import cv2
import numpy as np
import tensorflow as tf
import time
import random
from typing import Any, Dict, List

class EndpointHandler:

  def __init__(self, path=""):

    self.model = tf.saved_model.load(f'{path}/my_model')
    self.classes_1 = ["RoadAccidents", "Fighting", "NormalVideos"]
    self.locations = ['Miami', 'Smouha', 'Mandara', 'Sporting', 'Montazah']

  def preprocess_frame(self, frame):
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame = cv2.resize(frame, (224, 224))
    frame = frame.astype('float32') / 255.0
    frame = np.expand_dims(frame, axis=0)
    return frame

  def get_top_k(self, probs, k=1, label_map=None):
    if label_map is None:
      label_map = self.classes_1
    top_predictions = tf.argsort(probs, axis=-1, direction='DESCENDING')[:k]
    top_labels = tf.gather(label_map, top_predictions, axis=-1)
    top_labels = [label.decode('utf8') for label in top_labels.numpy()]
    top_probs = tf.gather(probs, top_predictions, axis=-1).numpy()
    return top_labels[0]

  def perform_action_recognition(self, url, k=1):

    cap = cv2.VideoCapture(url)
    start_time = time.time()

    while True:
      ret, frame = cap.read()
      if not ret:
        break

      preprocessed_frame = self.preprocess_frame(frame)
      outputs = self.model.signatures['serving_default'](image=preprocessed_frame[tf.newaxis])
      probs = tf.nn.softmax(outputs['classifier_head_1'])
      current_time = time.time() - start_time
      m, s = divmod(current_time, 60)
      h, m = divmod(m, 60)
      ip_address = url.split("/")[-1]
      output = {
          "class": self.get_top_k(probs[0], k=k),
          "elapsed_time": f"{int(h):02d}:{int(m):02d}:{int(s):02d}",
          "location": random.choice(self.locations),
          "ip_address": ip_address
      }
      yield output

    cap.release()

  def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:

    url = data.get("url")

    if url is None:
      raise ValueError("'url' is missing from the request body")

    if not isinstance(url, str):
      raise ValueError(f"Expected 'url' to be a str, but found {type(url)}")

    outputs = []
    for output in self.perform_action_recognition(url):
      outputs.append(output)

    return outputs