Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import joblib
+from pytorch_tabnet.tab_model import TabNetClassifier
+
+# Load model and preprocessing tools
+model = TabNetClassifier()
+model.load_model("tabnet_model.zip")
+scaler = joblib.load("scaler.save")
+encoder = joblib.load("encoder.save")
+
+# Features used in the model
+features = [f"{trait}{i}" for trait in ["EXT", "EST", "AGR", "CSN", "OPN"] for i in range(1, 11)]
+
+def predict_personality(*inputs):
+    X = np.array(inputs).reshape(1, -1).astype(np.float32)
+    X_scaled = scaler.transform(X)
+    y_pred = model.predict(X_scaled)
+    label = encoder.inverse_transform(y_pred)[0]
+    return f"Predicted Personality Type: {label}"
+
+# Create Gradio interface
+inputs = [gr.Slider(1, 5, step=0.1, label=f) for f in features]
+
+demo = gr.Interface(
+    fn=predict_personality,
+    inputs=inputs,
+    outputs=gr.Text(label="Personality Prediction"),
+    title="Personality Type Classifier (Introvert vs. Extrovert)",
+    description="This model predicts if a person is Introvert or Extrovert based on their IPIP-FFM scores."
+)
+
+demo.launch()