app.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import numpy as np
import joblib
import requests
from io import BytesIO

app = FastAPI()

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

MODEL_URL = "https://huggingface.co/GodfreyOwino/NPK_needs_mode2/resolve/main/npk_needs_model.joblib"

try:
    response = requests.get(MODEL_URL)
    response.raise_for_status()
    model_bytes = BytesIO(response.content)
    model = joblib.load(model_bytes)
    print("Model loaded successfully")
except Exception as e:
    print(f"Error loading model: {e}")
    print(f"Error type: {type(e)}")
    print(f"Error details: {str(e)}")
    raise HTTPException(status_code=500, detail="Unable to load the model.")

class InputData(BaseModel):
    features: list[float]

@app.post("/predict", tags=["Prediction"])
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("/", tags=["Root"])
async def root():
    return {"message": "NPK Needs Prediction Model API"}