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# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split
import gradio as gr
# Define the safety function
def safety(freedom):
return 1 - 0.7374 * freedom**2
# Load and process data for feature importance
def load_data():
# Replace this path with the appropriate dataset URL or local file path for Hugging Face deployment
data = pd.read_csv("data_ml.csv") # Ensure the dataset is uploaded to the Hugging Face repo
X = data.drop(columns=["Freedom"], errors="ignore")
y = data["Freedom"] if "Freedom" in data.columns else None
return X, y
# Calculate top features affecting safety
def get_top_features(X, y):
if y is None:
return pd.DataFrame({"Features": [], "Importance": []})
# Split data and train ElasticNet model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
model = ElasticNet(alpha=1.0, l1_ratio=0.5, random_state=0).fit(X_train, y_train)
# Get feature importance
feature_importance = pd.DataFrame({
"Features": X.columns,
"Importance": np.abs(model.coef_)
}).nlargest(7, "Importance").reset_index(drop=True)
return feature_importance
# Generate PPF curve data
freedom = np.arange(0, 1.01, 0.01)
safety_values = safety(freedom)
# Initialize data and top features
X, y = load_data()
top_features = get_top_features(X, y)
# Function to update outputs when slider changes
def update_prediction(freedom_value):
# Calculate safety
safety_score = safety(freedom_value)
# Create PPF plot
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(freedom, safety_values, label='Safety = 1 - 0.7374 * Freedom²', color='blue', linestyle='--')
ax.scatter(freedom_value, safety_score, color='red', zorder=5)
ax.text(
freedom_value,
safety_score,
f"Selected Point ({freedom_value:.2f}, {safety_score:.2f})",
fontsize=9,
verticalalignment='bottom'
)
ax.set_title("Safety vs Freedom Relationship")
ax.set_xlabel("Freedom")
ax.set_ylabel("Safety")
ax.legend()
ax.grid(False)
return (
f"Predicted Safety: {safety_score:.4f}",
fig,
top_features
)
# Create Gradio interface
with gr.Blocks() as app:
gr.Markdown("# Country Safety Predictor")
with gr.Row():
# Freedom slider
freedom_slider = gr.Slider(
label="Freedom Score (0 = Least Free, 1 = Most Free)",
minimum=0,
maximum=1,
value=0.5,
step=0.01
)
with gr.Row():
# Text box for safety prediction
safety_text = gr.Textbox(label="Predicted Safety:", value="")
with gr.Row():
# Plot output for safety vs freedom
plot_output = gr.Plot(label="Safety vs Freedom Relationship")
with gr.Row():
# Dataframe display for top features
features_output = gr.Dataframe(label="Top Features Affecting Safety")
# Set up the interface
freedom_slider.change(
fn=update_prediction,
inputs=freedom_slider,
outputs=[safety_text, plot_output, features_output]
)
# Run the app
if __name__ == "__main__":
app.queue().launch(debug=False, share=True) # Enable sharing for Hugging Face deployment |