init

Dockerfile

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+# Usa una imagen base de Python
+FROM python:3.12.7
+# 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 /code/requirements.txt
+RUN pip install fastapi uvicorn
+
+COPY . .
+
+RUN chmod -R 777 /code
+
+# Comando para ejecutar la aplicación
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException, Query
+from fastapi.responses import HTMLResponse
+from pydantic import BaseModel
+from typing import List
+import cv2
+from PIL import Image
+import numpy as np
+from io import BytesIO
+
+app = FastAPI()
+
+def buscar_existe(image):
+    existe = "NO"
+    print("resultado: ", image.shape)
+    eyeglasses_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') 
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    eyeglasses = eyeglasses_cascade.detectMultiScale(gray, 1.3, 5, minSize=(10, 10))
+    for (x, y, w, h) in eyeglasses:
+        existe = "SI"
+        break
+
+    return existe
+
+# Ruta de predicción
+@app.post('/predict/')
+async def predict(file: UploadFile = File(...), rostro: str = Query(...)):
+    try:
+        image = Image.open(BytesIO(await file.read()))
+        image = np.asarray(image)
+        prediction = buscar_existe(image)
+        return {"prediction": prediction}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirements.txt

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+fastapi
+numpy
+pydantic
+opencv-python-headless
+uvicorn[standard]
+python-multipart
+pillow