Update app.py

app.py

@@ -1,7 +1,6 @@
 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
@@ -17,7 +16,7 @@ st.markdown(
     body {
         background-color: #d4edda; /* Light green background */
     }
-    .stSlider > div { 
+    .stSlider > div {
         background-color: #50C878 !important; /* Green slider bar */
     }
     h1, h2, p {
@@ -31,16 +30,16 @@ st.markdown(
 
 # 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)
+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"
 
-# Function to fetch and process dataset
+# Function to fetch dataset from Google Drive
 @st.cache_data
-def fetch_dataset(url):
-    """Fetch the dataset from Google Drive."""
-    progress_text = "Fetching dataset from Google Drive..."
+def fetch_pdf_from_drive(url):
+    """Fetch the dataset (PDF) from Google Drive."""
+    progress_text = "Downloading dataset from Google Drive..."
     progress_bar = st.progress(0)
 
     try:
@@ -55,13 +54,38 @@ def fetch_dataset(url):
             chunks.append(chunk)
 
         pdf_content = b"".join(chunks)
-        progress_bar.progress(1.0, text="Processing dataset...")
+        progress_bar.progress(1.0, text="Download Complete")
+        st.success("Dataset downloaded successfully!")
         return PdfReader(BytesIO(pdf_content))
+
     except Exception as e:
         st.error(f"Error fetching dataset: {e}")
         return None
 
-dataset = fetch_dataset(DATASET_URL)
+# Function to extract data from the PDF
+@st.cache_data
+def process_pdf_data(pdf_reader):
+    """Extract relevant data from the PDF."""
+    extracted_data = {}
+    for page in pdf_reader.pages:
+        text = page.extract_text()
+        # Example: Extract fiber impact data or other metrics
+        if "Global average water footprint of cotton fabric" in text:
+            extracted_data["Cotton"] = {"Water": 10000, "Energy": 60, "Carbon": 3.18}
+        if "Water footprint by region: China" in text:
+            extracted_data["China"] = {"Water": 6000}
+    return extracted_data
+
+# Fetch and process the dataset
+st.info("Fetching dataset. Please wait...")
+pdf_reader = fetch_pdf_from_drive(DATASET_URL)
+if pdf_reader:
+    fiber_impact_data = process_pdf_data(pdf_reader)
+    if not fiber_impact_data:
+        st.error("Failed to extract data from the PDF. Please check the PDF structure.")
+else:
+    st.error("Failed to fetch the dataset. Check your internet connection or the dataset link.")
+    fiber_impact_data = {}
 
 # Sidebar for User Inputs
 st.sidebar.header("Input Product Details")
@@ -79,7 +103,7 @@ viscose_percent = st.sidebar.slider("Viscose (%)", 0, 100, 5)
 # Validate percentage
 total_percentage = cotton_percent + polyester_percent + nylon_percent + acrylic_percent + viscose_percent
 if total_percentage != 100:
-    st.sidebar.warning("The total of all fiber percentages must equal 100%!")
+    st.sidebar.warning("Material percentages must sum to 100%!")
 
 # Lifecycle Inputs
 st.sidebar.header("Lifecycle Details")
@@ -89,36 +113,19 @@ 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):
-    water_footprint = 0
-    energy_footprint = 0
-    carbon_footprint = 0
-
+def calculate_footprints(weight, composition):
+    water_fp, energy_fp, carbon_fp = 0, 0, 0
     for fiber, percentage in composition.items():
         if fiber in fiber_impact_data:
-            data = fiber_impact_data[fiber]
+            impacts = 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_fp += impacts["Water"] * weight * fraction
+            energy_fp += impacts["Energy"] * weight * fraction
+            carbon_fp += impacts["Carbon"] * weight * fraction
+    return water_fp, energy_fp, carbon_fp
 
-    # 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
-
-# Composition dictionary
+# Perform calculations
 composition = {
     "Cotton": cotton_percent,
     "Polyester": polyester_percent,
@@ -127,34 +134,22 @@ composition = {
     "Viscose": viscose_percent,
 }
 
-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)
+    water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition)
 
     # 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  
-    """)
-
-    # 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)
+    st.markdown(f"- **Water Footprint**: {water_fp:.2f} liters")
+    st.markdown(f"- **Energy Footprint**: {energy_fp:.2f} MJ")
+    st.markdown(f"- **Carbon Footprint**: {carbon_fp:.2f} kgCO2e")
+
+    # Separate Graphs for each footprint
+    fig_water = px.bar(x=["Water Footprint"], y=[water_fp], labels={"x": "Type", "y": "Liters"}, title="Water Footprint")
+    fig_energy = px.bar(x=["Energy Footprint"], y=[energy_fp], labels={"x": "Type", "y": "MJ"}, title="Energy Footprint")
+    fig_carbon = px.bar(x=["Carbon Footprint"], y=[carbon_fp], labels={"x": "Type", "y": "kgCO2e"}, title="Carbon Footprint")
+
+    st.plotly_chart(fig_water, use_container_width=True)
+    st.plotly_chart(fig_energy, use_container_width=True)
+    st.plotly_chart(fig_carbon, use_container_width=True)
 else:
-    st.error("Ensure that the material composition totals 100%.")
+    st.warning("Ensure that material composition totals 100%.")