Switching from double quotes to single quotes

app.py

@@ -12,11 +12,11 @@ def load_model(current_path):
     data_path = os.path.join(current_path, 'data')
     if not os.path.isdir(data_path):
         os.mkdir(data_path)
-        url = "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC1M_s1x.yaml"
+        url = 'https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC1M_s1x.yaml'
         wget.download(url, os.path.join(data_path, 'config.yaml'))
-        url = "https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1M_s1x/217144516/model_final_48a9d9.pkl"
+        url = 'https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_WC1M_s1x/217144516/model_final_48a9d9.pkl'
         wget.download(url, os.path.join(data_path, 'weights.pkl'))
-        url = "https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/Base-DensePose-RCNN-FPN.yaml"
+        url = 'https://raw.githubusercontent.com/facebookresearch/detectron2/main/projects/DensePose/configs/Base-DensePose-RCNN-FPN.yaml'
         wget.download(url, os.path.join(data_path, 'Base-DensePose-RCNN-FPN.yaml'))
 
 current_path = os.getcwd()
@@ -27,14 +27,14 @@ densepose_weights = os.path.join(current_path, 'data', 'weights.pkl')
 texture_processor = TextureProcessor(densepose_config, densepose_weights)
 
 inputs = [
-    gr.inputs.Image(label="Person Image", type='numpy'),
-    gr.inputs.Image(label="Model Image (with clothes)", type='numpy')
+    gr.inputs.Image(label='Person Image', type='numpy'),
+    gr.inputs.Image(label='Model Image (with clothes)', type='numpy')
 ]
 
-outputs = gr.outputs.Image(label="Result Image", type='numpy')
+outputs = gr.outputs.Image(label='Result Image', type='numpy')
 
-title = "JustClothify"
-description = "Upload an image of a person and an image of a model with clothes, the system will generate an image of a person wearing these clothes."
+title = 'JustClothify'
+description = 'Upload an image of a person and an image of a model with clothes, the system will generate an image of a person wearing these clothes.'
 
 gr.Interface(
     fn=image_processing, 

helpers/processor.py

@@ -65,7 +65,7 @@ class TextureProcessor:
     extractor = CompoundExtractor([extractor])
 
     with torch.no_grad():
-      outputs = self.predictor(original_image)["instances"]
+      outputs = self.predictor(original_image)['instances']
     
     image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
     image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
@@ -82,7 +82,7 @@ class TextureProcessor:
       return iuv
 
   def parse_bbox(self, result):
-      return result["pred_boxes_XYXY"][0].cpu().numpy()
+      return result['pred_boxes_XYXY'][0].cpu().numpy()
 
   def interpolate_tex(self, tex):
       valid_mask = np.array((tex.sum(0) != 0) * 1, dtype='uint8')
@@ -166,27 +166,27 @@ class TextureProcessor:
       add_densepose_config(cfg)
       cfg.merge_from_file(config_fpath)
       cfg.MODEL.WEIGHTS = model_fpath
-      cfg.MODEL.DEVICE = "cpu"
+      cfg.MODEL.DEVICE = 'cpu'
       cfg.freeze()
       return cfg
 
   def execute(self, image):
       context = {'results': []}
       with torch.no_grad():
-          outputs = self.predictor(image)["instances"]
+          outputs = self.predictor(image)['instances']
           self.execute_on_outputs(context, outputs)
-      return context["results"]
+      return context['results']
 
   def execute_on_outputs(self, context: Dict[str, Any], outputs: Instances):
     result = {}
-    if outputs.has("scores"):
-        result["scores"] = outputs.get("scores").cpu()
-    if outputs.has("pred_boxes"):
-        result["pred_boxes_XYXY"] = outputs.get("pred_boxes").tensor.cpu()
-        if outputs.has("pred_densepose"):
+    if outputs.has('scores'):
+        result['scores'] = outputs.get('scores').cpu()
+    if outputs.has('pred_boxes'):
+        result['pred_boxes_XYXY'] = outputs.get('pred_boxes').tensor.cpu()
+        if outputs.has('pred_densepose'):
             if isinstance(outputs.pred_densepose, DensePoseChartPredictorOutput):
                 extractor = DensePoseResultExtractor()
             elif isinstance(outputs.pred_densepose, DensePoseEmbeddingPredictorOutput):
                 extractor = DensePoseOutputsExtractor()
-            result["pred_densepose"] = extractor(outputs)[0]
-    context["results"].append(result)
+            result['pred_densepose'] = extractor(outputs)[0]
+    context['results'].append(result)