File size: 1,298 Bytes
dada16f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
from fastapi import FastAPI, HTTPException
import numpy as np
from keras.models import model_from_json
app = FastAPI()
loaded_model = None
# Cargar el modelo al iniciar la aplicaci贸n
def load_model():
global loaded_model
json_file = open("model.json", 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model.h5")
print("Modelo cargado en el disco")
app.add_event_handler("startup", load_model)
# Ruta de inicio
@app.get("/")
async def read_root():
return {"message": "隆Bienvenido a la API de predicci贸n! Visita /docs para ver la documentaci贸n."}
# Ruta de predicci贸n
@app.get("/predict/{x0}/{x1}/{x2}/{x3}/{x4}")
async def predict(x0: float, x1: float, x2: float, x3: float, x4: float):
global loaded_model
if loaded_model is None:
raise HTTPException(status_code=500, detail="El modelo no est谩 cargado.")
try:
# Convertir los datos de entrada a un array de NumPy para la predicci贸n
input_data = np.array([[x0, x1, x2, x3, x4]])
prediction = loaded_model.predict(input_data).round()
return {"prediction": prediction.tolist()}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
|