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import gradio as gr
from codecarbon import EmissionsTracker
# Import necessary libraries
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score
import pandas as pd
import numpy as np
# Let's create a sample dataset (you can replace this with your own data)
def create_sample_data():
np.random.seed(42)
n_samples = 10000
# Create features (X)
X = np.random.randn(n_samples, 4) # 4 features
# Create target (y) - binary classification
y = (X[:, 0] + X[:, 1] + X[:, 2] > 0).astype(int)
return X, y
# Get data (replace this with your data loading code)
X, y = create_sample_data()
tracker = EmissionsTracker()
def submit(username):
tracker.start()
tracker.start_task("train_model")
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Initialize the model
rf_model = RandomForestClassifier(
n_estimators=1000,
max_depth=5,
random_state=42
)
# Train the model
print("Training the model...")
rf_model.fit(X_train, y_train)
training_emissions = tracker.stop_task()
tracker.start_task("inference")
rf_model.predict(X_test)
inference_emissions = tracker.stop_task()
emissions = inference_emissions.emissions
energy = inference_emissions.energy_consumed
return [emissions, energy]
# Update the interface configuration
demo = gr.Interface(
fn=submit,
inputs=gr.Textbox(label="Username"),
outputs=[
gr.Number(label="Emissions (kgCO2eq)", precision=6),
gr.Number(label="Energy Consumed (kWh)", precision=6)
],
title="Carbon Emissions Tracker",
description="Track the carbon emissions and energy consumption of model training and inference."
)
# Launch the Gradio interface
if __name__ == "__main__":
demo.launch() |