| from typing import List, Dict | |
| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| scaler = joblib.load("scaler.joblib") | |
| 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"), | |
| } | |
| class Model: | |
| def __init__(self): | |
| self.scaler = scaler | |
| self.models = models | |
| def __call__(self, inputs: List[List[float]]) -> List[Dict[str, float]]: | |
| feature_names = [ | |
| "course overview", "reading file", "abstract materiale", | |
| "concrete material", "visual materials", "self-assessment", | |
| "exercises submit", "quiz submitted", "playing", "paused", | |
| "unstarted", "buffering" | |
| ] | |
| outputs = [] | |
| for features in inputs: | |
| input_df = pd.DataFrame([features], columns=feature_names) | |
| scaled_input = self.scaler.transform(input_df) | |
| predictions = {} | |
| for target, model in self.models.items(): | |
| predictions[target] = model.predict(scaled_input)[0] | |
| outputs.append(predictions) | |
| return outputs | |
| model = Model() | |