api.py

from pydantic import BaseModel
from huggingface_hub import hf_hub_download
import joblib
from fastapi import FastAPI
import numpy as np
import os
from typing import List

# Set a different cache directory
os.environ["HF_HOME"] = "/tmp/hf_cache"

app = FastAPI()

# Download model
model_path = hf_hub_download(
    repo_id="haseebnawazz/sleep_stage_classifier-RF",
    filename="class_balanced_RF_model.joblib",
    cache_dir="/tmp/hf_cache"
)
model = joblib.load(model_path)

class FeatureInput(BaseModel):
    features: List[float]

@app.post("/predict")
def predict(input: FeatureInput):
    try:
        features = np.array(input.features).reshape(1, -1)
        prediction = model.predict(features).tolist()
        return {"prediction": prediction}
    except Exception as e:
        error_message = traceback.format_exc()
        print("[SERVER ERROR]:", error_message)  # This will show in Hugging Face logs
        return {"error": str(e)}

@app.get("/")
async def read_root():
    return {"message": "Welcome to the API"}

@app.get("/logs")
async def get_logs():
    return {"logs": "container logs here"}