Update app.py

app.py

@@ -1,59 +1,173 @@
-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():
+    background_url = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/BCK2.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;
+    }}
+    </style>
+    """
+    st.markdown(css, unsafe_allow_html=True)
 
-# Groq API Initialization
-
-# Initialize the Groq client with the API key
-client = Groq(api_key="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")
-
-
+# Set page configuration
+st.set_page_config(page_title="GreenLens AI", layout="wide")
 
-def analyze_results_with_groq(results, mode="single"):
-    """
-    Interacts with Groq API to analyze results and provide suggestions.
-    Args:
-        results (dict): The results containing water, energy, and carbon footprints.
-        mode (str): Either 'single' or 'comparison' analysis mode.
+# Call the background function
+add_background()
 
-    Returns:
-        str: Suggestions or description of the results.
+# App title and subtitle
+st.markdown("<h1 style='text-align: center; color: white;'>GreenLens AI</h1>", unsafe_allow_html=True)
+st.markdown(
     """
-    # Prepare the message content for Groq API
-    if mode == "single":
-        message_content = (
-            f"Analyze the sustainability results for a single assessment: "
-            f"Water: {results['water']:.2f} kL, Energy: {results['energy']:.2f} MJ, "
-            f"Carbon: {results['carbon']:.2f} kg CO2e. Suggest alternative materials for better results."
-        )
-    else:
-        message_content = (
-            f"Compare two sustainability assessments:\n"
-            f"Assessment 1 - Water: {results['water1']:.2f} kL, Energy: {results['energy1']:.2f} MJ, "
-            f"Carbon: {results['carbon1']:.2f} kg CO2e.\n"
-            f"Assessment 2 - Water: {results['water2']:.2f} kL, Energy: {results['energy2']:.2f} MJ, "
-            f"Carbon: {results['carbon2']:.2f} kg CO2e.\n"
-            f"Provide a brief analysis and suggest alternative materials if possible."
-        )
-
-    # Make a call to the Groq API
-    chat_completion = client.chat.completions.create(
-        messages=[{"role": "user", "content": message_content}],
-        model="llama-3.3-70b-versatile",
+    <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,
+)
+
+# Dataset URL
+DATASET_URL = "https://huggingface.co/spaces/ZainMalik0925/GreenLensAI_LCA/resolve/main/DataSet01.xlsx"
+
+# Load dataset from Hugging Face Spaces
+@st.cache_data
+def process_dataset(url):
+    try:
+        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")
+        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")
+        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
+
+# 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")),
     )
+    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."
+        )
 
-    # Return the content of the response
-    return chat_completion.choices[0].message.content
+        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}"
 
-# Other code remains unchanged...
-# Load dataset, calculate footprints, and inputs...
+# 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)
@@ -67,10 +181,13 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         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)
+        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)
 
         # Bar chart comparison
         comparison_data = pd.DataFrame({
@@ -80,27 +197,26 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         })
         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"
+            x="Footprint Type",
+            y="Value",
+            color="Assessment",
+            title="Comparison of Assessments"
         )
         st.plotly_chart(style_figure(fig))
-
-        # Use Groq to analyze comparative results
-        results = {"water1": water1, "energy1": energy1, "carbon1": carbon1,
-                   "water2": water2, "energy2": energy2, "carbon2": carbon2}
-        suggestion = analyze_results_with_groq(results, mode="comparison")
-        st.markdown(f"<div class='highlight'><h3>Groq Analysis</h3><p>{suggestion}</p></div>", unsafe_allow_html=True)
-
     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)
+        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)
 
         # Bar chart for single assessment
         result_data = pd.DataFrame({
@@ -110,10 +226,14 @@ if fiber_impact_data and transport_impact_data and washing_impact_data:
         fig = px.bar(result_data, x="Footprint Type", y="Value", title="Single Assessment Footprint Breakdown")
         st.plotly_chart(style_figure(fig))
 
-        # Use Groq to analyze single assessment results
-        results = {"water": water, "energy": energy, "carbon": carbon}
-        suggestion = analyze_results_with_groq(results, mode="single")
-        st.markdown(f"<div class='highlight'><h3>Groq Analysis</h3><p>{suggestion}</p></div>", unsafe_allow_html=True)
-
+        # 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.")
+    st.error("Failed to load dataset.")