Update app.py

app.py

@@ -1,76 +1,70 @@
 import streamlit as st
 import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
 import requests
 from io import BytesIO
 from PyPDF2 import PdfReader
-import plotly.graph_objects as go
 import time
 
 # Set Page Configurations
 st.set_page_config(page_title="GreenLens-AI", layout="wide")
-st.markdown("<h1 style='text-align: center; color: #4CAF50;'>GreenLens-AI</h1>", unsafe_allow_html=True)
+
+# Custom Styling for the App
 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)
+    <style>
+    body {
+        background-color: #d4edda; /* Light green background */
+    }
+    .stSlider > div { 
+        background-color: #50C878 !important; /* Green slider bar */
+    }
+    h1, h2, p {
+        text-align: center;
+        color: #4CAF50;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Title and Tagline
+st.markdown("<h1>GreenLens-AI</h1>", unsafe_allow_html=True)
+st.markdown("<p>A Sustainable Tool for Calculating Carbon, Energy, and Ecological Footprints 🌿</p>", unsafe_allow_html=True)
 
 # Google Drive Dataset Link
 DATASET_URL = "https://drive.google.com/uc?id=1QY9yv2mhz4n8bOTi4ahbjBpapltqXV6D"
 
-# Step 1: Function to fetch and process PDF dataset
+# Function to fetch and process dataset
 @st.cache_data
-def fetch_and_process_pdf(url):
-    """Fetch the PDF dataset from Google Drive and process it."""
+def fetch_dataset(url):
+    """Fetch the dataset from Google Drive."""
     progress_text = "Fetching dataset from Google Drive..."
     progress_bar = st.progress(0)
 
     try:
-        # Download the file
         response = requests.get(url, stream=True)
         total_size = int(response.headers.get('content-length', 0))
         downloaded_size = 0
         chunks = []
 
-        # Download with progress
         for chunk in response.iter_content(chunk_size=8192):
             downloaded_size += len(chunk)
             progress_bar.progress(min(1.0, downloaded_size / total_size), text=progress_text)
             chunks.append(chunk)
 
         pdf_content = b"".join(chunks)
-        progress_bar.progress(1.0, text="Processing PDF...")
-
-        # Parse the PDF content
-        pdf_reader = PdfReader(BytesIO(pdf_content))
-        pdf_text = ""
-        for page in pdf_reader.pages:
-            pdf_text += page.extract_text()
-        
-        # Parse relevant data from the text (mock implementation, adjust as needed)
-        fiber_impact_data = {
-            "Cotton": {"Water": 10000, "Energy": 60, "Carbon": 3.18},
-            "Polyester": {"Water": 62, "Energy": 125, "Carbon": 4.8},
-            "Nylon": {"Water": 70, "Energy": 120.47, "Carbon": 5.4},
-            "Acrylic": {"Water": 50, "Energy": 175, "Carbon": 6.2},
-            "Viscose": {"Water": 200, "Energy": 100, "Carbon": 4.2},
-        }
-
-        st.success("Dataset loaded successfully!")
-        return fiber_impact_data
-
+        progress_bar.progress(1.0, text="Processing dataset...")
+        return PdfReader(BytesIO(pdf_content))
     except Exception as e:
-        st.error(f"Error fetching or processing dataset: {e}")
+        st.error(f"Error fetching dataset: {e}")
         return None
 
-# Load dataset dynamically from Google Drive
-fiber_impact_data = fetch_and_process_pdf(DATASET_URL)
+dataset = fetch_dataset(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)
 
@@ -82,10 +76,10 @@ 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%
+# Validate percentage
 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.warning("The total of all fiber percentages must equal 100%!")
 
 # Lifecycle Inputs
 st.sidebar.header("Lifecycle Details")
@@ -95,14 +89,21 @@ 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)
 
+# Mock Fiber Impact Data
+fiber_impact_data = {
+    "Cotton": {"Water": 10000, "Energy": 60, "Carbon": 3.18},
+    "Polyester": {"Water": 62, "Energy": 125, "Carbon": 4.8},
+    "Nylon": {"Water": 70, "Energy": 120.47, "Carbon": 5.4},
+    "Acrylic": {"Water": 50, "Energy": 175, "Carbon": 6.2},
+    "Viscose": {"Water": 200, "Energy": 100, "Carbon": 4.2},
+}
+
 # Function to calculate footprints
 def calculate_footprints(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]
@@ -111,35 +112,13 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
             energy_footprint += data["Energy"] * weight * fraction
             carbon_footprint += data["Carbon"] * weight * fraction
 
-    # Transportation impacts
-    transport_factor = {
-        "Plane": 1.102,
-        "Ship": 0.011,
-        "Train": 0.05,
-        "Truck": 0.25,
-    }[lifecycle_inputs["transport_mode"]]
-    
-    transport_emissions = transport_factor * lifecycle_inputs["transport_distance"] * weight
-    carbon_footprint += transport_emissions
-
-    # 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"]
+    # Transportation footprint
+    transport_factor = {"Plane": 1.102, "Ship": 0.011, "Train": 0.05, "Truck": 0.25}[lifecycle_inputs["transport_mode"]]
+    carbon_footprint += transport_factor * lifecycle_inputs["transport_distance"] * weight
 
     return water_footprint, energy_footprint, carbon_footprint
 
-# 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 dictionary
 composition = {
     "Cotton": cotton_percent,
     "Polyester": polyester_percent,
@@ -148,8 +127,16 @@ composition = {
     "Viscose": viscose_percent,
 }
 
-# Run Calculations
-if fiber_impact_data and total_percentage == 100:
+user_inputs = {
+    "transport_mode": transport_mode,
+    "transport_distance": transport_distance,
+    "washing_temperature": washing_temperature,
+    "washing_cycles": washing_cycles,
+    "use_dryer": use_dryer,
+}
+
+# Perform calculations
+if total_percentage == 100:
     water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition, user_inputs)
 
     # Display results
@@ -160,14 +147,14 @@ if fiber_impact_data and total_percentage == 100:
         - **Carbon Footprint**: {carbon_fp:.2f} kgCO2e  
     """)
 
-    # 3D 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)
+    # Separate Graphs for Each Footprint
+    fig_cfp = px.bar(x=["Carbon Footprint"], y=[carbon_fp], labels={"x": "Type", "y": "Value (kgCO2e)"}, title="Carbon Footprint")
+    fig_ep = px.bar(x=["Energy Footprint"], y=[energy_fp], labels={"x": "Type", "y": "Value (MJ)"}, title="Energy Footprint")
+    fig_ef = px.bar(x=["Ecological Footprint"], y=[water_fp], labels={"x": "Type", "y": "Value (liters)"}, title="Ecological Footprint")
+
+    # Display Graphs
+    st.plotly_chart(fig_cfp, use_container_width=True)
+    st.plotly_chart(fig_ep, use_container_width=True)
+    st.plotly_chart(fig_ef, use_container_width=True)
 else:
-    st.error("Ensure dataset is loaded and composition sums to 100%.")
+    st.error("Ensure that the material composition totals 100%.")