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Update app.py (#2)
Browse files- Update app.py (3585217f47b69a09617a7c64f6ca9ec39a3bf1ce)
Co-authored-by: Jairo MT <JairoDanielMT@users.noreply.huggingface.co>
app.py
CHANGED
@@ -1,35 +1,32 @@
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from fastapi import FastAPI, HTTPException
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from keras.models import model_from_json
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from pydantic import BaseModel
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from keras.models import Sequential
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from keras.layers import Dense
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import numpy as np
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# Definición del modelo de datos de entrada
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class InputData(BaseModel):
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data: list
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app = FastAPI()
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# Carga del modelo
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def load_model():
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model = load_model()
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@app.post("/predict/")
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async def predict(data: InputData):
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try:
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input_data = np.array(data.data).reshape(1, -1) # Asumiendo que la entrada debe ser de forma (1, num_features)
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prediction = model.predict(input_data).round()
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return {"prediction": prediction.tolist()}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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from fastapi import FastAPI, HTTPException
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from tensorflow.keras.models import model_from_json
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from pydantic import BaseModel
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import numpy as np
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class InputData(BaseModel):
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data: list
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app = FastAPI()
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def load_model():
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try:
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with open("model.json", 'r') as json_file:
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loaded_model_json = json_file.read()
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loaded_model = model_from_json(loaded_model_json)
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loaded_model.load_weights("model.h5")
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loaded_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])
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return loaded_model
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except Exception as e:
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print(f"Error cargando el modelo: {str(e)}")
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raise
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model = load_model()
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@app.post("/predict/")
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async def predict(data: InputData):
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try:
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input_data = np.array(data.data).reshape(1, -1)
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prediction = model.predict(input_data).round()
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return {"prediction": prediction.tolist()}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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