Update app.py

app.py

@@ -5,7 +5,6 @@ except:
     os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
 import spaces
 import cv2
-import supervision as sv
 import torch
 from matplotlib.pyplot import axis
 import gradio as gr
@@ -32,15 +31,8 @@ cfg.MODEL.ROI_HEADS.NUM_CLASSES = 11
 cfg.MODEL.WEIGHTS = model_path
 
 my_metadata = MetadataCatalog.get("dbmdz_coco_all")
-#my_metadata.thing_classes = ["GRAFFITI", "FADED_SIGNAGE","POTHOLES","GARBAGE","CONSTRUCTION_ROAD","BROKEN_SIGNAGE","BAD_STREETLIGHT","BAD_BILLBOARD","SAND_ON_ROAD","CLUTTER_SIDEWALK","UNKEPT_FACADE"]
 my_metadata.thing_classes = ["None", "BAD_BILLBOARD","BROKEN_SIGNAGE","CLUTTER_SIDEWALK","CONSTRUCTION_ROAD","FADED_SIGNAGE","GARBAGE","GRAFFITI","POTHOLES","SAND_ON_ROAD","UNKEPT_FACADE"]
 
-# #smart_dict={'GRAFFITI' : 0.0 , 'FADED_SIGNAGE': 1.0 , 'POTHOLES': 2.0,
-#             'GARBAGE' : 3.0 , 'CONSTRUCTION_ROAD': 4.0 , 'BROKEN_SIGNAGE': 5.0,
-#             'BAD_STREETLIGHT' : 6.0 , 'BAD_BILLBOARD': 7.0 , 'SAND_ON_ROAD':8.0,
-#             'CLUTTER_SIDEWALK' : 9.0 , 'UNKEPT_FACADE': 10.0}
-if not torch.cuda.is_available():
-    cfg.MODEL.DEVICE = "cpu"
 
     
 def predict_frame(frame,_):
@@ -57,6 +49,7 @@ def inference(image_url, image, min_score):
     cfg.MODEL.DEVICE = "cpu"
   else:
     cfg.MODEL.DEVICE = "cuda"
+      print( cfg.MODEL.DEVICE)
     if image_url:
         r = requests.get(image_url)
         if r:
@@ -81,7 +74,6 @@ def inference(image_url, image, min_score):
 def process_vid(video_path):
 
   cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
-  torch.cuda.empty_cache()
   if not torch.cuda.is_available():
     cfg.MODEL.DEVICE = "cpu"
   else:
@@ -148,39 +140,10 @@ def process_vid(video_path):
   return output_path
 
 
-
-
-def infer_video(video_path):
-    sv.process_video(source_path=video_path, target_path=f"result.mp4", callback=predict_frame)
-    return f"result.mp4"
-
-
 title = "Smartathon Phase2 Demo - Baseer"
 description = "This demo introduces an interactive playground for our trained Detectron2 model."
 article = '<p>Detectron model is available from our repository <a href="https://github.com/asalhi/Smartathon-Baseer">here</a>.</p>'
 
-# gr.Interface(
-#     inference,
-#     [gr.inputs.Textbox(label="Image URL", placeholder=""),
-#      gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image"),
-#      gr.Slider(minimum=0.0, maximum=1.0, value=0.4, label="Minimum score"),
-#     ],
-#     gr.outputs.Image(type="pil", label="Output"),
-#     #gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image")),
-#     title=title,
-#     description=description,
-#     article=article,
-#     #examples=[['./d1.jpeg'], ['./d2.jpeg'], ['./d3.jpeg'],['./d4.jpeg'],['./d5.jpeg'],['./d6.jpeg']],
-#     examples = gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image")),
-#     cache_examples=False).launch()
-#     #examples=['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg']
-
-
-
-
-# gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=input_image)
-
-