Update app.py

app.py

@@ -1,198 +1,72 @@
 import streamlit as st
 import pandas as pd
 import requests
-from io import BytesIO
-from PyPDF2 import PdfReader
-import plotly.graph_objects as go
-
-# 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://drive.google.com/file/d/1JMECXBOPU5UD9hdEUA0uv1g_Qm1CkWYn/view?usp=drive_link"
+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
 
-# Step 1: Function to fetch and process PDF dataset
-@st.cache_data
-def fetch_and_process_pdf(url):
-    """Fetch and extract required data from the PDF dataset."""
-    try:
-        # Log start of fetching
-        st.info("Fetching dataset from Google Drive...")
+# 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.")
         
-        # Download the file
-        response = requests.get(url, stream=True)
-        if response.status_code != 200:
-            raise Exception("Failed to download the file. Check the URL or permissions.")
+        # User Input for LCA variables
+        st.subheader("Input Parameters")
         
-        pdf_content = BytesIO(response.content)
-
-        # Parse the PDF content
-        st.info("Processing the PDF file...")
-        pdf_reader = PdfReader(pdf_content)
-        pdf_text = ""
-        for page in pdf_reader.pages:
-            pdf_text += page.extract_text().strip()  # Extract and clean text
-
-        # Log extracted text for debugging
-        st.write("Extracted Text from PDF:", pdf_text[:500])  # Show first 500 characters for verification
-
-        # Extract necessary data from the text
-        # Expected format: "FIBER_NAME, WATER (L/kg), ENERGY (MJ/kg), CARBON (kgCO2e/kg)"
-        lines = pdf_text.split("\n")
-        fiber_impact_data = {}
-        for line in lines:
-            parts = line.split(",")
-            if len(parts) == 4:  # Ensure proper format
-                fiber, water, energy, carbon = map(str.strip, parts)
-                try:
-                    fiber_impact_data[fiber] = {
-                        "Water": float(water),
-                        "Energy": float(energy),
-                        "Carbon": float(carbon),
-                    }
-                except ValueError:
-                    st.warning(f"Invalid data format in line: {line}")
-                    continue
-
-        # Log parsed dataset
-        st.write("Parsed Fiber Impact Data:", fiber_impact_data)
-        return fiber_impact_data
-
-    except Exception as e:
-        st.error(f"Error loading or processing the PDF: {e}")
-        return None
-
-# Load dataset dynamically
-fiber_impact_data = fetch_and_process_pdf(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%.")
+        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
+        }
+        
+        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()