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RubenRicapa commited on Jun 19

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0ea6968

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Dockerfile +18 -0
app.py +128 -0
matrizMM.txt +50 -0
requirements.txt +7 -0
somlucuma.pkl +3 -0

Dockerfile ADDED Viewed

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+# Usa una imagen base de Python
+FROM python:3.9
+# Establece el directorio de trabajo
+WORKDIR /code
+RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6
+# Copia los archivos necesarios al contenedor
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir -r /code/requirements.txt
+COPY . .
+RUN chmod -R 777 /code
+# Comando para ejecutar la aplicación
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py ADDED Viewed

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+import pickle
+from minisom import MiniSom
+import numpy as np
+from PIL import Image
+import math
+import requests
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List
+import os
+import cv2
+class InputData(BaseModel):
+    data: str   # Lista de características numéricas (flotantes)
+app = FastAPI()
+# Función para construir el modelo manualmente
+def build_model():
+    with open('som.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
+def sobel(I):
+    m,n = I.shape# I de 254x254
+    Gx = np.zeros([m-2,n-2],np.float32)# Gx de 252x252
+    Gy = np.zeros([m-2,n-2],np.float32)# Gy de 252x252
+    gx = [[-1,0,1],[ -2,0,2],[ -1,0,1]]
+    gy = [[1,2,1],[ 0,0,0],[ -1,-2,-1]]
+    for j in range(1,m-2):
+        for i in range(1,n-2):
+            Gx[j-1,i-1] = sum(sum(I[j-1:j+2,i-1:i+2]*gx))
+            Gy[j-1,i-1] = sum(sum(I[j-1:j+2,i-1:i+2]*gy))
+    return Gx,Gy
+def medfilt2(G,d=3):
+    m,n = G.shape
+    temp = np.zeros([m+2*(d//2),n+2*(d//2)],np.float32)
+    salida = np.zeros([m,n],np.float32)
+    temp[1:m+1,1:n+1] = G
+    for i in range(1,m):
+        for j in range(1,n):
+            A = np.asarray(temp[i-1:i+2,j-1:j+2]).reshape(-1)
+            salida[i-1,j-1] = np.sort(A)[d+1]
+    return salida
+def orientacion(patron,w):
+    Gx,Gy = sobel(patron)# patron de 254x254
+    Gx = medfilt2(Gx)# Gx de 252x252
+    Gy = medfilt2(Gy)# Gy de 252x252
+    m,n = Gx.shape
+    mOrientaciones = np.zeros([m//w,n//w],np.float32)# de una matriz de 18x18
+    for i in range(m//w):
+        for j in range(n//w):
+            YY = sum(sum(2*Gx[i*w:(i+1)*w,j:j+1]*Gy[i*w:(i+1)*w,j:j+1]))
+            XX = sum(sum(Gx[i*w:(i+1)*w,j:j+1]**2-Gy[i*w:(i+1)*w,j:j+1]**2))
+            #YY = sum(sum(2*Gx[i*w:(i+1)*w,0:1]*Gy[i*w:(i+1)*w,0:1]))
+            #XX = sum(sum(Gx[i*w:(i+1)*w,0:1]**2-Gy[i*w:(i+1)*w,0:1]**2))
+            mOrientaciones[i,j] = (0.5*math.atan2(YY,XX) + math.pi/2.0)*(180.0/math.pi)
+    return mOrientaciones
+def representativo(archivo):
+    im = Image.open(archivo)
+    m,n = im.size
+    imarray = np.array(im,np.float32)
+    patron = imarray[1:m-1,1:n-1]# de 256x256  a 254x254
+    EE = orientacion(patron,14)# retorna EE de 18x18
+    return np.asarray(EE).reshape(-1)
+@app.post("/predict/")
+async def predict(data: InputData):
+    print(f"Data: {data}")
+    global som
+    global MM
+    try:
+        # Descargar la imagen desde la URL
+        respuesta = requests.get(data.data)
+        imagen_temporal = "imagen_temporal.jpg"
+        with open(imagen_temporal, 'wb') as f:
+            f.write(respuesta.content)
+        if respuesta.status_code != 200:
+            raise HTTPException(status_code=400, detail="No se pudo descargar la imagen")
+        img = cv2.imread(imagen_temporal, cv2.IMREAD_GRAYSCALE)
+        resized = cv2.resize(img, (256, 256), interpolation=cv2.INTER_AREA)
+        imagen_normalizada = resized.astype(np.float32) / 255.0
+        cv2.imwrite(imagen_temporal, imagen_normalizada)
+        # Convertir la imagen descargada a un array de NumPy
+        B = representativo(imagen_temporal)
+        w = som.winner(B)
+        os.remove(imagen_temporal)
+        #imagen = Image.open(BytesIO(respuesta.content))
+        #imagen_array = np.array(imagen)
+        # Aquí puedes hacer cualquier cosa que necesites con la imagen
+        # Por ejemplo, procesarla o realizar la predicción
+        # Supongamos que aquí se realiza la predicción con la imagen
+        # Retornar el resultado de la predicción (puedes adaptar esto según tus necesidades)
+        return {"prediction": MM[w]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+'''
+# 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 ADDED Viewed

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requirements.txt ADDED Viewed

	@@ -0,0 +1,7 @@

+tensorflow
+minisom
+fastapi
+numpy
+pydantic
+pillow
+opencv-python

somlucuma.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:a827886bb66ba6be62af74d0ff8afef0114b87d770ef90f402376e255dde7ed3
+size 6544251