Update app.py

app.py

@@ -1,7 +1,60 @@
-from fastapi import FastAPI
+# app.py
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Literal
+import joblib
+import numpy as np
 
-app = FastAPI()
+app = FastAPI(title="Transaction Categorizer")
 
-@app.get("/")
-def greet_json():
-    return {"Hello": "World!"}
+# Load models once at startup
+shop_model     = joblib.load("shop_classifier.pkl")
+category_model = joblib.load("category_classifier.pkl")
+
+def predict_or_na(pipeline, text: str, threshold: float = 0.3) -> str:
+    """
+    Returns the top class if its probability ≥ threshold, else "N/A"
+    """
+    probs = pipeline.predict_proba([text])[0]
+    top_idx = int(np.argmax(probs))
+    return str(pipeline.classes_[top_idx]) if probs[top_idx] >= threshold else "N/A"
+
+class TransactionIn(BaseModel):
+    id: str
+    description: str
+
+class TransactionOut(BaseModel):
+    id: str
+    description: str
+    shop: str
+    category: str
+
+@app.post("/predict", response_model=List[TransactionOut])
+def predict_transactions(
+    items: List[TransactionIn],
+    threshold: float = 0.3
+):
+    """
+    Predict shop and category for each transaction.
+    - **items**: list of `{ id, description }`
+    - **threshold**: optional override for probability cutoff
+    """
+    results = []
+    for item in items:
+        desc_norm = item.description.lower().strip()
+        shop_pred     = predict_or_na(shop_model,     desc_norm, threshold)
+        category_pred = predict_or_na(category_model, desc_norm, threshold)
+        results.append(
+            TransactionOut(
+                id=item.id,
+                description=item.description,
+                shop=shop_pred,
+                category=category_pred
+            )
+        )
+    return results
+
+# Optional healthcheck
+@app.get("/health")
+def health():
+    return {"status": "ok"}