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import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
import os | |
from fastapi import FastAPI, HTTPException | |
from pydantic import BaseModel | |
from typing import List | |
from tensorflow.keras.models import model_from_json | |
from sklearn.preprocessing import StandardScaler | |
class InputData(BaseModel): | |
data: List[float] # Lista de caracter铆sticas num茅ricas (flotantes) | |
app = FastAPI() | |
# Cargar el modelo desde JSON | |
with open('model.json', 'r') as json_file: | |
model_json = json_file.read() | |
model = model_from_json(model_json) | |
# Cargar los pesos en el modelo | |
model.load_weights('model_weights.h5') | |
# Ruta de predicci贸n | |
async def predict(data: InputData): | |
try: | |
# Convertir la lista de entrada a un array de NumPy para la predicci贸n | |
input_data = np.array(data.data).reshape(1, -1) | |
# Escalar los datos (aseg煤rate de que el escalador ha sido entrenado y guardado adecuadamente) | |
scaler = StandardScaler() | |
input_data = scaler.fit_transform(input_data) | |
# Realizar la predicci贸n | |
prediction = model.predict(input_data).round() | |
return {"prediction": prediction.tolist()} | |
except Exception as e: | |
raise HTTPException(status_code=500, detail=str(e)) |