.error("Failed to load dataset.")