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import gradio as gr
import csv
import matplotlib.pyplot as plt
import io
from PIL import Image
def display_emissions_data_and_chart(tire_id):
file_path = 'predictions.csv'
try:
with open(file_path, mode='r', newline='') as file:
reader = csv.DictReader(file)
for row in reader:
if row['Identifier'] == tire_id:
# Extracting data for pie chart
emissions_labels = [
'Predicted Material CO2',
'Raw Material Transportation CO2',
'Fuel CO2',
'Plant Energy Consumption CO2'
]
emissions_values = [
float(row.get('Predicted Material CO2 Emissions', 0)),
float(row.get('Raw Material Transportation CO2 Emissions', 0)),
float(row.get('Fuel CO2 Emissions', 0)),
float(row.get('Plant Energy Consumption CO2 Emissions', 0))
]
# Generate Pie Chart
fig, ax = plt.subplots(figsize=(9, 6)) # Adjusted figure size to be 50% larger
ax.pie(emissions_values, labels=emissions_labels, autopct='%1.1f%%', startangle=90)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
buf = io.BytesIO()
plt.savefig(buf, format='png', bbox_inches='tight')
buf.seek(0)
plt.close(fig)
# Convert bytes to PIL Image
buf_image = Image.open(buf)
return buf_image
except FileNotFoundError:
return "The predictions.csv file was not found."
# Define a wrapper function for Gradio that calls the display function
def get_emissions_data(tire_id):
image = display_emissions_data_and_chart(tire_id)
if isinstance(image, Image.Image):
return image
else:
# If the result isn't an image, return a placeholder or error image
return Image.new('RGB', (200, 200), color='red')
# Set up the Gradio Blocks
with gr.Blocks() as demo:
with gr.Row():
tire_id_input = gr.Textbox(label="Enter Tire ID")
submit_button = gr.Button("Submit")
output_image = gr.Image(label="Emissions Data Chart")
submit_button.click(
fn=get_emissions_data,
inputs=tire_id_input,
outputs=output_image
)
demo.launch()
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