I cleaned up my code to make it easier to read.
Result:
2  0.91796875 ← it’s ok but the image has 3 people and i need that total.
import numpy as np
import cv2

from PIL import Image
from tflite_runtime.interpreter import Interpreter

def load_labels(path):
  with open(path, 'r') as f:
    return {i: line.strip() for i, line in enumerate(f.readlines())}

def set_input_tensor(interpreter, image):
  tensor_index = interpreter.get_input_details()[0]['index']
  input_tensor = interpreter.tensor(tensor_index)()[0]
  input_tensor[:, :] = image

def classify_image(interpreter, image, top_k=1):
  """Returns a sorted array of classification results."""
  set_input_tensor(interpreter, image)
  interpreter.invoke()
  output_details = interpreter.get_output_details()[0]
  output = np.squeeze(interpreter.get_tensor(output_details['index']))

  # If the model is quantized (uint8 data), then dequantize the results
  if output_details['dtype'] == np.uint8:
    scale, zero_point = output_details['quantization']
    output = scale * (output - zero_point)

  ordered = np.argpartition(-output, top_k)
  return [(i, output[i]) for i in ordered[:top_k]]

labels = load_labels("person.txt")

interpreter = Interpreter("model.tflite")
interpreter.allocate_tensors()
_, height, width, _ = interpreter.get_input_details()[0]['shape']

image = cv2.imread("3people.jpg")
up_points = (height, width)
image = cv2.resize(image, up_points, interpolation= cv2.INTER_LINEAR)

results = classify_image(interpreter, image)
label_id, prob = results[0]

print ( label_id, prob )