cwd

app.py

@@ -2,13 +2,16 @@ import gradio as gr
 import torch
 from PIL import Image
 
+import os
+cwd = os.getcwd()
+
 # Images
 #torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
 #torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
 
 # Model
 #model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
-model =  torch.hub.load('/yolov5', 'custom', path='/saved_model/s1000_best.pt', source='local')  # local model
+model =  torch.hub.load(cwd+'/yolov5', 'custom', path=cwd+'/saved_model/s1000_best.pt', source='local')  # local model
 
 
 def yolo(im, size=640):
@@ -30,7 +33,7 @@ article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled o
           "and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |" \
           "<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
 
-path_folder = '/datasets/s1000/'
+path_folder = cwd+'/datasets/s1000/'
 examples = [[path_folder+'s1000 (1).png'], [path_folder+'s1000 (2).png'],[path_folder+'s1000 (3).png'],[path_folder+'s1000 (4).png'],[path_folder+'s1000 (5).png']]
 gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(
     debug=True)