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{
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{
"cell_type": "code",
"execution_count": 2,
"id": "dd03eb44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"token\n",
"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\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/david/Documents/python-env-test/venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:101: FutureWarning: `name` and `organization` input arguments are deprecated and will be removed in v0.10. Pass `repo_id` instead.\n",
" warnings.warn(\n",
"Cloning https://huggingface.co/datasets/HuggingDavid/simple-mnist-flagging into local empty directory.\n"
]
},
{
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"text/plain": [
"Download file img/tmp7qxdqjtl.png: 46%|####5 | 8.28k/18.1k [00:00<?, ?B/s]"
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"text/plain": [
"Clean file img/tmp7qxdqjtl.png: 6%|5 | 1.00k/18.1k [00:00<?, ?B/s]"
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"text/plain": [
"Download file img/tmpb9pmlzsj.png: 100%|##########| 15.4k/15.4k [00:00<?, ?B/s]"
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"Clean file img/tmpb9pmlzsj.png: 7%|6 | 1.00k/15.4k [00:00<?, ?B/s]"
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"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7880\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7880/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
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"<IPython.core.display.HTML object>"
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"data": {
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"(<gradio.routes.App at 0x162231e40>, 'http://127.0.0.1:7880/', None)"
]
},
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"metadata": {},
"output_type": "execute_result"
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{
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"text/plain": [
"Upload file img/tmpjuysmmri.png: 100%|##########| 17.6k/17.6k [00:00<?, ?B/s]"
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{
"name": "stderr",
"output_type": "stream",
"text": [
"remote: Scanning LFS files for validity, may be slow... \n",
"remote: LFS file scan complete. \n",
"To https://huggingface.co/datasets/HuggingDavid/simple-mnist-flagging\n",
" 4b19b7d..458cf22 main -> main\n",
"\n"
]
}
],
"source": [
"import torch\n",
"import gradio as gr\n",
"from torchvision import transforms\n",
"from PIL import ImageOps\n",
"import os\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()\n",
"\n",
"hf_writer = gr.HuggingFaceDatasetSaver(os.getenv('HF_TOKEN'), \"simple-mnist-flagging\")\n",
"\n",
"def load_model():\n",
" model_dict = torch.load('linear_model.pt')\n",
" return model_dict\n",
"\n",
"model = load_model()\n",
"convert_tensor = transforms.ToTensor()\n",
"\n",
"def predict(img):\n",
" img = ImageOps.grayscale(img).resize((28,28))\n",
" image_tensor = convert_tensor(img).view(28*28)\n",
" res = image_tensor @ model['weights'] + model['bias']\n",
" res = res.sigmoid()\n",
" return {\"It's 3\": float(res), \"It's 7\": float(1-res)}\n",
"\n",
"title = \"Is it 7 or 3\"\n",
"description = '<p><center>Write a number, 7 or 3, in the middle.</center></p>'\n",
"\n",
"gr.Interface(fn=predict, \n",
" inputs=gr.Paint(type=\"pil\", invert_colors=True),\n",
" outputs=gr.Label(num_top_classes=2),\n",
" title=title,\n",
" flagging_options=[\"incorrect\",\"ambiguous\"],\n",
" flagging_callback=hf_writer,\n",
" description=description,\n",
" allow_flagging='manual').launch()"
]
}
],
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