Spaces:
Sleeping
Sleeping
File size: 2,370 Bytes
6ccbbfd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | """
src/predict.py
──────────────
Load saved model + scaler and run inference for a single student.
Returns prediction label, probabilities, and top feature importances.
"""
import os, json
import numpy as np
import joblib
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_DIR = os.path.join(BASE_DIR, "..", "models")
RISK_LABELS = {0: "Low Risk", 1: "Medium Risk", 2: "High Risk"}
def load_artifacts():
model = joblib.load(os.path.join(MODEL_DIR, "best_model.pkl"))
scaler = joblib.load(os.path.join(MODEL_DIR, "scaler.pkl"))
with open(os.path.join(MODEL_DIR, "model_meta.json")) as f:
meta = json.load(f)
return model, scaler, meta
def predict_student(user_input: dict):
"""
Full prediction pipeline for one student.
Returns
-------
dict with keys:
risk_label str e.g. "High Risk"
risk_code int 0/1/2
confidence float probability of predicted class
probabilities dict {label: prob}
top_features list [(feature_name, importance), …]
model_name str
"""
from preprocessing import preprocess_single_input, FEATURES
model, scaler, meta = load_artifacts()
X = preprocess_single_input(user_input, scaler, FEATURES)
probs = model.predict_proba(X)[0]
risk_code = int(np.argmax(probs))
risk_label = RISK_LABELS[risk_code]
confidence = float(probs[risk_code])
probabilities = {RISK_LABELS[i]: float(p) for i, p in enumerate(probs)}
# Feature importance from saved metadata
top_features = meta.get("feature_importance", [])
return {
"risk_label": risk_label,
"risk_code": risk_code,
"confidence": confidence,
"probabilities": probabilities,
"top_features": top_features,
"model_name": meta.get("best_model_name", "Model"),
}
if __name__ == "__main__":
sample = {
"study_time": 2, "absences": 12, "failures": 1,
"G1": 9.0, "G2": 8.5, "internet": 1,
"higher_edu": 1, "activities": 0, "romantic": 1,
"family_support": "low", "gender": "M",
}
result = predict_student(sample)
print(f"Prediction : {result['risk_label']}")
print(f"Confidence : {result['confidence']:.1%}")
print(f"Top factors: {result['top_features'][:3]}")
|