{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# Define the core prediction function\n",
"def predict_cat_dog(image):\n",
" # Preprocess image\n",
" print(type(image))\n",
" image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image\n",
" image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale\n",
" image = np.array(image)\n",
" image = np.expand_dims(image, axis=0) # same as image[None, ...]\n",
" \n",
" # Predict\n",
" prediction = model.predict(image)\n",
" \n",
" # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability\n",
" # we could also change the output layer to dense(1, activation='sigmoid')\n",
" prediction = np.round(float(tf.sigmoid(prediction)), 2)\n",
" p_jolteon = prediction\n",
" p_eevee = (1 - prediction)\n",
" p_dratini = (2 - prediction)\n",
" return {'jolteon': p_jolteon, 'eevee': p_eevee, 'dratini': p_dratini}"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7862\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
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"execution_count": 20,
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"name": "stdout",
"output_type": "stream",
"text": [
"\n"
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{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\route_utils.py\", line 270, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1847, in process_api\n",
" result = await self.call_function(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1433, in call_function\n",
" prediction = await anyio.to_thread.run_sync(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
" result = context.run(func, *args)\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\utils.py\", line 805, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"C:\\Users\\dom-k\\AppData\\Local\\Temp\\ipykernel_33600\\3230146116.py\", line 11, in predict_cat_dog\n",
" prediction = model.predict(image)\n",
"NameError: name 'model' is not defined\n"
]
}
],
"source": [
"# Create the Gradio interface\n",
"input_image = gr.Image()\n",
"iface = gr.Interface(\n",
" fn=predict_cat_dog,\n",
" inputs=input_image, \n",
" outputs=gr.Label(),\n",
" examples=[\"images_pokemon/Dratini1.png\", \"images_pokemon/Dratini2.png\", \"images_pokemon/Dratini3.png\", \"images_pokemon/Dratini4.png\", \"images_pokemon/Dratini5.png\",\n",
" \"images_pokemon/Eevee1.png\", \"images_pokemon/Eevee2.png\", \"images_pokemon/Eevee3.png\", \"images_pokemon/Eevee4.png\", \"images_pokemon/Eevee5.png\",\n",
" \"images_pokemon/Jolteon1.png\", \"images_pokemon/Jolteon2.png\", \"images_pokemon/Jolteon3.png\", \"images_pokemon/Jolteon4.png\", \"images_pokemon/Jolteon5.png\"], \n",
" description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
"iface.launch()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7863\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
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"text/html": [
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"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\queueing.py\", line 527, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\route_utils.py\", line 270, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1847, in process_api\n",
" result = await self.call_function(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\blocks.py\", line 1433, in call_function\n",
" prediction = await anyio.to_thread.run_sync(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2134, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 851, in run\n",
" result = context.run(func, *args)\n",
" File \"c:\\Users\\dom-k\\anaconda3\\envs\\KIA\\lib\\site-packages\\gradio\\utils.py\", line 805, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"C:\\Users\\dom-k\\AppData\\Local\\Temp\\ipykernel_33600\\3230146116.py\", line 11, in predict_cat_dog\n",
" prediction = model.predict(image)\n",
"NameError: name 'model' is not defined\n"
]
}
],
"source": [
"import gradio as gr\n",
"\n",
"def greet(name, intensity):\n",
" return \"Hello, \" + name + \"!\" * int(intensity)\n",
"\n",
"demo = gr.Interface(\n",
" fn=greet,\n",
" inputs=[\"text\", \"slider\"],\n",
" outputs=[\"text\"],\n",
")\n",
"\n",
"demo.launch()\n"
]
}
],
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