Update app.py

app.py

@@ -1,72 +1,185 @@
 import streamlit as st
 import pandas as pd
 import requests
-import io
-
-# Function to load the data from the Google Sheets link
-def load_data_from_google_sheets(sheet_url):
-    sheet_id = sheet_url.split('/d/')[1].split('/')[0]
-    export_url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=xlsx"
-    
-    # Downloading the sheet content
-    response = requests.get(export_url)
-    
-    if response.status_code == 200:
-        file = io.BytesIO(response.content)
-        data = pd.read_excel(file)
-        return data
-    else:
-        st.error("Failed to load the dataset. Please check the link.")
-        return None
+from io import BytesIO
+import plotly.graph_objects as go
 
-# Function to calculate CFP, Energy Footprints, Water Footprints, and Ecological Footprints
-def calculate_footprints(data, user_inputs):
-    # Logic for calculation based on user inputs and loaded dataset
-    # For the sake of example, let's assume you are calculating the CFP for the user's input
-    # Here, we can write the necessary calculations as per the LCA logic
-    cfp = user_inputs['carbon_emission'] * data['emission_factor'].mean()  # Example calculation
-    energy_fp = user_inputs['energy_consumed'] * data['energy_factor'].mean()
-    water_fp = user_inputs['water_used'] * data['water_factor'].mean()
-    ecological_fp = user_inputs['ecological_impact'] * data['ecological_factor'].mean()
-
-    return cfp, energy_fp, water_fp, ecological_fp
-
-# Streamlit App Logic
-def main():
-    st.title("Life Cycle Assessment (LCA) Calculator")
-    
-    # Load the dataset from the provided link
-    sheet_url = "https://docs.google.com/spreadsheets/d/1CfuCrqRvu1LjBVmJh9b7mZiHBJ2kvJ0t/edit?usp=drive_link&ouid=108672166866225546443&rtpof=true&sd=true"
-    data = load_data_from_google_sheets(sheet_url)
-    
-    if data is not None:
-        st.write("Dataset loaded successfully.")
-        
-        # User Input for LCA variables
-        st.subheader("Input Parameters")
-        
-        carbon_emission = st.number_input("Carbon Emission (kg CO2)", min_value=0.0)
-        energy_consumed = st.number_input("Energy Consumed (kWh)", min_value=0.0)
-        water_used = st.number_input("Water Used (liters)", min_value=0.0)
-        ecological_impact = st.number_input("Ecological Impact (units)", min_value=0.0)
-        
-        user_inputs = {
-            "carbon_emission": carbon_emission,
-            "energy_consumed": energy_consumed,
-            "water_used": water_used,
-            "ecological_impact": ecological_impact
-        }
+# Set Page Configurations
+st.set_page_config(page_title="GreenLens-AI", layout="wide")
+
+# Page title and description
+st.markdown("<h1 style='text-align: center; color: #4CAF50;'>GreenLens-AI</h1>", unsafe_allow_html=True)
+st.markdown(
+    """
+    <p style='text-align: center; color: #4CAF50; font-size: 18px;'>
+    A Comprehensive Tool for Calculating Water, Energy, and Carbon Footprints of Textile Products 🌍
+    </p>
+    """, unsafe_allow_html=True)
+
+# Google Drive Dataset Link
+DATASET_URL = "https://docs.google.com/uc?export=download&id=1CfuCrqRvu1LjBVmJh9b7mZiHBJ2kvJ0t"
+
+# Step 1: Function to fetch and process the Excel dataset
+@st.cache_data
+def fetch_and_process_excel(url):
+    """Fetch and extract required data from the Excel dataset."""
+    try:
+        st.info("Fetching dataset from Google Drive...")
         
-        if st.button("Calculate LCA"):
-            # Perform LCA calculations
-            cfp, energy_fp, water_fp, ecological_fp = calculate_footprints(data, user_inputs)
-            
-            # Display results
-            st.subheader("Results")
-            st.write(f"Total Carbon Footprint (CFP): {cfp} kg CO2")
-            st.write(f"Total Energy Footprint: {energy_fp} kWh")
-            st.write(f"Total Water Footprint: {water_fp} liters")
-            st.write(f"Total Ecological Footprint: {ecological_fp} units")
-    
-if __name__ == "__main__":
-    main()
+        # Download the file
+        response = requests.get(url)
+        if response.status_code != 200:
+            raise Exception("Failed to download the file. Check the URL or permissions.")
+
+        # Read the Excel file into a pandas DataFrame
+        excel_content = BytesIO(response.content)
+        df = pd.read_excel(excel_content)
+
+        # Convert DataFrame into a dictionary for fiber impact data
+        # Assuming the Excel file has columns: "Fiber", "Water", "Energy", "Carbon"
+        if not all(col in df.columns for col in ["Fiber", "Water", "Energy", "Carbon"]):
+            raise Exception("Excel file does not have the required columns: Fiber, Water, Energy, Carbon.")
+
+        # Build the dictionary
+        fiber_impact_data = {}
+        for _, row in df.iterrows():
+            fiber_impact_data[row["Fiber"]] = {
+                "Water": row["Water"],
+                "Energy": row["Energy"],
+                "Carbon": row["Carbon"],
+            }
+
+        # Log the parsed dataset
+        st.write("Parsed Fiber Impact Data:", fiber_impact_data)
+        st.success("Dataset loaded successfully!")
+        return fiber_impact_data
+
+    except Exception as e:
+        st.error(f"Error loading or processing the Excel file: {e}")
+        return None
+
+# Load dataset dynamically
+fiber_impact_data = fetch_and_process_excel(DATASET_URL)
+
+# Sidebar for User Inputs
+st.sidebar.header("Input Product Details")
+
+# Input Section: Product-Specific Inputs
+product_type = st.sidebar.selectbox("Product Type", ["T-shirt", "Jeans", "Shirt", "Carpet"])
+product_weight = st.sidebar.number_input("Product Weight (kg)", min_value=0.01, step=0.01, value=0.25)
+
+# Fiber Composition Input
+st.sidebar.subheader("Material Composition (%)")
+cotton_percent = st.sidebar.slider("Cotton (%)", 0, 100, 50)
+polyester_percent = st.sidebar.slider("Polyester (%)", 0, 100, 30)
+nylon_percent = st.sidebar.slider("Nylon (%)", 0, 100, 10)
+acrylic_percent = st.sidebar.slider("Acrylic (%)", 0, 100, 5)
+viscose_percent = st.sidebar.slider("Viscose (%)", 0, 100, 5)
+
+# Validation check: Percentages must add up to 100%
+total_percentage = cotton_percent + polyester_percent + nylon_percent + acrylic_percent + viscose_percent
+if total_percentage != 100:
+    st.sidebar.error("The total of all fiber percentages must equal 100%!")
+
+st.sidebar.write(f"Total Material Percentage: {total_percentage}%")  # Debugging sidebar inputs
+
+# Lifecycle Inputs
+st.sidebar.header("Lifecycle Details")
+washing_cycles = st.sidebar.number_input("Number of Washing Cycles", min_value=0, step=10, value=30)
+washing_temperature = st.sidebar.selectbox("Washing Temperature", ["Cold", "30°C", "40°C", "60°C"])
+use_dryer = st.sidebar.checkbox("Use Tumble Dryer?")
+transport_mode = st.sidebar.selectbox("Transport Mode", ["Plane", "Ship", "Train", "Truck"])
+transport_distance = st.sidebar.number_input("Transport Distance (km)", min_value=0, step=50)
+
+# Function to calculate footprints
+def calculate_footprints(weight, composition, lifecycle_inputs):
+    """Calculate water, energy, and carbon footprints."""
+    try:
+        # Log inputs
+        st.write("Inputs to function:", weight, composition, lifecycle_inputs)
+
+        # Initialize footprints
+        water_footprint = 0
+        energy_footprint = 0
+        carbon_footprint = 0
+
+        # Fiber contributions
+        for fiber, percentage in composition.items():
+            if fiber in fiber_impact_data:
+                data = fiber_impact_data[fiber]
+                fraction = percentage / 100
+                water_footprint += data["Water"] * weight * fraction
+                energy_footprint += data["Energy"] * weight * fraction
+                carbon_footprint += data["Carbon"] * weight * fraction
+            else:
+                st.warning(f"No data found for fiber: {fiber}")
+
+        # Transportation impacts
+        transport_factor = {
+            "Plane": 1.102,
+            "Ship": 0.011,
+            "Train": 0.05,
+            "Truck": 0.25,
+        }.get(lifecycle_inputs["transport_mode"], 0)
+        carbon_footprint += transport_factor * lifecycle_inputs["transport_distance"] * weight
+
+        # Washing and drying impacts
+        washing_energy = {"Cold": 0.02, "30°C": 0.1, "40°C": 0.2, "60°C": 0.5}
+        dryer_energy = 0.5 if lifecycle_inputs["use_dryer"] else 0
+        carbon_footprint += (
+            washing_energy[lifecycle_inputs["washing_temperature"]] * lifecycle_inputs["washing_cycles"] * 0.05
+        )
+        energy_footprint += dryer_energy * lifecycle_inputs["washing_cycles"]
+
+        # Log final results
+        st.write("Calculated Results:", water_footprint, energy_footprint, carbon_footprint)
+        return water_footprint, energy_footprint, carbon_footprint
+
+    except Exception as e:
+        st.error(f"Error in calculations: {e}")
+        return None, None, None
+
+# User inputs as a dictionary
+user_inputs = {
+    "transport_mode": transport_mode,
+    "transport_distance": transport_distance,
+    "washing_temperature": washing_temperature,
+    "washing_cycles": washing_cycles,
+    "use_dryer": use_dryer,
+}
+
+# Collect the composition dictionary
+composition = {
+    "Cotton": cotton_percent,
+    "Polyester": polyester_percent,
+    "Nylon": nylon_percent,
+    "Acrylic": acrylic_percent,
+    "Viscose": viscose_percent,
+}
+
+# Run Calculations if conditions are met
+if fiber_impact_data and total_percentage == 100:
+    water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition, user_inputs)
+
+    if water_fp is not None:
+        # Display results
+        st.subheader("Calculated Results")
+        st.markdown(f"""
+        - **Water Footprint**: {water_fp:.2f} liters  
+        - **Energy Footprint**: {energy_fp:.2f} MJ  
+        - **Carbon Footprint**: {carbon_fp:.2f} kgCO2e  
+        """)
+
+        # Visualization
+        fig = go.Figure()
+        fig.add_trace(go.Bar(
+            x=["Water Footprint", "Energy Footprint", "Carbon Footprint"],
+            y=[water_fp, energy_fp, carbon_fp],
+            text=[f"{water_fp:.2f} L", f"{energy_fp:.2f} MJ", f"{carbon_fp:.2f} kgCO2e"],
+            textposition="auto",
+            marker=dict(color=["blue", "orange", "green"])
+        ))
+        fig.update_layout(title="Footprint Breakdown", xaxis_title="Footprint Type", yaxis_title="Value")
+        st.plotly_chart(fig)
+else:
+    st.error("Ensure dataset is loaded and the composition sums to 100%.")