bugg in config

.dockerignore

@@ -6,8 +6,6 @@ __pycache__
 .Python
 env
 .env
-Makefile
 page_txt.txt
 page_xml.xml
-helper/text/images/
-helper/text/videos/
+src/tests/

helper/gradio_config.py

@@ -92,11 +92,6 @@ class GradioConfig:
         return f"""
         function monitorButtonHover() {{
 
-            gradioURL = window.location.href
-            if (!gradioURL.endsWith('?__theme=dark')) {{
-                window.location.replace(gradioURL + '?__theme=dark');
-            }}
-
             const buttons = document.querySelectorAll('{button_ids}');
             buttons.forEach(function(button) {{
                 button.addEventListener('mouseenter', function() {{
@@ -110,6 +105,11 @@ class GradioConfig:
         }}
         """
 
+        #     gradioURL = window.location.href
+        # if (!gradioURL.endsWith('?__theme=dark')) {{
+        #     window.location.replace(gradioURL + '?__theme=dark');
+        # }}
+
 
 buttons_with_tooltip = {
     "run_pipeline_button": "Runs HTR on the image. Takes approx 1-2 mins per image (depending on hardware).",

requirements.txt

@@ -1,19 +1,19 @@
-torch
-torchvision
-openmim
-gradio
-pandas
-numpy
-opencv-python-headless
-jinja2
-transformers
-huggingface_hub
-datasets
-requests
+torch==2.0.1
+torchvision==0.15.2
+openmim==0.3.9
+gradio==3.35.2
+pandas==2.0.3
+numpy==1.25.0
+opencv-python-headless==4.7.0.72
+Jinja2==3.1.2
+transformers==4.30.2
+huggingface-hub==0.15.1
+datasets==2.13.1
+requests==2.31.0
 # scipy
 # sklearn
 
-# make install_openmmlab
+# make install_openmmlab (they are excuted in dockerfile)
 # !pip install -U openmim
 # !mim install mmengine
 # !mim install mmcv

test_api.ipynb

@@ -376,82 +376,13 @@
     "\n",
     "client = Client(\"http://127.0.0.1:7860/\")\n",
     "job = client.submit(\n",
-    "    \"./helper/examples/images/1861_R0000277_00153.jpg\",  # str (filepath or URL to image) in 'Image to run HTR-pipeline on' Image component\n",
+    "    \"./test.jpg\",  # str (filepath or URL to image) in 'Image to run HTR-pipeline on' Image component\n",
     "    \"test_api\",  # str  in 'parameter_22' Textbox component\n",
     "    api_name=\"/predict\",\n",
     ")\n",
     "\n",
     "print(job.result())\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7861\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "def create_object(arg):\n",
-    "    return gr.Textbox(value=arg)\n",
-    "\n",
-    "my_objects = {}\n",
-    "\n",
-    "test_list =[\"first\", \"second\"] \n",
-    "for i in test_list:\n",
-    "    object_name = f\"object_{i}\"\n",
-    "    new_object = create_object(i)\n",
-    "    my_objects[object_name] = new_object\n",
-    "\n",
-    "# Accessing objects by their assigned names\n",
-    "first_object = my_objects[\"object_first\"]\n",
-    "second_object = my_objects[\"object_second\"]\n",
-    "\n",
-    "with gr.Blocks() as test:\n",
-    "    with gr.Row():\n",
-    "        first_object.render()\n",
-    "    with gr.Row():\n",
-    "        second_object.render()\n",
-    "\n",
-    "test.launch()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {