from flask import Flask, request, jsonify
import joblib
import pandas as pd

app = Flask(__name__)

# Load models and scaler
models = {
    "processing": joblib.load("svm_model_processing.joblib"),
    "perception": joblib.load("svm_model_perception.joblib"),
    "input": joblib.load("svm_model_input.joblib"),
    "understanding": joblib.load("svm_model_understanding.joblib"),
}
scaler = joblib.load("scaler.joblib")

@app.route("/predict", methods=["POST"])
def predict():
    try:
        # Parse input data from JSON
        input_data = request.json
        df = pd.DataFrame([input_data])

        # Scale the data
        df_scaled = scaler.transform(df)

        # Make predictions for all target variables
        predictions = {}
        for target, model in models.items():
            predictions[target] = model.predict(df_scaled)[0]

        return jsonify({"success": True, "predictions": predictions})
    except Exception as e:
        return jsonify({"success": False, "error": str(e)})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000)