Update app.py

app.py

@@ -14,15 +14,17 @@ 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)
+    """,
+    unsafe_allow_html=True,
+)
 
 # Google Drive Dataset Link
-DATASET_URL = "https://docs.google.com/uc?export=download&id=1K9elWl_s6lMlzY3JbPqKliNHBZ8n4BOr"
+DATASET_URL = "https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/23453236/59122b29-51ec-4aab-8b41-5301f001a94b/DataSet01.xlsx"
 
-# Step 1: Function to fetch and process Excel dataset
+# Function to fetch and process Excel dataset
 @st.cache_data
 def fetch_and_process_excel(url):
-    """Fetch and extract data from the updated .xlsx file."""
+    """Fetch and extract data from the provided .xlsx file."""
     try:
         st.info("Fetching dataset from Google Drive...")
 
@@ -31,30 +33,37 @@ def fetch_and_process_excel(url):
         if response.status_code != 200:
             raise Exception("Failed to download the file. Check the URL or permissions.")
 
-        # Load the Excel file
+        # Load the Excel file into a BytesIO object
         excel_content = BytesIO(response.content)
 
-        # Read all sheets into pandas DataFrames
+        # Read all relevant 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")
+        transport_data = pd.read_excel(excel_content, sheet_name="Transport Impact 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
+        # Verify required columns exist in all sheets
+        if not {"Fiber Type", "Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"}.issubset(fiber_data.columns):
+            raise Exception("Required columns missing in 'Fiber Impact Data'.")
+        if not {"Transport Mode", "CFP (kg CO2e/km)"}.issubset(transport_data.columns):
+            raise Exception("Required columns missing in 'Transport Impact Data'.")
+        if not {"Washing Temperature", "Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"}.issubset(
+            washing_data.columns
+        ):
+            raise Exception("Required columns missing in 'Washing Data'.")
+
+        # Process Fiber Impact Data
+        fiber_impact_data = fiber_data.set_index("Fiber Type")[["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]].to_dict(
+            orient="index"
+        )
+
+        # Process Transport Impact Data
         transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
 
-        # Convert Washing Data into a dictionary for dynamic lookup
+        # Process Washing Data
         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, transport_impact_data, washing_impact_data
 
@@ -62,13 +71,13 @@ def fetch_and_process_excel(url):
         st.error(f"Error loading or processing the Excel file: {e}")
         return None, None, None
 
+
 # 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")
 
-# 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)
 
@@ -90,9 +99,9 @@ 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"])
+washing_temperature = st.sidebar.selectbox("Washing Temperature", list(washing_impact_data.keys()) if washing_impact_data else [])
 use_dryer = st.sidebar.checkbox("Use Tumble Dryer?")
-transport_mode = st.sidebar.selectbox("Transport Mode", list(transport_impact_data.keys()))
+transport_mode = st.sidebar.selectbox("Transport Mode", list(transport_impact_data.keys()) if transport_impact_data else [])
 transport_distance = st.sidebar.number_input("Transport Distance (km)", min_value=0, step=50)
 
 # Function to calculate footprints
@@ -112,8 +121,6 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
                 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 = transport_impact_data.get(lifecycle_inputs["transport_mode"], 0)
@@ -121,13 +128,12 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
 
         # Washing and drying impacts
         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)
+        washing_energy = washing_data.get("Energy Use (MJ/wash)", 0) * lifecycle_inputs["washing_cycles"]
+        washing_carbon = washing_data.get("Carbon (kg CO2e/wash)", 0) * lifecycle_inputs["washing_cycles"]
         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"]
+        energy_footprint += washing_energy
+        carbon_footprint += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])
 
         return water_footprint, energy_footprint, carbon_footprint
 
@@ -135,6 +141,7 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
         st.error(f"Error in calculations: {e}")
         return None, None, None
 
+
 # User inputs as a dictionary
 user_inputs = {
     "transport_mode": transport_mode,
@@ -155,27 +162,4 @@ composition = {
 
 # 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%.")
+    water_fp, energy_fp, carb