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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import numpy as np
from huggingface_hub import hf_hub_download, HfApi
import joblib
import os
from datetime import datetime, timedelta
app = FastAPI()
REPO_ID = "GodfreyOwino/NPK_needs_mode2"
FILENAME = "npk_needs_model.joblib"
UPDATE_FREQUENCY = timedelta(days=1)
def get_latest_model():
try:
api = HfApi()
remote_info = api.model_info(repo_id=REPO_ID)
remote_mtime = remote_info.lastModified
cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
if os.path.exists(cached_path):
local_mtime = datetime.fromtimestamp(os.path.getmtime(cached_path))
if datetime.now() - local_mtime < UPDATE_FREQUENCY:
print("Using cached model (checked recently)")
return joblib.load(cached_path)
if remote_mtime > local_mtime:
print("Downloading updated model")
cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, force_download=True)
else:
print("Cached model is up-to-date")
else:
print("Downloading model for the first time")
cached_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
except Exception as e:
print(f"Error checking/downloading model: {e}")
print(f"Error type: {type(e)}")
print(f"Error details: {str(e)}")
raise HTTPException(status_code=500, detail="Unable to download or find the model.")
return joblib.load(cached_path)
model = get_latest_model()
print("Model loaded successfully")
class InputData(BaseModel):
features: list[float]
@app.post("/predict")
async def predict(data: InputData):
try:
input_data = np.array(data.features).reshape(1, -1)
prediction = model.predict(input_data)
return {"prediction": prediction.tolist()}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
@app.get("/")
async def root():
return {"message": "NPK Needs Prediction Model API"} |