{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77414e9d91534e578d51cced47102e57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a GemmaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TAX INVOICE\n",
      "Bill No. 10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoProcessor, PaliGemmaForConditionalGeneration\n",
    "import requests\n",
    "from PIL import Image\n",
    "\n",
    "model_id = \"google/paligemma-3b-mix-224\"\n",
    "model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)\n",
    "processor = AutoProcessor.from_pretrained(model_id)\n",
    "\n",
    "prompt = \"ocr\"\n",
    "image_file = \"sample_invoice.png\"\n",
    "raw_image = Image.open(image_file)\n",
    "inputs = processor(prompt, raw_image, return_tensors=\"pt\")\n",
    "output = model.generate(**inputs, max_new_tokens=100)\n",
    "\n",
    "print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])\n",
    "# bee\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU is not available\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    print(\"GPU is available\")\n",
    "else:\n",
    "    print(\"GPU is not available\")"
   ]
  }
 ],
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