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BrighitBravoAmes
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Parent(s):
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Upload 5 files
Browse files- Dockerfile +15 -0
- app.py +52 -0
- gitattributes +35 -0
- model.h5 +3 -0
- model.json +1 -0
Dockerfile
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# Usa una imagen base de Python
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FROM python:3.9
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# Establece el directorio de trabajo
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WORKDIR /code
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# Copia los archivos necesarios al contenedor
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir -r /code/requirements.txt
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COPY . .
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RUN chmod -R 777 /code
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# Comando para ejecutar la aplicación
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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app.py
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from keras.api.models import Sequential
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from keras.api.layers import InputLayer, Dense
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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import numpy as np
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from typing import List
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class InputData(BaseModel):
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data: List[float] # Lista de características numéricas (flotantes)
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app = FastAPI()
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# Función para construir el modelo manualmente
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def build_model():
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model = Sequential(
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[
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InputLayer(
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input_shape=(2,), name="dense_2_input"
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), # Ajusta el tamaño de entrada según tu modelo
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Dense(16, activation="relu", name="dense_2"),
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Dense(1, activation="sigmoid", name="dense_3"),
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]
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)
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model.load_weights(
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"model.h5"
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) # Asegúrate de que los nombres de las capas coincidan para que los pesos se carguen correctamente
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model.compile(
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loss="mean_squared_error", optimizer="adam", metrics=["binary_accuracy"]
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)
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return model
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model = build_model() # Construir el modelo al iniciar la aplicación
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# Ruta de predicción
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@app.post("/predict/")
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async def predict(data: InputData):
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print(f"Data: {data}")
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global model
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try:
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# Convertir la lista de entrada a un array de NumPy para la predicción
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input_data = np.array(data.data).reshape(
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1, -1
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) # 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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gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tar filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:c77ee4f3b494b48b05355ea63692c68a94499890f2f8044406b6c45ea785599f
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size 13864
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model.json
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{"class_name": "Sequential", "config": {"name": "sequential_1", "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 2], "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_2_input"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "batch_input_shape": [null, 2], "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 2]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 16]}}]}, "keras_version": "2.15.0", "backend": "tensorflow"}
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