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Dockerfile

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+# Usa una imagen base de Python
+FROM python:3.10.8
+
+# Establece el directorio de trabajo
+WORKDIR /code
+
+# Copia los archivos necesarios al contenedor
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# Ajusta los permisos de los archivos
+RUN chmod -R 777 /code
+
+
+CMD ["sh", "-c", "uvicorn app:app --host 0.0.0.0 --port $PORT"]

app.py

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+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 aplicación
+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": "¡Bienvenido a la API de predicción! Visita /docs para ver la documentación."}
+
+# Ruta de predicción
+@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 está cargado.")
+    try:
+        # Convertir los datos de entrada a un array de NumPy para la predicción
+        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))

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:188bfde544e2d865ebc9874bad96a3389d555b4ccba4300e3407deaa127eb5a6
+size 16648

model.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 5], "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_input"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "batch_input_shape": [null, 5], "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.12.0", "backend": "tensorflow"}