Update app.py

app.py

@@ -3,14 +3,15 @@ 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 
+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
+
 
 PATH_TO_LABELS = 'label_map.pbtxt'   
 category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
@@ -43,9 +44,7 @@ def load_model2():
     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):
 
@@ -55,10 +54,8 @@ def predict(pilimg):
 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()
 
@@ -75,7 +72,6 @@ def predict2(image_np):
         line_thickness=3)
 
     result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
-    
     return result_pil_img
 
 
@@ -95,6 +91,6 @@ gr.Interface(fn=predict,
              title = title,
              description = description,
              css=css_code,
-             inputs=gr.Image(type="pil", height=309),
-             outputs=gr.Image(type="pil", height=350)
+             inputs=gr.Image(type="pil", height=250),
+             outputs=gr.Image(type="pil", height=250)
              ).launch(share=True)