upload space files

.gitattributes

@@ -35,3 +35,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 test_images/ss.png filter=lfs diff=lfs merge=lfs -text
 test_images/ss2.png filter=lfs diff=lfs merge=lfs -text
 test_images/ss3.png filter=lfs diff=lfs merge=lfs -text
+test_images/test_images/ss.png filter=lfs diff=lfs merge=lfs -text
+test_images/test_images/ss2.png filter=lfs diff=lfs merge=lfs -text
+test_images/test_images/ss3.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,5 +1,4 @@
-from pathlib import Path
-import os
+
 import json
 import gradio as gr
 import yolov5
@@ -13,14 +12,7 @@ article = f"<p style='text-align: center'> <a href='https://huggingface.co/{mode
 current_model_id = models_ids[-1]
 model = yolov5.load(current_model_id)
 
-if "test_images":
-    image_filenames = os.listdir(test_images)
-    examples = [
-        [Path(test_images) / image_filename, 0.25, models_ids[-1]]
-        for image_filename in image_filenames
-    ]
-else:
-    examples = None
+examples = [[WindowsPath('space_content/test_images/ss.png'), 0.25, 'keremberke/yolov5m-valorant'], [WindowsPath('space_content/test_images/ss2.png'), 0.25, 'keremberke/yolov5m-valorant'], [WindowsPath('space_content/test_images/ss3.png'), 0.25, 'keremberke/yolov5m-valorant'], [WindowsPath('space_content/test_images/ss4.jpg'), 0.25, 'keremberke/yolov5m-valorant'], [WindowsPath('space_content/test_images/ss5.jpg'), 0.25, 'keremberke/yolov5m-valorant'], [WindowsPath('space_content/test_images/ss6.jpg'), 0.25, 'keremberke/yolov5m-valorant'], [WindowsPath('space_content/test_images/test_images'), 0.25, 'keremberke/yolov5m-valorant']]
 
 
 def predict(image, threshold=0.25, model_id=None):