app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import numpy as np

app = FastAPI()

class InputData(BaseModel):
    input1: float
    input2: float
    input3: float
    input4: float
    input5: float
    input6: float
    input7: float

# Load the model and handle potential errors gracefully
try:
    model = joblib.load('random_forest_model.joblib')
    status = 'Loaded'
    print(f"Model {status}")
except Exception as e:
    status = f"not loaded: {e}"
    print(f"Model {status}")

@app.get('/')
def health_check():
    # Return the current status of the app (whether the model is loaded or not)
    return {'status': f'{status}'}

@app.post('/predict')
def predict(input: InputData):
    # Ensure the model is loaded before making predictions
    if status != 'Loaded':
        raise HTTPException(status_code=500, detail="Model not loaded. Please check the server logs.")
    
    # Prepare the input data for prediction
    data = np.array([[input.input1, input.input2,
                      input.input3, input.input4,
                      input.input5, input.input6,
                      input.input7]])
    
    # Make prediction using the loaded model
    prediction = model.predict(data).tolist()

    # Return the prediction in JSON format
    return {'prediction': prediction[0]}