Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -1,19 +1,21 @@
|
|
1 |
import streamlit as st
|
|
|
|
|
2 |
|
3 |
-
# Set page configurations
|
4 |
st.set_page_config(page_title="GreenLens-AI", layout="wide")
|
5 |
|
6 |
# Add custom CSS for the background image
|
7 |
def set_background():
|
8 |
st.markdown(
|
9 |
-
|
10 |
<style>
|
11 |
-
.stApp {
|
12 |
background: url("https://drive.google.com/uc?id=1UTGAcHDwFs-JFjG163FTOhusLf1sDMK3");
|
13 |
background-size: cover;
|
14 |
background-position: center;
|
15 |
background-attachment: fixed;
|
16 |
-
}
|
17 |
</style>
|
18 |
""",
|
19 |
unsafe_allow_html=True
|
@@ -22,34 +24,7 @@ def set_background():
|
|
22 |
# Set the background
|
23 |
set_background()
|
24 |
|
25 |
-
|
26 |
-
import pandas as pd
|
27 |
-
import plotly.express as px
|
28 |
-
|
29 |
-
# Set page configurations
|
30 |
-
st.set_page_config(page_title="GreenLens-AI", layout="wide")
|
31 |
-
|
32 |
-
# Page title and description
|
33 |
-
st.markdown("<h1 style='text-align: center; color: #4CAF50;'>GreenLens-AI</h1>", unsafe_allow_html=True)
|
34 |
-
st.markdown(
|
35 |
-
"""
|
36 |
-
<p style='text-align: center; color: #4CAF50;'>
|
37 |
-
A Tool for Calculating Water, Energy, and Carbon Footprints of Textile Products 🌍
|
38 |
-
</p>
|
39 |
-
""",
|
40 |
-
unsafe_allow_html=True,
|
41 |
-
)
|
42 |
-
|
43 |
-
# Sidebar for file upload
|
44 |
-
st.sidebar.header("Step 1: Upload Dataset")
|
45 |
-
uploaded_file = st.sidebar.file_uploader("Upload your Excel file (.xlsx)", type=["xlsx"])
|
46 |
-
|
47 |
-
# Initialize data containers
|
48 |
-
fiber_impact_data = None
|
49 |
-
transport_impact_data = None
|
50 |
-
washing_impact_data = None
|
51 |
-
|
52 |
-
# Function to process the uploaded Excel file
|
53 |
@st.cache_data
|
54 |
def process_excel(file):
|
55 |
try:
|
@@ -58,24 +33,18 @@ def process_excel(file):
|
|
58 |
transport_data = pd.read_excel(file, sheet_name="Transport Impact Data")
|
59 |
washing_data = pd.read_excel(file, sheet_name="Washing Data")
|
60 |
|
61 |
-
# Convert into dictionaries for
|
62 |
fiber_impact_data = fiber_data.set_index("Fiber Type")[["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]].to_dict(orient="index")
|
63 |
transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
|
64 |
washing_impact_data = washing_data.set_index("Washing Temperature")[["Water (L/kg)", "Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"]].to_dict(orient="index")
|
65 |
-
|
66 |
return fiber_impact_data, transport_impact_data, washing_impact_data
|
67 |
except Exception as e:
|
68 |
-
st.error(f"Error processing
|
69 |
return None, None, None
|
70 |
|
71 |
-
#
|
72 |
-
if uploaded_file:
|
73 |
-
fiber_impact_data, transport_impact_data, washing_impact_data = process_excel(uploaded_file)
|
74 |
-
|
75 |
-
# Function to calculate footprints
|
76 |
def calculate_footprints(weight, composition, lifecycle_inputs):
|
77 |
water_fp, energy_fp, carbon_fp = 0, 0, 0
|
78 |
-
|
79 |
for fiber, percentage in composition.items():
|
80 |
if fiber in fiber_impact_data:
|
81 |
data = fiber_impact_data[fiber]
|
@@ -83,116 +52,94 @@ def calculate_footprints(weight, composition, lifecycle_inputs):
|
|
83 |
water_fp += data["Water (L/kg)"] * weight * fraction
|
84 |
energy_fp += data["Energy (MJ/kg)"] * weight * fraction
|
85 |
carbon_fp += data["Carbon (kg CO2e/kg)"] * weight * fraction
|
86 |
-
|
87 |
if lifecycle_inputs["transport_mode"] in transport_impact_data:
|
88 |
carbon_fp += transport_impact_data[lifecycle_inputs["transport_mode"]] * lifecycle_inputs["transport_distance"] * weight
|
89 |
-
|
90 |
if lifecycle_inputs["washing_temperature"] in washing_impact_data:
|
91 |
washing_data = washing_impact_data[lifecycle_inputs["washing_temperature"]]
|
92 |
washing_water = washing_data["Water (L/kg)"] * lifecycle_inputs["washing_cycles"]
|
93 |
washing_energy = washing_data["Energy Use (MJ/wash)"] * lifecycle_inputs["washing_cycles"]
|
94 |
washing_carbon = washing_data["Carbon (kg CO2e/wash)"] * lifecycle_inputs["washing_cycles"]
|
95 |
dryer_carbon = washing_data["Dryer CFP (kg CO2e/cycle)"] if lifecycle_inputs["use_dryer"] else 0
|
96 |
-
|
97 |
water_fp += washing_water
|
98 |
energy_fp += washing_energy
|
99 |
carbon_fp += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])
|
100 |
-
|
101 |
-
# Convert water footprint from liters to kiloliters for visualization
|
102 |
-
water_fp_kL = water_fp / 1000 # Convert liters to kiloliters
|
103 |
-
return water_fp_kL, energy_fp, carbon_fp
|
104 |
-
|
105 |
-
# Sidebar inputs for all scenarios
|
106 |
-
def get_inputs(key_prefix):
|
107 |
-
product_weight = st.sidebar.number_input(f"{key_prefix} - Product Weight (kg)", min_value=0.01, step=0.01, value=0.5, key=f"{key_prefix}_weight")
|
108 |
|
109 |
-
|
110 |
-
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
|
116 |
-
|
117 |
-
|
118 |
-
|
|
|
|
|
119 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
120 |
composition = {
|
121 |
"Conventional Cotton": cotton,
|
122 |
"Polyester": polyester,
|
123 |
"Nylon 6": nylon,
|
124 |
"Acrylic": acrylic,
|
125 |
-
"Viscose": viscose
|
126 |
}
|
127 |
-
|
128 |
-
|
129 |
-
|
130 |
-
|
131 |
-
|
132 |
-
transport_mode = st.sidebar.selectbox(f"{key_prefix} - Transport Mode", list(transport_impact_data.keys()), key=f"{key_prefix}_transport_mode")
|
133 |
-
transport_distance = st.sidebar.number_input(f"{key_prefix} - Transport Distance (km)", min_value=0, step=10, value=100, key=f"{key_prefix}_transport_distance")
|
134 |
-
|
135 |
-
lifecycle_inputs = {
|
136 |
-
"washing_temperature": washing_temperature,
|
137 |
-
"washing_cycles": washing_cycles,
|
138 |
-
"use_dryer": use_dryer,
|
139 |
-
"transport_mode": transport_mode,
|
140 |
-
"transport_distance": transport_distance,
|
141 |
-
}
|
142 |
-
|
143 |
-
return product_weight, composition, lifecycle_inputs
|
144 |
|
145 |
-
|
146 |
-
|
147 |
comparison_mode = st.sidebar.checkbox("Enable Comparison Mode")
|
148 |
|
149 |
if comparison_mode:
|
150 |
-
# Input for two assessments
|
151 |
col1, col2 = st.columns(2)
|
152 |
with col1:
|
153 |
st.subheader("Assessment 1")
|
154 |
-
|
155 |
with col2:
|
156 |
st.subheader("Assessment 2")
|
157 |
-
|
158 |
|
159 |
-
|
160 |
-
|
161 |
-
water_fp_2, energy_fp_2, carbon_fp_2 = calculate_footprints(product_weight_2, composition_2, lifecycle_inputs_2)
|
162 |
|
163 |
-
|
164 |
-
|
165 |
-
assessment_data = pd.DataFrame({
|
166 |
"Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
|
167 |
-
"Assessment 1": [
|
168 |
-
"Assessment 2": [
|
169 |
})
|
170 |
-
fig = px.line(
|
171 |
-
|
172 |
-
|
173 |
-
y="Value",
|
174 |
-
color="Assessment",
|
175 |
-
markers=True,
|
176 |
-
title="Footprint Trends: Assessment 1 vs. Assessment 2"
|
177 |
-
)
|
178 |
st.plotly_chart(fig)
|
179 |
else:
|
180 |
-
|
181 |
-
|
182 |
-
water_fp, energy_fp, carbon_fp = calculate_footprints(product_weight, composition, lifecycle_inputs)
|
183 |
-
|
184 |
-
# Display results
|
185 |
st.subheader("Results")
|
186 |
-
st.markdown(f"- **Water Footprint**: {
|
187 |
-
|
188 |
-
|
189 |
-
|
190 |
-
# Visualization for single scenario
|
191 |
-
result_data = pd.DataFrame({
|
192 |
"Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
|
193 |
-
"Value": [
|
194 |
-
})
|
195 |
-
fig = px.line(result_data, x="Footprint Type", y="Value", markers=True, title="Footprint Trends")
|
196 |
st.plotly_chart(fig)
|
197 |
else:
|
198 |
st.info("Please upload a dataset to proceed.")
|
|
|
1 |
import streamlit as st
|
2 |
+
import pandas as pd
|
3 |
+
import plotly.express as px
|
4 |
|
5 |
+
# Set page configurations (must be the first Streamlit command)
|
6 |
st.set_page_config(page_title="GreenLens-AI", layout="wide")
|
7 |
|
8 |
# Add custom CSS for the background image
|
9 |
def set_background():
|
10 |
st.markdown(
|
11 |
+
"""
|
12 |
<style>
|
13 |
+
.stApp {
|
14 |
background: url("https://drive.google.com/uc?id=1UTGAcHDwFs-JFjG163FTOhusLf1sDMK3");
|
15 |
background-size: cover;
|
16 |
background-position: center;
|
17 |
background-attachment: fixed;
|
18 |
+
}
|
19 |
</style>
|
20 |
""",
|
21 |
unsafe_allow_html=True
|
|
|
24 |
# Set the background
|
25 |
set_background()
|
26 |
|
27 |
+
# Process uploaded Excel file
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
28 |
@st.cache_data
|
29 |
def process_excel(file):
|
30 |
try:
|
|
|
33 |
transport_data = pd.read_excel(file, sheet_name="Transport Impact Data")
|
34 |
washing_data = pd.read_excel(file, sheet_name="Washing Data")
|
35 |
|
36 |
+
# Convert data into dictionaries for calculations
|
37 |
fiber_impact_data = fiber_data.set_index("Fiber Type")[["Water (L/kg)", "Energy (MJ/kg)", "Carbon (kg CO2e/kg)"]].to_dict(orient="index")
|
38 |
transport_impact_data = transport_data.set_index("Transport Mode")["CFP (kg CO2e/km)"].to_dict()
|
39 |
washing_impact_data = washing_data.set_index("Washing Temperature")[["Water (L/kg)", "Energy Use (MJ/wash)", "Carbon (kg CO2e/wash)", "Dryer CFP (kg CO2e/cycle)"]].to_dict(orient="index")
|
|
|
40 |
return fiber_impact_data, transport_impact_data, washing_impact_data
|
41 |
except Exception as e:
|
42 |
+
st.error(f"Error processing file: {e}")
|
43 |
return None, None, None
|
44 |
|
45 |
+
# Calculate footprints
|
|
|
|
|
|
|
|
|
46 |
def calculate_footprints(weight, composition, lifecycle_inputs):
|
47 |
water_fp, energy_fp, carbon_fp = 0, 0, 0
|
|
|
48 |
for fiber, percentage in composition.items():
|
49 |
if fiber in fiber_impact_data:
|
50 |
data = fiber_impact_data[fiber]
|
|
|
52 |
water_fp += data["Water (L/kg)"] * weight * fraction
|
53 |
energy_fp += data["Energy (MJ/kg)"] * weight * fraction
|
54 |
carbon_fp += data["Carbon (kg CO2e/kg)"] * weight * fraction
|
55 |
+
|
56 |
if lifecycle_inputs["transport_mode"] in transport_impact_data:
|
57 |
carbon_fp += transport_impact_data[lifecycle_inputs["transport_mode"]] * lifecycle_inputs["transport_distance"] * weight
|
58 |
+
|
59 |
if lifecycle_inputs["washing_temperature"] in washing_impact_data:
|
60 |
washing_data = washing_impact_data[lifecycle_inputs["washing_temperature"]]
|
61 |
washing_water = washing_data["Water (L/kg)"] * lifecycle_inputs["washing_cycles"]
|
62 |
washing_energy = washing_data["Energy Use (MJ/wash)"] * lifecycle_inputs["washing_cycles"]
|
63 |
washing_carbon = washing_data["Carbon (kg CO2e/wash)"] * lifecycle_inputs["washing_cycles"]
|
64 |
dryer_carbon = washing_data["Dryer CFP (kg CO2e/cycle)"] if lifecycle_inputs["use_dryer"] else 0
|
|
|
65 |
water_fp += washing_water
|
66 |
energy_fp += washing_energy
|
67 |
carbon_fp += washing_carbon + (dryer_carbon * lifecycle_inputs["washing_cycles"])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
68 |
|
69 |
+
water_fp /= 1000 # Convert water from liters to kiloliters
|
70 |
+
return water_fp, energy_fp, carbon_fp
|
71 |
+
|
72 |
+
# Sidebar inputs
|
73 |
+
def get_inputs(prefix):
|
74 |
+
weight = st.sidebar.number_input(f"{prefix} Product Weight (kg)", min_value=0.01, value=0.5, step=0.01, key=f"{prefix}_weight")
|
75 |
+
st.sidebar.subheader(f"{prefix} Material Composition (%)")
|
76 |
+
cotton = st.sidebar.number_input("Conventional Cotton (%)", 0, 100, 50, step=1, key=f"{prefix}_cotton")
|
77 |
+
polyester = st.sidebar.number_input("Polyester (%)", 0, 100, 30, step=1, key=f"{prefix}_polyester")
|
78 |
+
nylon = st.sidebar.number_input("Nylon 6 (%)", 0, 100, 10, step=1, key=f"{prefix}_nylon")
|
79 |
+
acrylic = st.sidebar.number_input("Acrylic (%)", 0, 100, 5, step=1, key=f"{prefix}_acrylic")
|
80 |
+
viscose = st.sidebar.number_input("Viscose (%)", 0, 100, 5, step=1, key=f"{prefix}_viscose")
|
81 |
|
82 |
+
if cotton + polyester + nylon + acrylic + viscose != 100:
|
83 |
+
st.sidebar.error("Fiber composition must sum to 100%!")
|
84 |
+
|
85 |
+
lifecycle_inputs = {
|
86 |
+
"washing_cycles": st.sidebar.number_input(f"{prefix} Washing Cycles", min_value=0, value=30, key=f"{prefix}_wash_cycles"),
|
87 |
+
"washing_temperature": st.sidebar.selectbox(f"{prefix} Washing Temperature", list(washing_impact_data.keys()), key=f"{prefix}_wash_temp"),
|
88 |
+
"use_dryer": st.sidebar.checkbox(f"{prefix} Use Dryer?", key=f"{prefix}_use_dryer"),
|
89 |
+
"transport_mode": st.sidebar.selectbox(f"{prefix} Transport Mode", list(transport_impact_data.keys()), key=f"{prefix}_transport_mode"),
|
90 |
+
"transport_distance": st.sidebar.number_input(f"{prefix} Transport Distance (km)", min_value=0, value=100, step=10, key=f"{prefix}_transport_distance")
|
91 |
+
}
|
92 |
+
|
93 |
composition = {
|
94 |
"Conventional Cotton": cotton,
|
95 |
"Polyester": polyester,
|
96 |
"Nylon 6": nylon,
|
97 |
"Acrylic": acrylic,
|
98 |
+
"Viscose": viscose
|
99 |
}
|
100 |
+
return weight, composition, lifecycle_inputs
|
101 |
+
|
102 |
+
# Main App Logic
|
103 |
+
st.sidebar.header("Step 1: Upload Dataset")
|
104 |
+
uploaded_file = st.sidebar.file_uploader("Upload your Excel file (.xlsx)", type=["xlsx"])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
105 |
|
106 |
+
if uploaded_file:
|
107 |
+
fiber_impact_data, transport_impact_data, washing_impact_data = process_excel(uploaded_file)
|
108 |
comparison_mode = st.sidebar.checkbox("Enable Comparison Mode")
|
109 |
|
110 |
if comparison_mode:
|
|
|
111 |
col1, col2 = st.columns(2)
|
112 |
with col1:
|
113 |
st.subheader("Assessment 1")
|
114 |
+
weight1, composition1, lifecycle1 = get_inputs("Assessment 1")
|
115 |
with col2:
|
116 |
st.subheader("Assessment 2")
|
117 |
+
weight2, composition2, lifecycle2 = get_inputs("Assessment 2")
|
118 |
|
119 |
+
water1, energy1, carbon1 = calculate_footprints(weight1, composition1, lifecycle1)
|
120 |
+
water2, energy2, carbon2 = calculate_footprints(weight2, composition2, lifecycle2)
|
|
|
121 |
|
122 |
+
st.subheader("Comparison")
|
123 |
+
df = pd.DataFrame({
|
|
|
124 |
"Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
|
125 |
+
"Assessment 1": [water1, energy1, carbon1],
|
126 |
+
"Assessment 2": [water2, energy2, carbon2],
|
127 |
})
|
128 |
+
fig = px.line(df.melt(id_vars="Footprint Type", var_name="Assessment", value_name="Value"),
|
129 |
+
x="Footprint Type", y="Value", color="Assessment", markers=True,
|
130 |
+
title="Comparison of Assessments")
|
|
|
|
|
|
|
|
|
|
|
131 |
st.plotly_chart(fig)
|
132 |
else:
|
133 |
+
weight, composition, lifecycle = get_inputs("Single")
|
134 |
+
water, energy, carbon = calculate_footprints(weight, composition, lifecycle)
|
|
|
|
|
|
|
135 |
st.subheader("Results")
|
136 |
+
st.markdown(f"- **Water Footprint**: {water:.2f} kL\n"
|
137 |
+
f"- **Energy Footprint**: {energy:.2f} MJ\n"
|
138 |
+
f"- **Carbon Footprint**: {carbon:.2f} kg CO2e")
|
139 |
+
fig = px.line(pd.DataFrame({
|
|
|
|
|
140 |
"Footprint Type": ["Water (kL)", "Energy (MJ)", "Carbon (kg CO2e)"],
|
141 |
+
"Value": [water, energy, carbon]
|
142 |
+
}), x="Footprint Type", y="Value", markers=True, title="Footprint Trends")
|
|
|
143 |
st.plotly_chart(fig)
|
144 |
else:
|
145 |
st.info("Please upload a dataset to proceed.")
|