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from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np
import os
app = FastAPI()
# -------- Load models once at startup --------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_DIR = os.path.join(BASE_DIR, "models")
stockout_model = joblib.load(
os.path.join(MODEL_DIR, "restaurant_stockout_classifier.joblib")
)
wastage_model = joblib.load(
os.path.join(MODEL_DIR, "restaurant_wastage_regressor.joblib")
)
# -------- Request schema --------
class PredictRequest(BaseModel):
features: list[float]
# -------- Health check --------
@app.get("/")
def root():
return {
"status": "ok",
"message": "ProjectY Classifier + Regressor API is running"
}
# -------- Stockout classifier --------
@app.post("/predict/stockout")
def predict_stockout(req: PredictRequest):
X = np.array([req.features]) # shape: (1, n_features)
prediction = stockout_model.predict(X)[0]
response = {
"prediction": int(prediction) if isinstance(prediction, (int, np.integer)) else float(prediction)
}
# Optional probabilities (if supported)
if hasattr(stockout_model, "predict_proba"):
response["probabilities"] = stockout_model.predict_proba(X)[0].tolist()
return response
# -------- Wastage regressor --------
@app.post("/predict/wastage")
def predict_wastage(req: PredictRequest):
X = np.array([req.features])
prediction = wastage_model.predict(X)[0]
return {
"prediction": float(prediction)
}
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