Spaces:
Sleeping
Sleeping
Commit 路
bbc1f44
1
Parent(s): 6d3561e
deploy cat vs dog
Browse files- .ipynb_checkpoints/app-checkpoint.ipynb +191 -0
- app.ipynb +230 -0
- app.py +20 -4
- cat.jpeg +0 -0
- catdog.jpeg +0 -0
- dog.jpeg +0 -0
- model.pkl +3 -0
.ipynb_checkpoints/app-checkpoint.ipynb
ADDED
|
@@ -0,0 +1,191 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 2,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"ename": "ModuleNotFoundError",
|
| 10 |
+
"evalue": "No module named 'fastai'",
|
| 11 |
+
"output_type": "error",
|
| 12 |
+
"traceback": [
|
| 13 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 14 |
+
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
| 15 |
+
"Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#export\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mall\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgradio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgr\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_cat\u001b[39m(x): \u001b[38;5;28;01mreturn\u001b[39;00m x[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39misupper()\n",
|
| 16 |
+
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastai'"
|
| 17 |
+
]
|
| 18 |
+
}
|
| 19 |
+
],
|
| 20 |
+
"source": [
|
| 21 |
+
"#export\n",
|
| 22 |
+
"\n",
|
| 23 |
+
"from fastai.vision.all import *\n",
|
| 24 |
+
"import gradio as gr\n",
|
| 25 |
+
"\n",
|
| 26 |
+
"def is_cat(x): return x[0].isupper()"
|
| 27 |
+
]
|
| 28 |
+
},
|
| 29 |
+
{
|
| 30 |
+
"cell_type": "markdown",
|
| 31 |
+
"metadata": {},
|
| 32 |
+
"source": [
|
| 33 |
+
"Primero importamos la liberia de FastAI y de gradio.\n",
|
| 34 |
+
"Luego cargamos una imagen de prueba."
|
| 35 |
+
]
|
| 36 |
+
},
|
| 37 |
+
{
|
| 38 |
+
"cell_type": "code",
|
| 39 |
+
"execution_count": 3,
|
| 40 |
+
"metadata": {},
|
| 41 |
+
"outputs": [
|
| 42 |
+
{
|
| 43 |
+
"data": {
|
| 44 |
+
"image/jpeg": "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",
|
| 45 |
+
"image/png": "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",
|
| 46 |
+
"text/plain": [
|
| 47 |
+
"PILImage mode=RGB size=128x128"
|
| 48 |
+
]
|
| 49 |
+
},
|
| 50 |
+
"execution_count": 3,
|
| 51 |
+
"metadata": {},
|
| 52 |
+
"output_type": "execute_result"
|
| 53 |
+
}
|
| 54 |
+
],
|
| 55 |
+
"source": [
|
| 56 |
+
"im_dog = PILImage.create('dog.jpeg')\n",
|
| 57 |
+
"im_dog.thumbnail((128,128))\n",
|
| 58 |
+
"im_dog"
|
| 59 |
+
]
|
| 60 |
+
},
|
| 61 |
+
{
|
| 62 |
+
"cell_type": "markdown",
|
| 63 |
+
"metadata": {},
|
| 64 |
+
"source": [
|
| 65 |
+
"Cargamos el modelo ya entrenado. (Si es un gato o no)."
|
| 66 |
+
]
|
| 67 |
+
},
|
| 68 |
+
{
|
| 69 |
+
"cell_type": "code",
|
| 70 |
+
"execution_count": 5,
|
| 71 |
+
"metadata": {},
|
| 72 |
+
"outputs": [],
|
| 73 |
+
"source": [
|
| 74 |
+
"#export\n",
|
| 75 |
+
"learn = load_learner('model.pkl')"
|
| 76 |
+
]
|
| 77 |
+
},
|
| 78 |
+
{
|
| 79 |
+
"cell_type": "code",
|
| 80 |
+
"execution_count": 6,
|
| 81 |
+
"metadata": {},
|
| 82 |
+
"outputs": [
|
| 83 |
+
{
|
| 84 |
+
"data": {
|
| 85 |
+
"text/plain": [
|
| 86 |
+
"('False', tensor(0), tensor([9.9999e-01, 1.3617e-05]))"
|
| 87 |
+
]
|
| 88 |
+
},
|
| 89 |
+
"execution_count": 6,
|
| 90 |
+
"metadata": {},
|
| 91 |
+
"output_type": "execute_result"
|
| 92 |
+
}
|
| 93 |
+
],
|
| 94 |
+
"source": [
|
| 95 |
+
"learn.predict(im_dog)"
|
| 96 |
+
]
|
| 97 |
+
},
|
| 98 |
+
{
|
| 99 |
+
"cell_type": "markdown",
|
| 100 |
+
"metadata": {},
|
| 101 |
+
"source": [
|
| 102 |
+
"Creamos una funci贸n para gradio. Este necesita la informaci贸n en un diccionario con las categorias y las probabilidades."
|
| 103 |
+
]
|
| 104 |
+
},
|
| 105 |
+
{
|
| 106 |
+
"cell_type": "code",
|
| 107 |
+
"execution_count": 8,
|
| 108 |
+
"metadata": {},
|
| 109 |
+
"outputs": [],
|
| 110 |
+
"source": [
|
| 111 |
+
"#export \n",
|
| 112 |
+
"categories = ('cat', 'dog')\n",
|
| 113 |
+
"\n",
|
| 114 |
+
"def classify_image(img):\n",
|
| 115 |
+
" pred, idx, probs = learn.predict(img)\n",
|
| 116 |
+
" return dict(zip(categories, map(float, probs)))"
|
| 117 |
+
]
|
| 118 |
+
},
|
| 119 |
+
{
|
| 120 |
+
"cell_type": "markdown",
|
| 121 |
+
"metadata": {},
|
| 122 |
+
"source": [
|
| 123 |
+
"Aqu铆 un ejemplo de lo que retorna la funci贸n."
|
| 124 |
+
]
|
| 125 |
+
},
|
| 126 |
+
{
|
| 127 |
+
"cell_type": "code",
|
| 128 |
+
"execution_count": null,
|
| 129 |
+
"metadata": {},
|
| 130 |
+
"outputs": [],
|
| 131 |
+
"source": [
|
| 132 |
+
"classify_image(im_dog)"
|
| 133 |
+
]
|
| 134 |
+
},
|
| 135 |
+
{
|
| 136 |
+
"cell_type": "code",
|
| 137 |
+
"execution_count": 1,
|
| 138 |
+
"metadata": {},
|
| 139 |
+
"outputs": [
|
| 140 |
+
{
|
| 141 |
+
"ename": "NameError",
|
| 142 |
+
"evalue": "name 'gr' is not defined",
|
| 143 |
+
"output_type": "error",
|
| 144 |
+
"traceback": [
|
| 145 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 146 |
+
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
| 147 |
+
"Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#export\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m image \u001b[38;5;241m=\u001b[39m \u001b[43mgr\u001b[49m\u001b[38;5;241m.\u001b[39minputs\u001b[38;5;241m.\u001b[39mImage(shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m192\u001b[39m, \u001b[38;5;241m192\u001b[39m))\n\u001b[1;32m 3\u001b[0m label \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39moutputs\u001b[38;5;241m.\u001b[39mLabel()\n\u001b[1;32m 4\u001b[0m examples \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdog.jpeg\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcat.jpeg\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcatdog.jpeg\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
|
| 148 |
+
"\u001b[0;31mNameError\u001b[0m: name 'gr' is not defined"
|
| 149 |
+
]
|
| 150 |
+
}
|
| 151 |
+
],
|
| 152 |
+
"source": [
|
| 153 |
+
"#export\n",
|
| 154 |
+
"image = gr.inputs.Image(shape=(192, 192))\n",
|
| 155 |
+
"label = gr.outputs.Label()\n",
|
| 156 |
+
"examples = [\"dog.jpeg\", \"cat.jpeg\", \"catdog.jpeg\"]\n",
|
| 157 |
+
"\n",
|
| 158 |
+
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
|
| 159 |
+
"intf.launch(inline=False)\n"
|
| 160 |
+
]
|
| 161 |
+
},
|
| 162 |
+
{
|
| 163 |
+
"cell_type": "code",
|
| 164 |
+
"execution_count": null,
|
| 165 |
+
"metadata": {},
|
| 166 |
+
"outputs": [],
|
| 167 |
+
"source": []
|
| 168 |
+
}
|
| 169 |
+
],
|
| 170 |
+
"metadata": {
|
| 171 |
+
"kernelspec": {
|
| 172 |
+
"display_name": "Python 3 (ipykernel)",
|
| 173 |
+
"language": "python",
|
| 174 |
+
"name": "python3"
|
| 175 |
+
},
|
| 176 |
+
"language_info": {
|
| 177 |
+
"codemirror_mode": {
|
| 178 |
+
"name": "ipython",
|
| 179 |
+
"version": 3
|
| 180 |
+
},
|
| 181 |
+
"file_extension": ".py",
|
| 182 |
+
"mimetype": "text/x-python",
|
| 183 |
+
"name": "python",
|
| 184 |
+
"nbconvert_exporter": "python",
|
| 185 |
+
"pygments_lexer": "ipython3",
|
| 186 |
+
"version": "3.8.10"
|
| 187 |
+
}
|
| 188 |
+
},
|
| 189 |
+
"nbformat": 4,
|
| 190 |
+
"nbformat_minor": 2
|
| 191 |
+
}
|
app.ipynb
ADDED
|
@@ -0,0 +1,230 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 10,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"#export\n",
|
| 10 |
+
"\n",
|
| 11 |
+
"from fastai.vision.all import *\n",
|
| 12 |
+
"import gradio as gr\n",
|
| 13 |
+
"\n",
|
| 14 |
+
"def is_cat(x): return x[0].isupper()"
|
| 15 |
+
]
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"cell_type": "markdown",
|
| 19 |
+
"metadata": {},
|
| 20 |
+
"source": [
|
| 21 |
+
"Primero importamos la liberia de FastAI y de gradio.\n",
|
| 22 |
+
"Luego cargamos una imagen de prueba."
|
| 23 |
+
]
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"cell_type": "code",
|
| 27 |
+
"execution_count": 11,
|
| 28 |
+
"metadata": {},
|
| 29 |
+
"outputs": [
|
| 30 |
+
{
|
| 31 |
+
"data": {
|
| 32 |
+
"image/jpeg": "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",
|
| 33 |
+
"image/png": "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",
|
| 34 |
+
"text/plain": [
|
| 35 |
+
"PILImage mode=RGB size=128x128"
|
| 36 |
+
]
|
| 37 |
+
},
|
| 38 |
+
"execution_count": 11,
|
| 39 |
+
"metadata": {},
|
| 40 |
+
"output_type": "execute_result"
|
| 41 |
+
}
|
| 42 |
+
],
|
| 43 |
+
"source": [
|
| 44 |
+
"im_dog = PILImage.create('dog.jpeg')\n",
|
| 45 |
+
"im_dog.thumbnail((128,128))\n",
|
| 46 |
+
"im_dog"
|
| 47 |
+
]
|
| 48 |
+
},
|
| 49 |
+
{
|
| 50 |
+
"cell_type": "markdown",
|
| 51 |
+
"metadata": {},
|
| 52 |
+
"source": [
|
| 53 |
+
"Cargamos el modelo ya entrenado. (Si es un gato o no)."
|
| 54 |
+
]
|
| 55 |
+
},
|
| 56 |
+
{
|
| 57 |
+
"cell_type": "code",
|
| 58 |
+
"execution_count": 12,
|
| 59 |
+
"metadata": {},
|
| 60 |
+
"outputs": [],
|
| 61 |
+
"source": [
|
| 62 |
+
"#export\n",
|
| 63 |
+
"learn = load_learner('model.pkl')"
|
| 64 |
+
]
|
| 65 |
+
},
|
| 66 |
+
{
|
| 67 |
+
"cell_type": "code",
|
| 68 |
+
"execution_count": 13,
|
| 69 |
+
"metadata": {},
|
| 70 |
+
"outputs": [
|
| 71 |
+
{
|
| 72 |
+
"data": {
|
| 73 |
+
"text/plain": [
|
| 74 |
+
"('False', tensor(0), tensor([9.9999e-01, 1.3617e-05]))"
|
| 75 |
+
]
|
| 76 |
+
},
|
| 77 |
+
"execution_count": 13,
|
| 78 |
+
"metadata": {},
|
| 79 |
+
"output_type": "execute_result"
|
| 80 |
+
}
|
| 81 |
+
],
|
| 82 |
+
"source": [
|
| 83 |
+
"learn.predict(im_dog)"
|
| 84 |
+
]
|
| 85 |
+
},
|
| 86 |
+
{
|
| 87 |
+
"cell_type": "markdown",
|
| 88 |
+
"metadata": {},
|
| 89 |
+
"source": [
|
| 90 |
+
"Creamos una funci贸n para gradio. Este necesita la informaci贸n en un diccionario con las categorias y las probabilidades."
|
| 91 |
+
]
|
| 92 |
+
},
|
| 93 |
+
{
|
| 94 |
+
"cell_type": "code",
|
| 95 |
+
"execution_count": 14,
|
| 96 |
+
"metadata": {},
|
| 97 |
+
"outputs": [],
|
| 98 |
+
"source": [
|
| 99 |
+
"#export \n",
|
| 100 |
+
"categories = ('cat', 'dog')\n",
|
| 101 |
+
"\n",
|
| 102 |
+
"def classify_image(img):\n",
|
| 103 |
+
" pred, idx, probs = learn.predict(img)\n",
|
| 104 |
+
" return dict(zip(categories, map(float, probs)))"
|
| 105 |
+
]
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"cell_type": "markdown",
|
| 109 |
+
"metadata": {},
|
| 110 |
+
"source": [
|
| 111 |
+
"Aqu铆 un ejemplo de lo que retorna la funci贸n."
|
| 112 |
+
]
|
| 113 |
+
},
|
| 114 |
+
{
|
| 115 |
+
"cell_type": "code",
|
| 116 |
+
"execution_count": 15,
|
| 117 |
+
"metadata": {},
|
| 118 |
+
"outputs": [
|
| 119 |
+
{
|
| 120 |
+
"data": {
|
| 121 |
+
"text/plain": [
|
| 122 |
+
"{'cat': 0.9999864101409912, 'dog': 1.3616780961456243e-05}"
|
| 123 |
+
]
|
| 124 |
+
},
|
| 125 |
+
"execution_count": 15,
|
| 126 |
+
"metadata": {},
|
| 127 |
+
"output_type": "execute_result"
|
| 128 |
+
}
|
| 129 |
+
],
|
| 130 |
+
"source": [
|
| 131 |
+
"classify_image(im_dog)"
|
| 132 |
+
]
|
| 133 |
+
},
|
| 134 |
+
{
|
| 135 |
+
"cell_type": "code",
|
| 136 |
+
"execution_count": 16,
|
| 137 |
+
"metadata": {},
|
| 138 |
+
"outputs": [
|
| 139 |
+
{
|
| 140 |
+
"name": "stderr",
|
| 141 |
+
"output_type": "stream",
|
| 142 |
+
"text": [
|
| 143 |
+
"/var/folders/pg/5_j7b3ss1cdg6_d5hsl953k00000gn/T/ipykernel_39816/3100131070.py:2: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
|
| 144 |
+
" image = gr.inputs.Image(shape=(192, 192))\n",
|
| 145 |
+
"/var/folders/pg/5_j7b3ss1cdg6_d5hsl953k00000gn/T/ipykernel_39816/3100131070.py:2: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
|
| 146 |
+
" image = gr.inputs.Image(shape=(192, 192))\n",
|
| 147 |
+
"/var/folders/pg/5_j7b3ss1cdg6_d5hsl953k00000gn/T/ipykernel_39816/3100131070.py:3: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
|
| 148 |
+
" label = gr.outputs.Label()\n",
|
| 149 |
+
"/var/folders/pg/5_j7b3ss1cdg6_d5hsl953k00000gn/T/ipykernel_39816/3100131070.py:3: GradioUnusedKwargWarning: You have unused kwarg parameters in Label, please remove them: {'type': 'auto'}\n",
|
| 150 |
+
" label = gr.outputs.Label()\n"
|
| 151 |
+
]
|
| 152 |
+
},
|
| 153 |
+
{
|
| 154 |
+
"name": "stdout",
|
| 155 |
+
"output_type": "stream",
|
| 156 |
+
"text": [
|
| 157 |
+
"Running on local URL: http://127.0.0.1:7860\n",
|
| 158 |
+
"\n",
|
| 159 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
| 160 |
+
]
|
| 161 |
+
},
|
| 162 |
+
{
|
| 163 |
+
"data": {
|
| 164 |
+
"text/plain": []
|
| 165 |
+
},
|
| 166 |
+
"execution_count": 16,
|
| 167 |
+
"metadata": {},
|
| 168 |
+
"output_type": "execute_result"
|
| 169 |
+
}
|
| 170 |
+
],
|
| 171 |
+
"source": [
|
| 172 |
+
"#export\n",
|
| 173 |
+
"image = gr.inputs.Image(shape=(192, 192))\n",
|
| 174 |
+
"label = gr.outputs.Label()\n",
|
| 175 |
+
"examples = [\"dog.jpeg\", \"cat.jpeg\", \"catdog.jpeg\"]\n",
|
| 176 |
+
"\n",
|
| 177 |
+
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
|
| 178 |
+
"intf.launch(inline=False)\n"
|
| 179 |
+
]
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"attachments": {},
|
| 183 |
+
"cell_type": "markdown",
|
| 184 |
+
"metadata": {},
|
| 185 |
+
"source": [
|
| 186 |
+
"Para exportar el codigo a un archivo .py y subir a huggingface. usamos la siguiente funci贸n."
|
| 187 |
+
]
|
| 188 |
+
},
|
| 189 |
+
{
|
| 190 |
+
"cell_type": "code",
|
| 191 |
+
"execution_count": 41,
|
| 192 |
+
"metadata": {},
|
| 193 |
+
"outputs": [],
|
| 194 |
+
"source": [
|
| 195 |
+
"from notebook2script import *"
|
| 196 |
+
]
|
| 197 |
+
},
|
| 198 |
+
{
|
| 199 |
+
"cell_type": "code",
|
| 200 |
+
"execution_count": 39,
|
| 201 |
+
"metadata": {},
|
| 202 |
+
"outputs": [],
|
| 203 |
+
"source": [
|
| 204 |
+
"\n",
|
| 205 |
+
"convert_notebook('app.ipynb', 'app.py')"
|
| 206 |
+
]
|
| 207 |
+
}
|
| 208 |
+
],
|
| 209 |
+
"metadata": {
|
| 210 |
+
"kernelspec": {
|
| 211 |
+
"display_name": "Python 3 (ipykernel)",
|
| 212 |
+
"language": "python",
|
| 213 |
+
"name": "python3"
|
| 214 |
+
},
|
| 215 |
+
"language_info": {
|
| 216 |
+
"codemirror_mode": {
|
| 217 |
+
"name": "ipython",
|
| 218 |
+
"version": 3
|
| 219 |
+
},
|
| 220 |
+
"file_extension": ".py",
|
| 221 |
+
"mimetype": "text/x-python",
|
| 222 |
+
"name": "python",
|
| 223 |
+
"nbconvert_exporter": "python",
|
| 224 |
+
"pygments_lexer": "ipython3",
|
| 225 |
+
"version": "3.8.10"
|
| 226 |
+
}
|
| 227 |
+
},
|
| 228 |
+
"nbformat": 4,
|
| 229 |
+
"nbformat_minor": 2
|
| 230 |
+
}
|
app.py
CHANGED
|
@@ -1,7 +1,23 @@
|
|
|
|
|
| 1 |
import gradio as gr
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
|
| 3 |
-
|
| 4 |
-
|
|
|
|
|
|
|
| 5 |
|
| 6 |
-
|
| 7 |
-
|
|
|
|
| 1 |
+
|
| 2 |
import gradio as gr
|
| 3 |
+
from fastai.vision.all import *
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
def is_cat(x):
|
| 7 |
+
return x[0].isupper()
|
| 8 |
+
|
| 9 |
+
learn = load_learner("model.pkl")
|
| 10 |
+
|
| 11 |
+
categories = ("cat", "dog")
|
| 12 |
+
|
| 13 |
+
def classify_image(img):
|
| 14 |
+
pred, idx, probs = learn.predict(img)
|
| 15 |
+
return dict(zip(categories, map(float, probs)))
|
| 16 |
|
| 17 |
+
#export
|
| 18 |
+
image = gr.inputs.Image(shape=(192, 192))
|
| 19 |
+
label = gr.outputs.Label()
|
| 20 |
+
examples = ["dog.jpeg", "cat.jpeg", "catdog.jpeg"]
|
| 21 |
|
| 22 |
+
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
|
| 23 |
+
intf.launch(inline=False)
|
cat.jpeg
ADDED
|
catdog.jpeg
ADDED
|
dog.jpeg
ADDED
|
model.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:94f19b7408db9a5dd226cfb986d4904c10ee1ccdaa08fa64cd90b1f705975ebd
|
| 3 |
+
size 47063121
|