Update app.py

app.py

@@ -1,7 +1,6 @@
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
 import tensorflow as tf
 import tf_bodypix
 from tf_bodypix.api import download_model, load_model, BodyPixModelPaths
@@ -30,44 +29,33 @@ rainbow = [
 def process_images(front_img, side_img, real_height_cm):
     fimage_array = preprocessing.image.img_to_array(front_img)
     simage_array = preprocessing.image.img_to_array(side_img)
-
+    
     # bodypix prediction
     frontresult = bodypix_model.predict_single(fimage_array)
     sideresult = bodypix_model.predict_single(simage_array)
-
+    
     front_mask = frontresult.get_mask(threshold=0.75)
     side_mask = sideresult.get_mask(threshold=0.75)
-
-    # preprocessing.image.save_img(f'{output_path}/frontbodypix-mask.jpg',front_mask)
-    # preprocessing.image.save_img(f'{output_path}/sidebodypix-mask.jpg',side_mask)
-
+    
     front_colored_mask = frontresult.get_colored_part_mask(front_mask, rainbow)
     side_colored_mask = sideresult.get_colored_part_mask(side_mask, rainbow)
-
-    # preprocessing.image.save_img(f'{output_path}/frontbodypix-colored-mask.jpg',front_colored_mask)
-    # preprocessing.image.save_img(f'{output_path}/sidebodypix-colored-mask.jpg',side_colored_mask)
-
+    
     frontposes = frontresult.get_poses()
     front_image_with_poses = draw_poses(
-        fimage_array.copy(), # create a copy to ensure we are not modifing the source image
+        fimage_array.copy(),
         frontposes,
         keypoints_color=(255, 100, 100),
         skeleton_color=(100, 100, 255)
     )
-
+    
     sideposes = sideresult.get_poses()
     side_image_with_poses = draw_poses(
-        simage_array.copy(), # create a copy to ensure we are not modifing the source image
+        simage_array.copy(),
         sideposes,
         keypoints_color=(255, 100, 100),
         skeleton_color=(100, 100, 255)
     )
-    # print(np.array(simage).shape)
-    # print(np.array(side_colored_mask).shape)
-
-    # preprocessing.image.save_img(f'{output_path}/frontbodypix-poses.jpg', front_image_with_poses)
-    # preprocessing.image.save_img(f'{output_path}/sidebodypix-poses.jpg', side_image_with_poses)
-
+    
     body_sizes = measure_body_sizes(side_colored_mask, front_colored_mask, sideposes, frontposes, real_height_cm, rainbow)
     measurements_df = pd.DataFrame([body_sizes[0]])
     return measurements_df
@@ -76,15 +64,15 @@ def process_images(front_img, side_img, real_height_cm):
 interface = gr.Interface(
     fn=process_images,
     inputs=[
-        gr.Image(source="webcam", type="numpy", label="Front Pose"),
-        gr.Image(source="webcam", type="numpy", label="Side Pose"),
+        gr.Image(sources="webcam", type="numpy", label="Front Pose"),
+        gr.Image(sources="webcam", type="numpy", label="Side Pose"),
         gr.Number(label="Enter Your Height (cm)")
     ],
     outputs=[
         gr.DataFrame(label="Body Size Measurements")
     ],
     title="Body Sizing System Demo",
-    description="Upload two images: Front View and Side View, and input the height in cm."
+    description="Capture two webcam images: Front View and Side View, and input your height in cm."
 )
 
 # Launch the app