First local working version!

.gitignore

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+bin
+lib
+output
+share

=1.12

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+Requirement already satisfied: xtcocotools in ./lib/python3.10/site-packages (1.14.3)
+Requirement already satisfied: cython>=0.27.3 in ./lib/python3.10/site-packages (from xtcocotools) (3.0.7)
+Requirement already satisfied: numpy>=1.20.0 in ./lib/python3.10/site-packages (from xtcocotools) (1.23.0)
+Requirement already satisfied: matplotlib>=2.1.0 in ./lib/python3.10/site-packages (from xtcocotools) (3.7.4)
+Requirement already satisfied: setuptools>=18.0 in ./lib/python3.10/site-packages (from xtcocotools) (65.5.0)
+Requirement already satisfied: kiwisolver>=1.0.1 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (1.4.5)
+Requirement already satisfied: cycler>=0.10 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (0.12.1)
+Requirement already satisfied: contourpy>=1.0.1 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (1.2.0)
+Requirement already satisfied: pillow>=6.2.0 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (9.4.0)
+Requirement already satisfied: packaging>=20.0 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (23.2)
+Requirement already satisfied: fonttools>=4.22.0 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (4.47.0)
+Requirement already satisfied: python-dateutil>=2.7 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (2.8.2)
+Requirement already satisfied: pyparsing>=2.3.1 in ./lib/python3.10/site-packages (from matplotlib>=2.1.0->xtcocotools) (2.4.5)
+Requirement already satisfied: six>=1.5 in ./lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=2.1.0->xtcocotools) (1.16.0)

app.py

@@ -23,34 +23,34 @@ warnings.filterwarnings("ignore")
 mmpose_model_list = ["human", "hand", "face", "animal", "wholebody",
                      "vitpose", "vitpose-s", "vitpose-b", "vitpose-l", "vitpose-h"]
 
-
 def save_image(img, img_path):
     # Convert PIL image to OpenCV image
     img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
     # Save OpenCV image
     cv2.imwrite(img_path, img)
 
-
-def download_test_image():
-    # Images
-    torch.hub.download_url_to_file(
-        'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg',
-        'bus.jpg')
-    torch.hub.download_url_to_file(
-        'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg',
-        'dogs.jpg')
-    torch.hub.download_url_to_file(
-        'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg',
-        'zidane.jpg')
+# def download_test_image():
+#     # Images
+#     torch.hub.download_url_to_file(
+#         'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg',
+#         'bus.jpg')
+#     torch.hub.download_url_to_file(
+#         'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg',
+#         'dogs.jpg')
+#     torch.hub.download_url_to_file(
+#         'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg',
+#         'zidane.jpg')
 
 
-def predict_pose(img, model_name, out_dir):
+def predict_pose(img, model_name):
     img_path = "input_img.jpg"
+    out_dir = "./output";
     save_image(img, img_path)
     device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
     inferencer = MMPoseInferencer(model_name, device=device)
     result_generator = inferencer(img_path, show=False, out_dir=out_dir)
     result = next(result_generator)
+    print(result)
     save_dir = './output/visualizations/'
     if os.path.exists(save_dir):
         out_img_path = save_dir + img_path
@@ -58,25 +58,14 @@ def predict_pose(img, model_name, out_dir):
     else:
         out_img_path = img_path
     out_img = PIL.Image.open(out_img_path)
-    return out_img
+    return (out_img, result)
 
-download_test_image()
+# download_test_image()
 input_image = gr.inputs.Image(type='pil', label="Original Image")
 model_name = gr.inputs.Dropdown(choices=[m for m in mmpose_model_list], label='Model')
-out_dir = gr.inputs.Textbox(label="Output Directory", default="./output")
 output_image = gr.outputs.Image(type="pil", label="Output Image")
+output_text = gr.outputs.Textbox(label="Output Text")
 
-examples = [
-    ['zidane.jpg', 'human'],
-    ['dogs.jpg', 'animal'],
-]
-title = "MMPose detection web demo"
-description = "<div align='center'><img src='https://raw.githubusercontent.com/open-mmlab/mmpose/main/resources/mmpose-logo.png' width='450''/><div>" \
-              "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmpose'>MMPose</a> MMPose 是一款基于 PyTorch 的姿态分析的开源工具箱，是 OpenMMLab 项目的成员之一。" \
-              "OpenMMLab Pose Estimation Toolbox and Benchmark..</p>"
-article = "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmpose'>MMPose</a></p>" \
-          "<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>"
-
-iface = gr.Interface(fn=predict_pose, inputs=[input_image, model_name, out_dir], outputs=output_image,
-                     examples=examples, title=title, description=description, article=article)
+title = "MMPose detection for ShopByShape"
+iface = gr.Interface(fn=predict_pose, inputs=[input_image, model_name], outputs=[output_image, output_text], title=title)
 iface.launch()

pyvenv.cfg

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+home = /usr/local/bin
+include-system-site-packages = false
+version = 3.10.11