app.py

# inference/app.py
from fastapi import FastAPI
from pydantic import BaseModel
from pathlib import Path
from transformers import pipeline
import os

# Put all caches in writable /tmp
os.environ.setdefault("HF_HOME", "/tmp/hf")
os.environ.setdefault("TRANSFORMERS_CACHE", "/tmp/transformers")
os.environ.setdefault("HF_DATASETS_CACHE", "/tmp/hf_datasets")
os.environ.setdefault("HF_HUB_DISABLE_TELEMETRY", "1")


app = FastAPI(title="Incident ML Inference API")


# LOCAL_MODEL = Path(__file__).resolve().parents[1] / "models" / "incident_classifier"

# # Category classifier (your fine-tuned model if available)
# if LOCAL_MODEL.exists():
#     incident_classifier = pipeline("text-classification", model="brijeshpandya/incident-classifier")
# else:
#     incident_classifier = pipeline("text-classification", model="cardiffnlp/twitter-xlm-roberta-base")

incident_classifier = pipeline("text-classification", model="brijeshpandya/incident-classifier")

# Sentiment (keep public model for now)
sentiment_analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")

class AnalyzeIn(BaseModel):
    text: str

@app.get("/health")
def health(): return {"ok": True, "using_local_model": LOCAL_MODEL.exists()}

@app.post("/analyze")
def analyze(data: AnalyzeIn):
    return {
        "category": incident_classifier(data.text),
        "sentiment": sentiment_analyzer(data.text)
    }