Upload 4 files

app.py

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+import pickle
+from minisom import MiniSom
+import numpy as np
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List
+
+class InputData(BaseModel):
+    data: List[float]  # Lista de características numéricas (flotantes)
+
+app = FastAPI()
+
+# Función para construir el modelo manualmente
+def build_model():
+    with open('somecoli.pkl', 'rb') as fid:
+        somecoli = pickle.load(fid)
+    MM = np.loadtxt('matrizMM.txt', delimiter=" ")
+    return somecoli,MM
+
+som,MM = build_model()  # Construir el modelo al iniciar la aplicación
+
+# Ruta de predicción
+@app.post("/predict/")
+async def predict(data: InputData):
+    print(f"Data: {data}")
+    global som
+    global MM
+    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 = [float(f) for f in input_data]
+        w = som.winner(input_data)
+        prediction = MM[w]        
+        
+        return {"prediction": prediction.tolist()}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

matrizMM.txt

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+0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 7.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 7.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 4.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 4.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00
+5.000000000000000000e+00 0.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 0.000000000000000000e+00 7.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 1.000000000000000000e+00 7.000000000000000000e+00
+5.000000000000000000e+00 5.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 1.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00
+5.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00
+5.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 1.000000000000000000e+00
+4.000000000000000000e+00 6.000000000000000000e+00 3.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 7.000000000000000000e+00 1.000000000000000000e+00 4.000000000000000000e+00 4.000000000000000000e+00 1.000000000000000000e+00

requirements.txt

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+tensorflow
+minisom
+fastapi
+numpy
+pydantic

somecoli.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6fef630a7f5255a12c23eb9bc352990011bc0e2b82e0c0c602e1f4700ee270b
+size 11594