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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "1c550b9b-ab70-46a5-a584-29a0d3ae31ee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting gradio\n",
" Using cached gradio-4.41.0-py3-none-any.whl.metadata (15 kB)\n",
"Collecting aiofiles<24.0,>=22.0 (from gradio)\n",
" Using cached aiofiles-23.2.1-py3-none-any.whl.metadata (9.7 kB)\n",
"Requirement already satisfied: anyio<5.0,>=3.0 in /home/obai33/.local/lib/python3.10/site-packages (from gradio) (4.4.0)\n",
"Collecting fastapi (from gradio)\n",
" Using cached fastapi-0.112.1-py3-none-any.whl.metadata (27 kB)\n",
"Collecting ffmpy (from gradio)\n",
" Using cached ffmpy-0.4.0-py3-none-any.whl.metadata (2.9 kB)\n",
"Collecting gradio-client==1.3.0 (from gradio)\n",
" Using cached gradio_client-1.3.0-py3-none-any.whl.metadata (7.1 kB)\n",
"Requirement already satisfied: httpx>=0.24.1 in /home/obai33/.local/lib/python3.10/site-packages (from gradio) (0.27.0)\n",
"Collecting huggingface-hub>=0.19.3 (from gradio)\n",
" Using cached huggingface_hub-0.24.5-py3-none-any.whl.metadata (13 kB)\n",
"Collecting importlib-resources<7.0,>=1.3 (from gradio)\n",
" Using cached importlib_resources-6.4.3-py3-none-any.whl.metadata (3.9 kB)\n",
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"Collecting orjson~=3.0 (from gradio)\n",
" Using cached orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (50 kB)\n",
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"Collecting pydantic>=2.0 (from gradio)\n",
" Using cached pydantic-2.8.2-py3-none-any.whl.metadata (125 kB)\n",
"Collecting pydub (from gradio)\n",
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"Collecting ruff>=0.2.2 (from gradio)\n",
" Using cached ruff-0.6.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (25 kB)\n",
"Collecting semantic-version~=2.0 (from gradio)\n",
" Using cached semantic_version-2.10.0-py2.py3-none-any.whl.metadata (9.7 kB)\n",
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"Requirement already satisfied: fsspec in /home/obai33/.local/lib/python3.10/site-packages (from gradio-client==1.3.0->gradio) (2024.5.0)\n",
"Collecting websockets<13.0,>=10.0 (from gradio-client==1.3.0->gradio)\n",
" Using cached websockets-12.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n",
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" Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)\n",
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"Downloading gradio-4.41.0-py3-none-any.whl (12.6 MB)\n",
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"Installing collected packages: pydub, websockets, tomlkit, shellingham, semantic-version, ruff, python-multipart, pydantic-core, orjson, importlib-resources, ffmpy, click, annotated-types, aiofiles, uvicorn, starlette, pydantic, huggingface-hub, typer, gradio-client, fastapi, gradio\n",
"Successfully installed aiofiles-23.2.1 annotated-types-0.7.0 click-8.1.7 fastapi-0.112.1 ffmpy-0.4.0 gradio-4.41.0 gradio-client-1.3.0 huggingface-hub-0.24.5 importlib-resources-6.4.3 orjson-3.10.7 pydantic-2.8.2 pydantic-core-2.20.1 pydub-0.25.1 python-multipart-0.0.9 ruff-0.6.1 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.38.2 tomlkit-0.12.0 typer-0.12.4 uvicorn-0.30.6 websockets-12.0\n"
]
}
],
"source": [
"!pip install gradio"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "c850d5ae-5d43-45fb-91cc-084427440a97",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn.functional as F\n",
"import torchvision\n",
"import matplotlib.pyplot as plt\n",
"import zipfile\n",
"import os\n",
"import gradio as gr\n",
"from PIL import Image\n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "c3d9ca4d-fb14-495e-b405-8a964ecc9a51",
"metadata": {},
"outputs": [],
"source": [
"CHARS = \"~=\" + \" abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789,.'-!?:;\\\"\"\n",
"BLANK = 0\n",
"PAD = 1\n",
"CHARS_DICT = {c: i for i, c in enumerate(CHARS)}\n",
"TEXTLEN = 30\n",
"\n",
"tokens_list = list(CHARS_DICT.keys())\n",
"silence_token = '|'\n",
"\n",
"if silence_token not in tokens_list:\n",
" tokens_list.append(silence_token)\n",
"\n",
"\n",
"def fit_picture(img):\n",
" target_height = 32\n",
" target_width = 400\n",
" \n",
" # Calculate resize dimensions\n",
" aspect_ratio = img.width / img.height\n",
" if aspect_ratio > (target_width / target_height):\n",
" resize_width = target_width\n",
" resize_height = int(target_width / aspect_ratio)\n",
" else:\n",
" resize_height = target_height\n",
" resize_width = int(target_height * aspect_ratio)\n",
" \n",
" # Resize transformation\n",
" resize_transform = transforms.Resize((resize_height, resize_width))\n",
" \n",
" # Pad transformation\n",
" padding_height = (target_height - resize_height) if target_height > resize_height else 0\n",
" padding_width = (target_width - resize_width) if target_width > resize_width else 0\n",
" pad_transform = transforms.Pad((0, 0, padding_width, padding_height), fill=0, padding_mode='constant')\n",
" \n",
" transform = torchvision.transforms.Compose([\n",
" torchvision.transforms.Grayscale(num_output_channels = 1),\n",
" torchvision.transforms.ToTensor(),\n",
" torchvision.transforms.Normalize(0.5,0.5),\n",
" resize_transform,\n",
" pad_transform\n",
" ])\n",
"\n",
" fin_img = transform(img)\n",
" return fin_img\n",
"\n",
"def load_model(filename):\n",
" data = torch.load(filename)\n",
" recognizer.load_state_dict(data[\"recognizer\"])\n",
" optimizer.load_state_dict(data[\"optimizer\"])\n",
"\n",
"def ctc_decode_sequence(seq):\n",
" \"\"\"Removes blanks and repetitions from the sequence.\"\"\"\n",
" ret = []\n",
" prev = BLANK\n",
" for x in seq:\n",
" if prev != BLANK and prev != x:\n",
" ret.append(prev)\n",
" prev = x\n",
" if seq[-1] == 66:\n",
" ret.append(66)\n",
" return ret\n",
"\n",
"def ctc_decode(codes):\n",
" \"\"\"Decode a batch of sequences.\"\"\"\n",
" ret = []\n",
" for cs in codes.T:\n",
" ret.append(ctc_decode_sequence(cs))\n",
" return ret\n",
"\n",
"\n",
"def decode_text(codes):\n",
" chars = [CHARS[c] for c in codes]\n",
" return ''.join(chars)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "6722e370-e7df-4efe-aa9d-e9436d3cc08e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Device: cuda\n"
]
}
],
"source": [
"class Residual(torch.nn.Module):\n",
" def __init__(self, in_channels, out_channels, stride, pdrop = 0.2):\n",
" super().__init__()\n",
" self.conv1 = torch.nn.Conv2d(in_channels, out_channels, 3, stride, 1)\n",
" self.bn1 = torch.nn.BatchNorm2d(out_channels)\n",
" self.conv2 = torch.nn.Conv2d(out_channels, out_channels, 3, 1, 1)\n",
" self.bn2 = torch.nn.BatchNorm2d(out_channels)\n",
" if in_channels != out_channels or stride != 1:\n",
" self.skip = torch.nn.Conv2d(in_channels, out_channels, 1, stride, 0)\n",
" else:\n",
" self.skip = torch.nn.Identity()\n",
" self.dropout = torch.nn.Dropout2d(pdrop)\n",
"\n",
" def forward(self, x):\n",
" y = torch.nn.functional.relu(self.bn1(self.conv1(x)))\n",
" y = torch.nn.functional.relu(self.bn2(self.conv2(y)) + self.skip(x))\n",
" y = self.dropout(y)\n",
" return y\n",
" \n",
"class TextRecognizer(torch.nn.Module):\n",
" def __init__(self, labels):\n",
" super().__init__()\n",
" self.feature_extractor = torch.nn.Sequential(\n",
" Residual(1, 32, 1),\n",
" Residual(32, 32, 2),\n",
" Residual(32, 32, 1),\n",
" Residual(32, 64, 2),\n",
" Residual(64, 64, 1),\n",
" Residual(64, 128, (2,1)),\n",
" Residual(128, 128, 1),\n",
" Residual(128, 128, (2,1)),\n",
" Residual(128, 128, (2,1)),\n",
" )\n",
" self.recurrent = torch.nn.LSTM(128, 128, 1 ,bidirectional = True)\n",
" self.output = torch.nn.Linear(256, labels)\n",
"\n",
" def forward(self, x):\n",
" x = self.feature_extractor(x)\n",
" x = x.squeeze(2)\n",
" x = x.permute(2,0,1)\n",
" x,_ = self.recurrent(x)\n",
" x = self.output(x)\n",
" return x\n",
"\n",
"recognizer = TextRecognizer(len(CHARS))\n",
"DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"print(\"Device:\", DEVICE)\n",
"LR = 1e-3\n",
"\n",
"recognizer.to(DEVICE)\n",
"optimizer = torch.optim.Adam(recognizer.parameters(), lr=LR)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "e61f1d87-4a82-4714-b4e1-33719064a735",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7889\n",
"Running on public URL: https://e1090d81e4ea8bf190.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://e1090d81e4ea8bf190.gradio.live\" 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": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 536, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 288, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1931, in process_api\n",
" result = await self.call_function(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1516, in call_function\n",
" prediction = await anyio.to_thread.run_sync( # type: ignore\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 2177, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 859, in run\n",
" result = context.run(func, *args)\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/utils.py\", line 826, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"/tmp/ipykernel_848/2152623987.py\", line 5, in ctc_read\n",
" imagefin = fit_picture(image)\n",
" File \"/tmp/ipykernel_848/2382022948.py\", line 19, in fit_picture\n",
" aspect_ratio = img.width / img.height\n",
"AttributeError: 'NoneType' object has no attribute 'width'\n",
"Traceback (most recent call last):\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 536, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 288, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1931, in process_api\n",
" result = await self.call_function(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1516, in call_function\n",
" prediction = await anyio.to_thread.run_sync( # type: ignore\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 2177, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 859, in run\n",
" result = context.run(func, *args)\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/utils.py\", line 826, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"/tmp/ipykernel_848/2152623987.py\", line 5, in ctc_read\n",
" imagefin = fit_picture(image)\n",
" File \"/tmp/ipykernel_848/2382022948.py\", line 19, in fit_picture\n",
" aspect_ratio = img.width / img.height\n",
"AttributeError: 'NoneType' object has no attribute 'width'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 32, 400])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/obai33/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 32, 400])\n",
"torch.Size([1, 1, 32, 400])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/obai33/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 32, 400])\n",
"torch.Size([1, 1, 32, 400])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/obai33/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 32, 400])\n",
"torch.Size([1, 1, 32, 400])\n",
"torch.Size([1, 1, 32, 400])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 536, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 288, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1931, in process_api\n",
" result = await self.call_function(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1516, in call_function\n",
" prediction = await anyio.to_thread.run_sync( # type: ignore\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 2177, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 859, in run\n",
" result = context.run(func, *args)\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/utils.py\", line 826, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"/tmp/ipykernel_848/2152623987.py\", line 5, in ctc_read\n",
" imagefin = fit_picture(image)\n",
" File \"/tmp/ipykernel_848/2382022948.py\", line 19, in fit_picture\n",
" aspect_ratio = img.width / img.height\n",
"AttributeError: 'NoneType' object has no attribute 'width'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 32, 400])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/obai33/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n",
"Traceback (most recent call last):\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 536, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 288, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1931, in process_api\n",
" result = await self.call_function(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1516, in call_function\n",
" prediction = await anyio.to_thread.run_sync( # type: ignore\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 2177, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 859, in run\n",
" result = context.run(func, *args)\n",
" File \"/home/obai33/.local/lib/python3.10/site-packages/gradio/utils.py\", line 826, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"/tmp/ipykernel_848/2152623987.py\", line 5, in ctc_read\n",
" imagefin = fit_picture(image)\n",
" File \"/tmp/ipykernel_848/2382022948.py\", line 19, in fit_picture\n",
" aspect_ratio = img.width / img.height\n",
"AttributeError: 'NoneType' object has no attribute 'width'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1, 32, 400])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/obai33/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n"
]
}
],
"source": [
"load_model('model.pt')\n",
"recognizer.eval()\n",
"\n",
"def ctc_read(image):\n",
" imagefin = fit_picture(image)\n",
" image_tensor = imagefin.unsqueeze(0).to(DEVICE)\n",
" print(image_tensor.size())\n",
" \n",
" with torch.no_grad():\n",
" scores = recognizer(image_tensor)\n",
"\n",
" predictions = scores.argmax(2).cpu().numpy()\n",
"\n",
" decoded_sequences = ctc_decode(predictions)\n",
"\n",
" # Convert decoded sequences to text\n",
" for i in decoded_sequences:\n",
" decoded_text = decode_text(i)\n",
"\n",
" return decoded_text\n",
"\n",
"\n",
"# Gradio Interface\n",
"iface = gr.Interface(\n",
" fn=ctc_read,\n",
" inputs=gr.Image(type=\"pil\"), # PIL Image input\n",
" outputs=\"text\", # Text output\n",
" title=\"Handwritten Text Recognition\",\n",
" description=\"Upload an image, and the custome AI will extract the text.\"\n",
")\n",
"\n",
"iface.launch(share=True)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|