Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import pandas as pd
-import requests
 from io import BytesIO
 import plotly.graph_objects as go
 
@@ -18,148 +17,163 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-# Google Drive Dataset Link
-DATASET_URL = "https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/23453236/59122b29-51ec-4aab-8b41-5301f001a94b/DataSet01.xlsx"
-
-# Function to fetch and process Excel dataset
+# Function to process Excel dataset
 @st.cache_data
-def fetch_and_process_excel(url):
-    """Fetch and extract data from the provided .xlsx file."""
+def process_excel(file):
+    """Process data from the uploaded .xlsx file."""
     try:
-        st.info("Fetching dataset from Google Drive...")
-
-        # 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.")
+        # Load Excel file
+        excel_content = BytesIO(file.read())
 
-        # Load the Excel file into a BytesIO object
-        excel_content = BytesIO(response.content)
+        # Verify required sheets exist
+        sheets = pd.ExcelFile(excel_content).sheet_names
+        required_sheets = ["Fiber Impact Data", "Transport Impact Data", "Washing Data"]
+        for sheet in required_sheets:
+            if sheet not in sheets:
+                raise Exception(f"Required sheet '{sheet}' is missing from the Excel file.")
 
-        # Read all relevant sheets into pandas DataFrames
+        # Read data from Excel sheets
         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")
 
-        # Verify required columns exist in all sheets
+        # Validate required columns in each sheet
         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'.")
+            raise Exception("Required columns are missing in 'Fiber Impact Data' sheet.")
         if not {"Transport Mode", "CFP (kg CO2e/km)"}.issubset(transport_data.columns):
-            raise Exception("Required columns missing in 'Transport Impact Data'.")
+            raise Exception("Required columns are missing in 'Transport Impact Data' sheet.")
         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'.")
+            raise Exception("Required columns are missing in 'Washing Data' sheet.")
 
-        # Process Fiber Impact Data
+        # Convert data into 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"
         )
-
-        # Process Transport Impact Data
         transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
-
-        # 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")
 
-        st.success("Dataset loaded successfully!")
+        st.success("File successfully uploaded and processed!")
         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, 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")
-
-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}%")
-
-# 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", 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()) if transport_impact_data else [])
-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:
-        # 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 (L/kg)"] * weight * fraction
-                energy_footprint += data["Energy (MJ/kg)"] * weight * fraction
-                carbon_footprint += data["Carbon (kg CO2e/kg)"] * weight * fraction
-
-        # Transportation impacts
-        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_data = washing_impact_data.get(lifecycle_inputs["washing_temperature"], {})
-        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
-
-        energy_footprint += washing_energy
-        carbon_footprint += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])
-
-        return water_footprint, energy_footprint, carbon_footprint
-
-    except Exception as e:
-        st.error(f"Error in calculations: {e}")
+        st.error(f"Error processing the Excel file: {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, carb
+# File uploader in Streamlit sidebar
+uploaded_file = st.sidebar.file_uploader("Upload Dataset to Initiate", type=["xlsx"])
+
+if uploaded_file is not None:
+    # Process the uploaded file
+    fiber_impact_data, transport_impact_data, washing_impact_data = process_excel(uploaded_file)
+
+    # Sidebar for User Inputs
+    st.sidebar.header("Input Product Details")
+
+    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, 100)
+    polyester_percent = st.sidebar.slider("Polyester (%)", 0, 100, 0)
+    nylon_percent = st.sidebar.slider("Nylon (%)", 0, 100, 0)
+    acrylic_percent = st.sidebar.slider("Acrylic (%)", 0, 100, 0)
+    viscose_percent = st.sidebar.slider("Viscose (%)", 0, 100, 0)
+
+    # Validate composition percentages
+    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}%")
+
+    # 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", 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()) if transport_impact_data else [])
+    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:
+            # 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 (L/kg)"] * weight * fraction
+                    energy_footprint += data["Energy (MJ/kg)"] * weight * fraction
+                    carbon_footprint += data["Carbon (kg CO2e/kg)"] * weight * fraction
+
+            # Transportation impacts
+            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_data = washing_impact_data.get(lifecycle_inputs["washing_temperature"], {})
+            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
+
+            energy_footprint += washing_energy
+            carbon_footprint += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])
+
+            return water_footprint, energy_footprint, carbon_footprint
+
+        except Exception as e:
+            st.error(f"Error in calculations: {e}")
+            return None, None, None
+
+    # Composition dictionary
+    composition = {
+        "Cotton": cotton_percent,
+        "Polyester": polyester_percent,
+        "Nylon": nylon_percent,
+        "Acrylic": acrylic_percent,
+        "Viscose": viscose_percent,
+    }
+
+    # Perform calculations
+    if total_percentage == 100:
+        water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition, {
+            "transport_mode": transport_mode,
+            "transport_distance": transport_distance,
+            "washing_temperature": washing_temperature,
+            "washing_cycles": washing_cycles,
+            "use_dryer": use_dryer,
+        })
+
+        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.info("Please upload a valid Dataset to Begin")