Trying to deploy TF2 Object detection model fine tuned for custom data using MobilenetV2 SSD model

Using Streamlit
,
1

Currently my prediction code works for single test image in Jupyter Notebook (below). But I need to built it on a web api and test the same. Request to kindly help or provide some leads to achieve the same.

#Model based detection
import io
import os
import scipy.misc
import numpy as np
import six
import time
import glob
from IPython.display import display

from six import BytesIO

import matplotlib
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont

import tensorflow as tf
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

%matplotlib inline    
def load_image_into_numpy_array(path):

  img_data = tf.io.gfile.GFile(path, 'rb').read()
  image = Image.open(BytesIO(img_data))
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)

category_index = label_map_util.create_category_index_from_labelmap('Models/Annotation/label_map.pbtxt', use_display_name=True)

tf.keras.backend.clear_session()
model = tf.saved_model.load(f'Models/saved_model/')
def run_inference_for_single_image(model, image):
  image = np.asarray(image)
  # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
  input_tensor = tf.convert_to_tensor(image)
  # The model expects a batch of images, so add an axis with `tf.newaxis`.
  input_tensor = input_tensor[tf.newaxis,...]

  # Run inference
  model_fn = model.signatures['serving_default']
  output_dict = model_fn(input_tensor)

  # All outputs are batches tensors.
  # Convert to numpy arrays, and take index [0] to remove the batch dimension.
  # We're only interested in the first num_detections.
  num_detections = int(output_dict.pop('num_detections'))
  output_dict = {key:value[0, :num_detections].numpy() 
                 for key,value in output_dict.items()}
  output_dict['num_detections'] = num_detections

  # detection_classes should be ints.
  output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
   
  # Handle models with masks:
  if 'detection_masks' in output_dict:
    # Reframe the the bbox mask to the image size.
    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
              output_dict['detection_masks'], output_dict['detection_boxes'],
               image.shape[0], image.shape[1])      
    detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
                                       tf.uint8)
    output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
    
  return output_dict
for image_path in glob.glob('images/Group_A_405.jpg'):
  image_np = load_image_into_numpy_array(image_path)
  output_dict = run_inference_for_single_image(model, image_np)
  scores = np.squeeze(output_dict['detection_scores'])
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks_reframed', None),
      use_normalized_coordinates=True,
      max_boxes_to_draw=50,
       min_score_thresh=.45,
      line_thickness=8)
  display(Image.fromarray(image_np))

final_score = np.squeeze(scores)    
count = 0
for i in range(100):
        if scores is None or final_score[i] > 0.45:
                count = count + 1
print(count)