Merge pull request #194 from udomai/w3d4_OCR

notebook for simple OCR model (uploaded from Colab)

week3/community-contributions/day4_OCR_model_exercise.ipynb

@@ -0,0 +1,150 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyPtAT7Yq5xd4vDcJEZtg69J"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "# getting the latest transformers first, since this will require a restart\n",
+        "\n",
+        "!pip install git+https://github.com/huggingface/transformers.git"
+      ],
+      "metadata": {
+        "id": "6gGKXU5RXORf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# imports\n",
+        "\n",
+        "import torch\n",
+        "from google.colab import userdata\n",
+        "from huggingface_hub import login\n",
+        "from transformers import AutoProcessor, AutoModelForImageTextToText\n",
+        "from google.colab import files"
+      ],
+      "metadata": {
+        "id": "yCRrF4aiXPPo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# logging in to HF\n",
+        "\n",
+        "hf_token = userdata.get('HF_TOKEN')\n",
+        "login(hf_token, add_to_git_credential=True)"
+      ],
+      "metadata": {
+        "id": "AAlOQuCbXcrv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_RRVc2j2Vun-"
+      },
+      "outputs": [],
+      "source": [
+        "# this will start an input prompt for uploading local files\n",
+        "\n",
+        "uploaded = files.upload()\n",
+        "print(uploaded.keys()) # this will look sth like dict_keys([\"note2.jpg\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "'''\n",
+        "ChatGPT and Gemini explain the following part roughly like so:\n",
+        "The string contained in image_path is the key of the entry in the dictionary of uploaded files (see box above).\n",
+        "The value to that key contains the image in binary format.\n",
+        "The \"with open(image_path, \"wb\") as f\" part means: Create a new file \"note2.jpg\" on the server, and write to it in binary mode (\"wb\").\n",
+        "f.write(image) writes the binary image to that new file. \"note2.jpg\" aka image_path will now contain the image.\n",
+        "'''\n",
+        "\n",
+        "image_path = \"note2.jpg\" # update this string depending on the printout in the previous cell!\n",
+        "image = uploaded[image_path]\n",
+        "with open(image_path, \"wb\") as f:\n",
+        "  f.write(image)"
+      ],
+      "metadata": {
+        "id": "V_UAuSSkXBKh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# from HF model instructions\n",
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "model = AutoModelForImageTextToText.from_pretrained(\"stepfun-ai/GOT-OCR-2.0-hf\", device_map=device)\n",
+        "processor = AutoProcessor.from_pretrained(\"stepfun-ai/GOT-OCR-2.0-hf\")"
+      ],
+      "metadata": {
+        "id": "AiFP-mQtXrpV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# also from HF documentation about this model, see https://huggingface.co/stepfun-ai/GOT-OCR-2.0-hf\n",
+        "\n",
+        "image = image_path\n",
+        "inputs = processor(image, return_tensors=\"pt\").to(device)\n",
+        "\n",
+        "ocr = model.generate(\n",
+        "    **inputs,\n",
+        "    do_sample=False,\n",
+        "    tokenizer=processor.tokenizer,\n",
+        "    stop_strings=\"<|im_end|>\",\n",
+        "    max_new_tokens=4096,\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "7Adr8HB_YNf5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prints out the recognized text. This can read my handwriting pretty well! And it works super quick on the free T4 GPU server here.\n",
+        "\n",
+        "print(processor.decode(ocr[0, inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True))"
+      ],
+      "metadata": {
+        "id": "nRsRUIIuYdJ9"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}