Update app.py

app.py

@@ -1,35 +1,32 @@
 from fastapi import FastAPI, HTTPException
-from keras.models import model_from_json,Model
+from tensorflow.keras.models import model_from_json
 from pydantic import BaseModel
-from keras.models import Sequential
-from keras.layers import Dense
 import numpy as np
 
-# Definición del modelo de datos de entrada
 class InputData(BaseModel):
-    data: list  # Asumiendo que la entrada es una lista de características numéricas
+    data: list
 
 app = FastAPI()
 
-# Carga del modelo
 def load_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("Cargado el modelo en el disco")
-    loaded_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])
-    return loaded_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:
-        # Convertir la lista de entrada a un array de NumPy para la predicción
-        input_data = np.array(data.data).reshape(1, -1)  # Asumiendo que la entrada debe ser de forma (1, num_features)
+        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))
+        raise HTTPException(status_code=500, detail=str(e))