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from fastapi import FastAPI, HTTPException
from tensorflow.keras.models import model_from_json
from pydantic import BaseModel
import numpy as np
class InputData(BaseModel):
data: list
app = FastAPI()
def load_model():
try:
with open("model.json", 'r') as json_file:
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model.h5")
loaded_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])
return loaded_model
except Exception as e:
print(f"Error cargando el modelo: {str(e)}")
raise
model = load_model()
@app.post("/predict/")
async def predict(data: InputData):
try:
input_data = np.array(data.data).reshape(1, -1)
prediction = model.predict(input_data).round()
return {"prediction": prediction.tolist()}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
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