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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "8cd1e865-53d5-460b-8bae-5658e3aa3d16",
"metadata": {},
"outputs": [],
"source": [
"import panel as pn\n",
"pn.extension()\n",
"import requests\n",
"import random\n",
"import PIL\n",
"from PIL import Image\n",
"import io\n",
"from transformers import CLIPProcessor, CLIPModel\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8570053-0b83-421b-95c2-695b6c709ba1",
"metadata": {},
"outputs": [],
"source": [
"pn.extension('texteditor', template=\"bootstrap\", sizing_mode='stretch_width')\n",
"\n",
"pn.state.template.param.update(\n",
" main_max_width=\"690px\",\n",
" header_background=\"#F08080\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca65cc07-8181-4259-8770-9c780621eb78",
"metadata": {},
"outputs": [],
"source": [
"# File input widget\n",
"file_input = pn.widgets.FileInput()\n",
"\n",
"# Button widget\n",
"compute_button = pn.widgets.Button(name=\"Compute\")\n",
"\n",
"# Text input widget\n",
"text_input = pn.widgets.TextInput(name='Possible class names (e.g., cat, dog)', placeholder='cat, dog')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3691594-df8c-4d03-99e8-db4d3b2520c0",
"metadata": {},
"outputs": [],
"source": [
"def normalize_image(value, width=600):\n",
" \"\"\"\n",
" normalize image to RBG channels and to the same size\n",
" \"\"\"\n",
" if value: \n",
" b = io.BytesIO(value)\n",
" image = PIL.Image.open(b).convert(\"RGB\")\n",
" else: \n",
" url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
" image = Image.open(requests.get(url, stream=True).raw)\n",
" aspect = image.size[1] / image.size[0]\n",
" height = int(aspect * width)\n",
" return image.resize((width, height), PIL.Image.LANCZOS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b139802-c9d6-4493-acb2-5051343c1ecc",
"metadata": {},
"outputs": [],
"source": [
"def image_classification(image):\n",
" model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
" processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
" possible_categories = text_input.value.split(\",\")\n",
" if text_input.value == '':\n",
" possible_categories = ['cat', ' dog']\n",
" inputs = processor(text=possible_categories, images=image, return_tensors=\"pt\", padding=True)\n",
" \n",
" outputs = model(**inputs)\n",
" logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n",
" probs = logits_per_image.softmax(dim=1)\n",
" return probs.detach().numpy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b6f0ce5-03a5-4a14-b0b7-74c8190ce928",
"metadata": {},
"outputs": [],
"source": [
"def get_result(_):\n",
" image = normalize_image(file_input.value)\n",
"\n",
" result = image_classification(image)\n",
" \n",
" possible_categories = text_input.value.split(\",\")\n",
" if text_input.value == '':\n",
" possible_categories = ['cat', ' dog']\n",
"\n",
" progress_bars = pn.Column(*[\n",
" pn.Row(\n",
" possible_categories[i], \n",
" pn.indicators.Progress(name='', value=int(j*100), width=500))\n",
" for i, j in enumerate(result[0])\n",
" ])\n",
" return progress_bars\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fd5a63f-012a-419c-8386-22b5b8ff243f",
"metadata": {},
"outputs": [],
"source": [
"# Bind the get_image function with the button widget\n",
"interactive_result = pn.bind(get_result, compute_button)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "399189f1-4ff6-4f4b-b050-76e9a46443dd",
"metadata": {},
"outputs": [],
"source": [
"# layout\n",
"pn.Column(\n",
" \"## \\U0001F60A Upload an image file and start classifying!\",\n",
" file_input,\n",
" pn.bind(pn.panel, file_input),\n",
" text_input, \n",
" compute_button,\n",
" interactive_result\n",
").servable(title=\"Panel Image Classification Demo\")"
]
}
],
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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