updated files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Helps with some builds
+RUN pip install --no-cache-dir --upgrade pip
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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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)
+    }

requirements.txt

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+fastapi==0.110.0
+uvicorn[standard]==0.27.1
+joblib==1.3.2
+numpy==1.26.4
+scikit-learn==1.4.2