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Dockerfile

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+FROM python:3.10-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1
+
+WORKDIR /app
+
+# System deps (optional but helps with torch wheels)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential git && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install -r /app/requirements.txt
+
+COPY app.py /app/app.py
+
+EXPOSE 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+

README.md

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+---
+title: PhishWatch Proxy
+emoji: 🛡️
+sdk: docker
+---
+
+# Hugging Face Space - Phishing Text Classifier (Docker + FastAPI)
+
+This Space exposes a minimal `/predict` endpoint for your MobileBERT phishing model so the Flutter app can call it reliably.
+
+## Files
+- Dockerfile - builds a small FastAPI server image
+- app.py - FastAPI app that loads the model and returns `{ label, score }`.
+- requirements.txt - Python dependencies.
+
+## How to deploy
+1. Create a new Space on Hugging Face (type: Docker).
+2. Upload the contents of this `hf_space/` folder to the Space root (including Dockerfile).
+3. In Space Settings → Variables, add:
+   - MODEL_ID = Perth0603/phishing-email-mobilebert
+4. Wait for the Space to build and become green. Test:
+   - GET `/` should return `{ status: ok, model: ... }`
+   - POST `/predict` with `{ "inputs": "Win an iPhone! Click here" }`
+
+## Flutter app config
+Set the Space URL in your env file so the app targets the Space instead of the Hosted Inference API:
+
+```
+{"HF_SPACE_URL":"https://<your-space>.hf.space"}
+```
+
+Run the app:
+```
+flutter run --dart-define-from-file=hf.env.json
+```

app.py

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+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)}
+
+

requirements.txt

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+fastapi==0.115.0
+uvicorn==0.30.6
+transformers==4.46.3
+torch==2.3.1
+accelerate>=0.33.0
+