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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/loic/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
" warnings.warn(\n",
"/Users/loic/Library/Python/3.9/lib/python/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"dogs-vs-cats-model_transferlearning.keras\"\n",
"model = tf.keras.models.load_model(model_path)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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)[0]), 2)\n",
" p_cat = (1 - prediction)\n",
" p_dog = prediction\n",
" return {'cat': p_cat, 'dog': p_dog}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(), dtype=float32, numpy=0.6341356>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.sigmoid(0.55)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7863/\" 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": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/gradio/queueing.py\", line 527, in process_events\n",
" response = await route_utils.call_process_api(\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/gradio/route_utils.py\", line 261, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/gradio/blocks.py\", line 1786, in process_api\n",
" result = await self.call_function(\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/gradio/blocks.py\", line 1338, in call_function\n",
" prediction = await anyio.to_thread.run_sync(\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/anyio/_backends/_asyncio.py\", line 2144, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/anyio/_backends/_asyncio.py\", line 851, in run\n",
" result = context.run(func, *args)\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/gradio/utils.py\", line 759, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"/var/folders/vr/l64rqhls46j_2hyn4pdl0m880000gn/T/ipykernel_56385/4113486017.py\", line 15, in predict_cat_dog\n",
" prediction = np.round(float(tf.sigmoid(prediction)[0]), 2)\n",
" File \"/Users/loic/Library/Python/3.9/lib/python/site-packages/tensorflow/python/framework/ops.py\", line 307, in __float__\n",
" return float(self._numpy())\n",
"TypeError: only length-1 arrays can be converted to Python scalars\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/cat1.jpeg\", \"images/cat2.jpeg\", \"images/cat3.jpeg\", \"images/cat4.jpeg\", \"images/dog1.jpeg\", \"images/dog2.jpeg\", \"images/dog3.jpeg\"], \n",
" description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
"iface.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv_new",
"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": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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