{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jarvis/.local/lib/python3.10/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": [
    "from transformers import ViTImageProcessor, ViTForImageClassification,FlaxViTForImageClassification\n",
    "from PIL import Image\n",
    "import requests\n",
    "from matplotlib import pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tiger cat', 'tabby, tabby cat', 'Egyptian cat'] [282 281 285]\n"
     ]
    }
   ],
   "source": [
    "url = 'http://images.cocodataset.org/val2017/000000039769.jpg'\n",
    "image = Image.open(requests.get(url, stream=True).raw)\n",
    "\n",
    "processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')\n",
    "model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\n",
    "\n",
    "inputs = processor(images=image, return_tensors=\"pt\")\n",
    "outputs = model(**inputs)\n",
    "logits = outputs.logits\n",
    "\n",
    "logits_np = logits.detach().cpu().numpy()\n",
    "logits_args = logits_np.argsort()[0][-3:]\n",
    "\n",
    "prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ]\n",
    "print(prediction_classes,logits_args)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tiger cat': -0.27440035,\n",
       " 'tabby, tabby cat': 0.8215165,\n",
       " 'Egyptian cat': -0.08364794}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = {}\n",
    "for i,item in enumerate(prediction_classes):\n",
    "    result[item] = logits_np[0][i]\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tiger cat', 'tabby, tabby cat', 'Egyptian cat']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model predicts one of the 1000 ImageNet classes\n",
    "\n",
    "prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ]\n",
    "\n",
    "prediction_classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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