Update app.py

app.py

@@ -1,43 +1,39 @@
 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
+# Add custom CSS for the 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 page configuration
+# Set up 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,
@@ -50,190 +46,122 @@ 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 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")
+        # 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")
         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):
-    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
+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
 
 # 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: 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")
+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)
 
     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,
+        "transport_distance": distance,
+        "washing_cycles": washing_cycles,
+        "washing_temperature": washing_temperature,
+        "use_dryer": use_dryer,
     }
 
-    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
+    return weight, materials, lifecycle_inputs
 
-# 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)
+# Load data
+fiber_data, transport_data, washing_data = process_dataset(DATASET_URL)
 
-        # 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>
+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)
+
+    # 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>
         </div>
-        """, unsafe_allow_html=True)
+        """,
+        unsafe_allow_html=True,
+    )
 
-        # Bar chart for single assessment
-        result_data = pd.DataFrame({
+    # Display bar chart
+    footprint_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))
-
-        # 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)
+            "Values": [water, energy, carbon],
+        }
+    )
+    fig = px.bar(footprint_data, x="Footprint Type", y="Values", title="Environmental Footprint Breakdown")
+    st.plotly_chart(fig)
 else:
-    st.error("Failed to load dataset.")
+    st.error("Failed to load required data.")