app.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from typing import List
from PIL import Image
import numpy as np
from io import BytesIO
import cv2

app = FastAPI()

def buscar_gato(image):
    existe = "No existe el GATO "
    print("resultado: ", image.shape)
    
    # Cargar el clasificador Haar para gatos
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalcatface.xml')
    
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces_rect = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=9) 
    #cats = cat_cascade.detectMultiScale(gray, 1.3, 5, minSize=(10, 10))
    existe = "No hay persona"
    for (x, y, w, h) in faces_rect:
        existe = "Si hay persona"
        break
    return existe

@app.post('/predict/')
async def predict(file: UploadFile = File(...)):
    try:
        image = Image.open(BytesIO(await file.read()))
        image = np.asarray(image)
        
        prediction = buscar_gato(image)
        
        return {"prediction": prediction}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))