Delete app.py

app.py

@@ -1,50 +0,0 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-import os
-
-
-MODEL_ID = os.environ.get("MODEL_ID", "Perth0603/phishing-email-mobilebert")
-
-app = FastAPI(title="Phishing Text Classifier", version="1.0.0")
-
-
-class PredictPayload(BaseModel):
-    inputs: str
-
-
-# Lazy singletons for model/tokenizer
-_tokenizer = None
-_model = None
-
-
-def _load_model():
-    global _tokenizer, _model
-    if _tokenizer is None or _model is None:
-        _tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-        _model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
-        # Warm-up
-        with torch.no_grad():
-            _ = _model(**_tokenizer(["warm up"], return_tensors="pt")).logits
-
-
-@app.get("/")
-def root():
-    return {"status": "ok", "model": MODEL_ID}
-
-
-@app.post("/predict")
-def predict(payload: PredictPayload):
-    _load_model()
-    with torch.no_grad():
-        logits = _model(**_tokenizer([payload.inputs], return_tensors="pt")).logits
-        probs = torch.softmax(logits, dim=-1)[0]
-        score, idx = torch.max(probs, dim=0)
-
-    # Map common ids to labels (kept generic; your config also has these)
-    id2label = {0: "LEGIT", 1: "PHISH"}
-    label = id2label.get(int(idx), str(int(idx)))
-    return {"label": label, "score": float(score)}
-
-