Create app.py

app.py

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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