Delete app.py

app.py

@@ -1,74 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-from six import BytesIO
-from PIL import Image
-import tensorflow as tf
-from object_detection.utils import label_map_util
-from object_detection.utils import visualization_utils as viz_utils
-from object_detection.utils import ops as utils_op
-import tarfile
-import wget 
-import gradio as gr
-from huggingface_hub import snapshot_download
-import os 
-
-PATH_TO_LABELS = 'data/label_map.pbtxt'   
-category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
-
-def pil_image_as_numpy_array(pilimg):
-
-    img_array = tf.keras.utils.img_to_array(pilimg)
-    img_array = np.expand_dims(img_array, axis=0)
-    return img_array
-    
-def load_image_into_numpy_array(path):
-                                    
-    image = None
-    image_data = tf.io.gfile.GFile(path, 'rb').read()
-    image = Image.open(BytesIO(image_data))
-    return pil_image_as_numpy_array(image)            
-
-def load_model():
-    # download_dir = snapshot_download(REPO_ID)
-    download_dir = os.path.join(download_dir, "saved_model")
-    saved_model_dir = os.path.join(download_dir, "saved_model")
-    detection_model = tf.saved_model.load(saved_model_dir)
-    return detection_model
-
-def load_model2():
-    wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
-    tarfile.open("balloon_model.tar.gz").extractall()
-    model_dir = 'saved_model'    
-    detection_model = tf.saved_model.load(str(model_dir))
-    return detection_model    
-
-# samples_folder = 'test_samples
-# image_path = 'test_samples/sample_balloon.jpeg
-# 
-
-def predict(pilimg):
-
-    image_np = pil_image_as_numpy_array(pilimg)
-    return predict2(image_np)
-
-def predict2(image_np):
-
-    results = detection_model(image_np)
-
-    # different object detection models have additional results
-    result = {key:value.numpy() for key,value in results.items()}
-    
-    label_id_offset = 0
-    image_np_with_detections = image_np.copy()
-
-    viz_utils.visualize_boxes_and_labels_on_image_array(
-        image_np_with_detections[0],
-        result['detection_boxes'][0],
-        (result['detection_classes'][0] + label_id_offset).astype(int),
-        result['detection_scores'][0],
-        category_index,
-        use_normalized_coordinates=True,
-        max_boxes_to_draw=200,
-        min_score_thresh=.60,
-        agnostic_mode=False,
-        line_thickness=2)