changed pose size

app.py

@@ -1,6 +1,6 @@
-# Copied megadetector section from https://huggingface.co/spaces/hlydecker/MegaDetector_v5 
-# Copied from https://huggingface.co/spaces/sofmi/MegaDetector_DLClive/blob/main/app.py
-# Copied from https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels/blob/main/app.py 
+# Built from https://huggingface.co/spaces/hlydecker/MegaDetector_v5 
+# Built from https://huggingface.co/spaces/sofmi/MegaDetector_DLClive/blob/main/app.py
+# Built from https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels/blob/main/app.py 
 
 
 import gradio as gr
@@ -60,7 +60,7 @@ gr_pose_font_input = gr.inputs.Dropdown(choices=['amiko', 'nature', 'painter', '
 gr_slider_font_size = gr.inputs.Slider(5,30,1,8,
                                         label='Set pose font size')
 
-gr_slider_pose_size = gr.inputs.Slider(1,20,1,2,
+gr_slider_pose_size = gr.inputs.Slider(0.5,5,0.2,2,
                                         label='Set pose size')                                          
 gr_mega_model_input = gr.inputs.Dropdown(choices=['md_v5a','md_v5b'],
                                          default='md_v5a', # default option
@@ -345,8 +345,8 @@ outputs = [gr_gallery_output, #gr_json_output,
 gr_title = "MegaDetector v5 + DLClive by Sofi, Nesli, Nirel, Vic"
 gr_description = "Detect and estimate the pose of animals in camera trap images, using MegaDetector v5a + DeepLabCut-live. \
                   Builds up on work from <a href='https://huggingface.co/spaces/hlydecker/MegaDetector_v5'>hlydecker/MegaDetector_v5</a> \
-                  <a href='https://huggingface.co/spaces/sofmi/MegaDetector_DLClive</a> \
-                  <a href='https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels</a>"
+                  <a href='https://huggingface.co/spaces/sofmi/MegaDetector_DLClive'>sofmi/MegaDetector_DLClive</a> \
+                  <a href='https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels'>Neslihan/megadetector_dlcmodels</a>" 
 # article = "<p style='text-align: center'>This app makes predictions using a YOLOv5x6 model that was trained to detect animals, humans, and vehicles in camera trap images; find out more about the project on <a href='https://github.com/microsoft/CameraTraps'>GitHub</a>. This app was built by Henry Lydecker but really depends on code and models developed by <a href='http://ecologize.org/'>Ecologize</a> and <a href='http://aka.ms/aiforearth'>Microsoft AI for Earth</a>. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset and developed by Ultralytics, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
 # examples = [['data/Macropod.jpg'], ['data/koala2.jpg'],['data/cat.jpg'],['data/BrushtailPossum.jpg']]