Update app.py

app.py

@@ -17,49 +17,53 @@ st.markdown(
     """, unsafe_allow_html=True)
 
 # Google Drive Dataset Link
-DATASET_URL = "https://docs.google.com/uc?export=download&id=1CfuCrqRvu1LjBVmJh9b7mZiHBJ2kvJ0t"
+DATASET_URL = "https://docs.google.com/uc?export=download&id=1K9elWl_s6lMlzY3JbPqKliNHBZ8n4BOr"
 
-# Step 1: Function to fetch and process the Excel dataset
+# Step 1: Function to fetch and process Excel dataset
 @st.cache_data
 def fetch_and_process_excel(url):
-    """Fetch and extract required data from the Excel dataset."""
+    """Fetch and extract data from the updated .xlsx file."""
     try:
         st.info("Fetching dataset from Google Drive...")
-        
-        # Download the file
+
+        # Download the Excel 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
+        # Load the Excel file
         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)
+
+        # Read all sheets into pandas DataFrames
+        fiber_data = pd.read_excel(excel_content, sheet_name="Fiber Impact Data")
+        transport_data = pd.read_excel(excel_content, sheet_name="Transport Data")
+        washing_data = pd.read_excel(excel_content, sheet_name="Washing Data")
+
+        # Convert Fiber Impact Data into a dictionary
+        fiber_impact_data = fiber_data.set_index("Fiber Type")[["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]].to_dict(orient="index")
+
+        # Convert Transport Data into a dictionary for dynamic lookup
+        transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
+
+        # Convert Washing Data into a dictionary for dynamic lookup
+        washing_impact_data = washing_data.set_index("Washing Temperature")[
+            ["Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"]
+        ].to_dict(orient="index")
+
+        # Debugging: Log extracted data for verification
+        st.write("Fiber Impact Data:", fiber_impact_data)
+        st.write("Transport Impact Data:", transport_impact_data)
+        st.write("Washing Impact Data:", washing_impact_data)
+
         st.success("Dataset loaded successfully!")
-        return fiber_impact_data
+        return fiber_impact_data, transport_impact_data, washing_impact_data
 
     except Exception as e:
         st.error(f"Error loading or processing the Excel file: {e}")
-        return None
+        return None, None, None
 
-# Load dataset dynamically
-fiber_impact_data = fetch_and_process_excel(DATASET_URL)
+# Load data dynamically from Google Drive
+fiber_impact_data, transport_impact_data, washing_impact_data = fetch_and_process_excel(DATASET_URL)
 
 # Sidebar for User Inputs
 st.sidebar.header("Input Product Details")
@@ -81,23 +85,20 @@ total_percentage = cotton_percent + polyester_percent + nylon_percent + acrylic_
 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
+st.sidebar.write(f"Total Material Percentage: {total_percentage}%")
 
 # 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_mode = st.sidebar.selectbox("Transport Mode", list(transport_impact_data.keys()))
 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
@@ -108,31 +109,26 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
             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
+                water_footprint += data["Water (L/kg)"] * weight * fraction
+                energy_footprint += data["Energy (MJ/kg)"] * weight * fraction
+                carbon_footprint += data["Carbon (kg CO2e/kg)"] * 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)
+        transport_factor = transport_impact_data.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)
+        washing_data = washing_impact_data.get(lifecycle_inputs["washing_temperature"], {})
+        washing_energy = washing_data.get("Energy Use (MJ/wash)", 0)
+        washing_carbon = washing_data.get("Carbon (kg CO2e/wash)", 0)
+        dryer_carbon = washing_data.get("Dryer CFP (kg CO2e/cycle)", 0) if lifecycle_inputs["use_dryer"] else 0
+
+        carbon_footprint += washing_carbon * lifecycle_inputs["washing_cycles"]
+        energy_footprint += washing_energy * lifecycle_inputs["washing_cycles"]
+        carbon_footprint += dryer_carbon * lifecycle_inputs["washing_cycles"]
+
         return water_footprint, energy_footprint, carbon_footprint
 
     except Exception as e: