Update app.py

app.py

@@ -1,39 +1,43 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
+import os
+from groq import Groq
 
-# Add custom CSS for the background and highlighted text
+# Add custom CSS for the app background and highlighted text
 def add_background():
     background_url = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/BKG03.jpg"
     css = f"""
     <style>
-        .stApp {{
-            background-image: url("{background_url}");
-            background-size: cover;
-            background-position: center;
-            background-attachment: fixed;
-        }}
-        .highlight {{
-            background-color: rgba(27, 27, 27, 0.7); /* 70% opaque black */
-            padding: 10px;
-            border-radius: 5px;
-            margin-bottom: 15px;
-            color: white;
-        }}
+    .stApp {{
+        background-image: url("{background_url}");
+        background-size: cover;
+        background-position: center;
+        background-attachment: fixed;
+    }}
+    .highlight {{
+        background-color: rgba(27, 27, 27, 0.7); /* 70% opaque black */
+        padding: 10px;
+        border-radius: 5px;
+        margin-bottom: 15px;
+        color: white;
+    }}
     </style>
     """
     st.markdown(css, unsafe_allow_html=True)
 
-# Set up page configuration
+# Set page configuration
 st.set_page_config(page_title="GreenLens AI", layout="wide")
+
+# Call the background function
 add_background()
 
 # App title and subtitle
 st.markdown("<h1 style='text-align: center; color: white;'>GreenLens AI</h1>", unsafe_allow_html=True)
 st.markdown(
     """
-    <p style='text-align: center; color: white; font-size: 18px;'> 
-    A Comprehensive Tool for Assessing Water, Energy, and Carbon Footprints of Textile Products 🌍 
+    <p style='text-align: center; color: white; font-size: 18px;'>
+    A Comprehensive Tool for Assessing Water, Energy, and Carbon Footprints of Textile Products 🌍
     </p>
     """,
     unsafe_allow_html=True,
@@ -46,122 +50,190 @@ DATASET_URL = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resol
 @st.cache_data
 def process_dataset(url):
     try:
-        # Read the Excel file
         excel_content = pd.ExcelFile(url)
         fiber_data = pd.read_excel(excel_content, sheet_name="Fiber Impact Data")
         transport_data = pd.read_excel(excel_content, sheet_name="Transport Impact Data")
         washing_data = pd.read_excel(excel_content, sheet_name="Washing Data")
 
-        # Convert data into dictionaries for calculations
-        fiber_impact_data = fiber_data.set_index("Fiber Type")[
-            ["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]
-        ].to_dict(orient="index")
+        # Convert data to dictionaries for calculations
+        fiber_impact_data = fiber_data.set_index("Fiber Type")[["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]].to_dict(orient="index")
         transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
-        washing_impact_data = washing_data.set_index("Washing Temperature")[
-            ["Water (L/kg)", "Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"]
-        ].to_dict(orient="index")
-
+        washing_impact_data = washing_data.set_index("Washing Temperature")[["Water (L/kg)", "Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"]].to_dict(orient="index")
         return fiber_impact_data, transport_impact_data, washing_impact_data
     except Exception as e:
         st.error(f"Error loading dataset: {e}")
         return None, None, None
 
 # Calculate footprints
-def calculate_footprints(weight, composition, lifecycle_inputs, fiber_data, transport_data, washing_data):
-    try:
-        water_fp, energy_fp, carbon_fp = 0.0, 0.0, 0.0
-
-        # Fiber impacts
-        for fiber, percentage in composition.items():
-            if fiber in fiber_data:
-                data = fiber_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
-
-        # Transportation impacts
-        if lifecycle_inputs["transport_mode"] in transport_data:
-            distance = lifecycle_inputs["transport_distance"]
-            carbon_fp += transport_data[lifecycle_inputs["transport_mode"]] * distance * weight
-
-        # Washing impacts
-        if lifecycle_inputs["washing_temperature"] in washing_data:
-            wash_data = washing_data[lifecycle_inputs["washing_temperature"]]
-            washing_cycles = lifecycle_inputs["washing_cycles"]
-            water_fp += wash_data["Water (L/kg)"] * washing_cycles
-            energy_fp += wash_data["Energy Use (MJ/wash)"] * washing_cycles
-            carbon_fp += wash_data["Carbon (kg CO2e/wash)"] * washing_cycles
-
-            if lifecycle_inputs["use_dryer"]:
-                carbon_fp += wash_data["Dryer CFP (kg CO2e/cycle)"] * washing_cycles
-
-        # Convert water from liters to kiloliters
-        water_fp /= 1000
-
-        return water_fp, energy_fp, carbon_fp
-    except Exception as e:
-        st.error(f"Error calculating footprints: {e}")
-        return 0.0, 0.0, 0.0
+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():
-    weight = st.sidebar.number_input("Product Weight (kg)", min_value=0.0, value=1.0, step=0.1)
-    st.sidebar.text("Material Composition (%)")
-    materials = {
-        "Conventional Cotton": st.sidebar.number_input("Cotton (%)", 0, 100, 50),
-        "Polyester": st.sidebar.number_input("Polyester (%)", 0, 100, 50),
-        "Nylon 6": st.sidebar.number_input("Nylon (%)", 0, 100, 0),
-        "Acrylic": st.sidebar.number_input("Acrylic (%)", 0, 100, 0),
-        "Viscose": st.sidebar.number_input("Viscose (%)", 0, 100, 0),
-    }
-
-    transport_mode = st.sidebar.selectbox("Transport Mode", ["Truck", "Airplane"])
-    distance = st.sidebar.number_input("Transport Distance (km)", min_value=0, value=1000)
-
-    washing_cycles = st.sidebar.number_input("Washing Cycles", min_value=0, value=0)
-    washing_temperature = st.sidebar.selectbox("Washing Temperature", ["Cold", "Warm", "Hot"])
-    use_dryer = st.sidebar.checkbox("Use Dryer?", value=False)
+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: 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")
+    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: green;'>{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": distance,
-        "washing_cycles": washing_cycles,
-        "washing_temperature": washing_temperature,
-        "use_dryer": use_dryer,
+        "transport_distance": transport_distance,
     }
 
-    return weight, materials, lifecycle_inputs
-
-# Load data
-fiber_data, transport_data, washing_data = process_dataset(DATASET_URL)
+    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")),
+    )
+    fig.update_traces(marker=dict(color="white", line=dict(color="gray", width=1)))  # Simulate 3D effect with border
+    return fig
 
-if fiber_data and transport_data and washing_data:
-    # Get user inputs
-    weight, composition, lifecycle = get_inputs()
-    water, energy, carbon = calculate_footprints(weight, composition, lifecycle, fiber_data, transport_data, washing_data)
+# 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)
+
+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
+        col1, col2 = st.columns(2)
+        with col1:
+            weight1, composition1, lifecycle1 = get_inputs("Assessment 1")
+        with col2:
+            weight2, composition2, lifecycle2 = get_inputs("Assessment 2")
+
+        # Calculate footprints for both assessments
+        water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1)
+        water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2)
+
+        # Display numerical comparison
+        st.markdown(f"""
+        <div class="highlight">
+        <h2>Numerical Comparison</h2>
+        <p>Assessment 1: Water: {water1:.2f} kL, Energy: {energy1:.2f} MJ, Carbon: {carbon1:.2f} kg CO2e</p>
+        <p>Assessment 2: Water: {water2:.2f} kL, Energy: {energy2:.2f} MJ, Carbon: {carbon2:.2f} kg CO2e</p>
+        </div>
+        """, unsafe_allow_html=True)
 
-    # Display numerical results
-    st.markdown(
-        f"""
-        <div class='highlight'>
-            <h2>Results</h2>
-            <p>Water Footprint: {water:.2f} kL</p>
-            <p>Energy Footprint: {energy:.2f} MJ</p>
-            <p>Carbon Footprint: {carbon:.2f} kg CO2e</p>
+        # 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="Assessment",
+            title="Comparison of Assessments"
+        )
+        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)
+
+        # Display results
+        st.markdown(f"""
+        <div class="highlight">
+        <h2>Single Assessment Results</h2>
+        <p>Water Footprint: {water:.2f} kL</p>
+        <p>Energy Footprint: {energy:.2f} MJ</p>
+        <p>Carbon Footprint: {carbon:.2f} kg CO2e</p>
         </div>
-        """,
-        unsafe_allow_html=True,
-    )
+        """, unsafe_allow_html=True)
 
-    # Display bar chart
-    footprint_data = pd.DataFrame(
-        {
+        # Bar chart for single assessment
+        result_data = pd.DataFrame({
             "Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
-            "Values": [water, energy, carbon],
-        }
-    )
-    fig = px.bar(footprint_data, x="Footprint Type", y="Values", title="Environmental Footprint Breakdown")
-    st.plotly_chart(fig)
+            "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))
+
+        # 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 required data.")
+    st.error("Failed to load dataset.")