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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
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"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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},
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/cvd1/pytorch/default/1/model.pkl\n",
"/kaggle/input/dog-jpeg/dog.jpeg\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-07T16:42:00.539921Z",
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},
"trusted": true
},
"outputs": [],
"source": [
"#|default.exp app"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_kg_hide-input": true,
"execution": {
"iopub.execute_input": "2024-11-07T16:42:00.548268Z",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting gradio\n",
" Downloading gradio-5.5.0-py3-none-any.whl.metadata (16 kB)\n",
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"Installing collected packages: tomlkit, semantic-version, ruff, python-multipart, ffmpy, starlette, huggingface-hub, safehttpx, gradio-client, fastapi, gradio\n",
" Attempting uninstall: tomlkit\n",
" Found existing installation: tomlkit 0.13.2\n",
" Uninstalling tomlkit-0.13.2:\n",
" Successfully uninstalled tomlkit-0.13.2\n",
" Attempting uninstall: python-multipart\n",
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" Uninstalling starlette-0.37.2:\n",
" Successfully uninstalled starlette-0.37.2\n",
" Attempting uninstall: huggingface-hub\n",
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" Attempting uninstall: fastapi\n",
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" Uninstalling fastapi-0.111.0:\n",
" Successfully uninstalled fastapi-0.111.0\n",
"Successfully installed fastapi-0.115.4 ffmpy-0.4.0 gradio-5.5.0 gradio-client-1.4.2 huggingface-hub-0.26.2 python-multipart-0.0.12 ruff-0.7.2 safehttpx-0.1.1 semantic-version-2.10.0 starlette-0.41.2 tomlkit-0.12.0\n"
]
}
],
"source": [
"#|export\n",
"from fastai.vision.all import *\n",
"!pip install gradio\n",
"import gradio as gr\n",
"\n",
"share = True\n",
"def is_cat(x): return x[0].isupper()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
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},
"trusted": true
},
"outputs": [],
"source": [
"#|export\n",
"learn = load_learner('/kaggle/input/cvd1/pytorch/default/1/model.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
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},
"trusted": true
},
"outputs": [],
"source": [
"#|export\n",
"categories = ('Dog', 'Cat')\n",
"\n",
"def classify_image(img):\n",
" pred,idx,probs = learn.predict(img)\n",
" return dict(zip(categories, map(float, probs)))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7860\n",
"* Running on public URL: https://f0396da6689b4b4b39.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#|export\n",
"image = \"image\"\n",
"label = \"label\"\n",
"\n",
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)\n",
"intf.launch(inline=False, share = True)"
]
}
],
"metadata": {
"kaggle": {
"accelerator": "none",
"dataSources": [
{
"datasetId": 5660894,
"sourceId": 9340954,
"sourceType": "datasetVersion"
},
{
"isSourceIdPinned": true,
"modelId": 157980,
"modelInstanceId": 135253,
"sourceId": 159107,
"sourceType": "modelInstanceVersion"
}
],
"dockerImageVersionId": 30761,
"isGpuEnabled": false,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|