FIrst commit

.gitignore

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+
+.ipynb_checkpoints/sandbox-checkpoint.ipynb

app.py

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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()

sandbox.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6941ccb0-6ed0-45c1-9460-8f8c0bbfc288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from codecarbon import EmissionsTracker\n",
+    "tracker = EmissionsTracker()\n",
+    "tracker.start()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}