GenAI-Workshop-Demo1.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"D_XJcxqx22uj"},"source":["## Using Gradio to show chunking.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fJUJLWQ92g6R"},"outputs":[],"source":["!pip install gradio==4.19.1\n","!pip install langchain==0.1.7\n","!pip install transformers==4.37.0\n","!pip install datasets"]},{"cell_type":"code","source":["import datasets\n","from datasets import load_dataset"],"metadata":{"id":"bMd1mKGJ_Ege","executionInfo":{"status":"ok","timestamp":1710251021072,"user_tz":420,"elapsed":2626,"user":{"displayName":"John Willis","userId":"15271974867993570949"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["hf_data = load_dataset('botchagalupe/ProfoundDeming')\n"],"metadata":{"id":"JCnj7LSw_Lmg","executionInfo":{"status":"ok","timestamp":1710251029200,"user_tz":420,"elapsed":3827,"user":{"displayName":"John Willis","userId":"15271974867993570949"}},"colab":{"base_uri":"https://localhost:8080/","height":240,"referenced_widgets":["7dcb3f66974246abb310a6e3e41eb042","6c567a9cebdc4566a1d94ead70c9abff","9c80103557824e1c81a5f50ad66c0feb","e24b4328f6cf435dbc284004e3224727","139d15c5efd64301a17feb6b0532c01d","114962e568b0466faff26e96ab17affb","a6c33fb4845a43c388e11be9b53a6d23","2db77344c3a24680905cdf65d74b93b2","a4127d61ebdc4d3f809b8338095160f8","49133aa634b34a1fb7697a3b69c91f88","89504cd472fb4654852657ac2cd75b60","0e3d61218dae45328ac014eb04ad9304","1edaf74015344672823942f06615929c","66ac45c80689441f8cd17a9578016fb6","47c2859188874f05a7de0d805dbd1bbc","095e588b01b848d293027e914ed65ad1","c3b19e31b6754d5d8071e4d10905b946","2d994eda43ce44c2a12e1de64885ac38","daa1210a30c3468596d943c677ab23a8","471701b5e5a84407910544434f503bd0","f461191d6aec4f9b8c5e195abbccb2c3","1f1ec04625f9460189d6a55a82cfd2ff","8ac59e66a8904100896ce4209b8c93de","10be8ce4b59a4e71b47d6bd47a1486ac","b211b7b96c6e4d67bddf7b8425eeab5b","bbfeeed387294dbea1c11cabf3d2c4de","51ffe28436c54399b4526b8e0fec1b1a","8e5d3b99bdf54fb99423068e896ef3f7","cb24c0c437c644eebaf936581ceec646","bc6df0022be1483986c1c00d69bc856c","f9383e61a8a54c469cdb6414e5031c21","1364bbb2921d406ba57f0485087ab0e7","a68a2db85f654185a7466278a31b3c64"]},"outputId":"7763b685-dabe-4f80-ad57-f91261ffa975"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/291 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7dcb3f66974246abb310a6e3e41eb042"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/74.4k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0e3d61218dae45328ac014eb04ad9304"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/669 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ac59e66a8904100896ce4209b8c93de"}},"metadata":{}}]},{"cell_type":"code","source":["dataset = \"\"\n","for item in hf_data ['train']:\n","    dataset += item['text'] + \" \"  # Adding a space after each text part\n","\n","print(\"Data Ingested\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xc4PqF1CA-Oq","executionInfo":{"status":"ok","timestamp":1710251038898,"user_tz":420,"elapsed":120,"user":{"displayName":"John Willis","userId":"15271974867993570949"}},"outputId":"85e0c471-659c-4c7b-e60e-b9992c904b6a"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Data Ingested\n"]}]},{"cell_type":"code","source":["### Utilities to get overlap between strings\n","\n","def get_overlap_length(left: str, right: str):\n","    good_length, overlap = 0, \"\"\n","    for i in range(min(len(left), len(right))):\n","        if left[-i:] == right[:i]:\n","            good_length = i\n","            overlap = left[-i:]\n","    return good_length, overlap\n","\n","def get_overlap_list(strings):\n","    \"\"\"\n","    Returns a list of tuples of the form (overlap_length, overlap), one tuple for each pair of strings in the input list.\n","    \"\"\"\n","    overlaps = []\n","    for i in range(len(strings) - 1):\n","        overlaps.append(get_overlap_length(strings[i], strings[i+1]))\n","    return overlaps\n","\n","def unoverlap_list(strings):\n","    \"\"\"\n","    Returns a list of tuples of the form (content, is_overlap), where is_overlap is a boolean indicating whether the content is an overlap or not.\n","    \"\"\"\n","    overlaps = get_overlap_list(strings)\n","    new_list = []\n","    for index, string in enumerate(strings):\n","        # Add the last overlap when needed\n","        if index > 0 and len(overlaps[index-1][1]) > 0:\n","            new_list.append((overlaps[index-1][1], True))\n","\n","        # prune the string with left and right overlaps\n","        left_overlap_length, right_overlap_length = 0, 0\n","        if index > 0:\n","            left_overlap_length = overlaps[index-1][0]\n","        if index < len(strings) - 1:\n","            right_overlap_length = overlaps[index][0]\n","\n","        new_list.append((string[left_overlap_length:len(string)-right_overlap_length], False))\n","    return new_list"],"metadata":{"id":"jUnvlO6b2c0d","executionInfo":{"status":"ok","timestamp":1710251042037,"user_tz":420,"elapsed":128,"user":{"displayName":"John Willis","userId":"15271974867993570949"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["import gradio as gr\n","from langchain.text_splitter import (\n","    CharacterTextSplitter,\n","    RecursiveCharacterTextSplitter,\n","    Language,\n",")\n","from transformers import AutoTokenizer\n","#from overlap import unoverlap_lis"],"metadata":{"id":"JzIGzmnJ41Sf","executionInfo":{"status":"ok","timestamp":1710251056695,"user_tz":420,"elapsed":11135,"user":{"displayName":"John Willis","userId":"15271974867993570949"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["LABEL_TEXTSPLITTER = \"🦜🔗 LangChain's CharacterTextSplitter\"\n","LABEL_RECURSIVE = \"🦜🔗 LangChain's RecursiveCharacterTextSplitter\"\n","\n","bert_tokenizer = AutoTokenizer.from_pretrained('google-bert/bert-base-uncased')\n","\n","def length_tokens(txt):\n","    return len(bert_tokenizer.tokenize(txt))\n","\n","\n","def extract_separators_from_string(separators_str):\n","    try:\n","        separators_str = separators_str.replace(\"\\\\n\", \"\\n\").replace(\"\\\\t\", \"\\t\").replace(\"\\\\\\\\\", \"\\\\\") # fix special characters\n","        separators = separators_str[1:-1].split(\", \")\n","        return [separator.replace('\"', \"\").replace(\"'\", \"\") for separator in separators]\n","    except Exception as e:\n","        raise gr.Error(f\"\"\"\n","        Did not succeed in extracting seperators from string: {separator_str} due to: {str(e)}.\n","        Please type it in the correct format: \"['separator_1', 'separator_2', ...]\"\n","        \"\"\")\n","\n","def change_split_selection(split_selection):\n","    return (\n","        gr.Textbox.update(visible=(split_selection==LABEL_RECURSIVE)),\n","        gr.Radio.update(visible=(split_selection==LABEL_RECURSIVE)),\n","    )\n","\n","def chunk(text, length, splitter_selection, separators_str, length_unit_selection, chunk_overlap):\n","    separators = extract_separators_from_string(separators_str)\n","    length_function = (length_tokens if \"token\" in length_unit_selection.lower() else len)\n","    if splitter_selection == LABEL_TEXTSPLITTER:\n","        text_splitter = CharacterTextSplitter(\n","            chunk_size=length,\n","            chunk_overlap=int(chunk_overlap),\n","            length_function=length_function,\n","            strip_whitespace=False,\n","            is_separator_regex=False,\n","            separator=\" \",\n","        )\n","    elif splitter_selection == LABEL_RECURSIVE:\n","        text_splitter = RecursiveCharacterTextSplitter(\n","            chunk_size=length,\n","            chunk_overlap=int(chunk_overlap),\n","            length_function=length_function,\n","            strip_whitespace=False,\n","            separators=separators,\n","        )\n","    splits = text_splitter.create_documents([text])\n","    text_splits = [split.page_content for split in splits]\n","    unoverlapped_text_splits = unoverlap_list(text_splits)\n","    output = [((split[0], 'Overlap') if split[1] else (split[0], f\"Chunk {str(i)}\")) for i, split in enumerate(unoverlapped_text_splits)]\n","    return output\n","\n","def change_preset_separators(choice):\n","    text_splitter = RecursiveCharacterTextSplitter()\n","    if choice == \"Default\":\n","        return [\"\\n\\n\", \"\\n\", \" \", \"\"]\n","    elif choice == \"Markdown\":\n","        return text_splitter.get_separators_for_language(Language.MARKDOWN)\n","    elif choice == \"Python\":\n","        return text_splitter.get_separators_for_language(Language.PYTHON)\n","    else:\n","        raise gr.Error(\"Choice of preset not recognized.\")\n","\n","\n","with gr.Blocks(theme=gr.themes.Soft(text_size='lg', font=[\"monospace\"], primary_hue=gr.themes.colors.green)) as demo:\n","    text = gr.Textbox(label=\"Your text 🪶\", value=dataset)\n","    with gr.Row():\n","        split_selection = gr.Dropdown(\n","            choices=[\n","                LABEL_TEXTSPLITTER,\n","                LABEL_RECURSIVE,\n","            ],\n","            value=LABEL_RECURSIVE,\n","            label=\"Method to split chunks 🍞\",\n","        )\n","        separators_selection = gr.Textbox(\n","            elem_id=\"textbox_id\",\n","            value=[\"\\n\\n\", \"\\n\", \" \", \"\"],\n","            info=\"Separators used in RecursiveCharacterTextSplitter\",\n","            show_label=False, # or set label to an empty string if you want to keep its space\n","            visible=True,\n","        )\n","        separator_preset_selection = gr.Radio(\n","            ['Default', 'Python', 'Markdown'],\n","            label=\"Choose a preset\",\n","            info=\"This will apply a specific set of separators to RecursiveCharacterTextSplitter.\",\n","            visible=True,\n","        )\n","    with gr.Row():\n","        length_unit_selection = gr.Dropdown(\n","            choices=[\n","                \"Character count\",\n","                \"Token count (BERT tokens)\",\n","            ],\n","            value=\"Character count\",\n","            label=\"Length function\",\n","            info=\"How should we measure our chunk lengths?\",\n","        )\n","        slider_count = gr.Slider(\n","            50, 500, value=200, step=1, label=\"Chunk length 📏\", info=\"In the chosen unit.\"\n","        )\n","        chunk_overlap = gr.Slider(\n","            0, 50, value=10, step=1, label=\"Overlap between chunks\", info=\"In the chosen unit.\"\n","        )\n","    out = gr.HighlightedText(\n","        label=\"Output\",\n","        show_legend=True,\n","        show_label=False,\n","        color_map={'Overlap': '#DADADA'}\n","    )\n","\n","    split_selection.change(\n","        fn=change_split_selection,\n","        inputs=split_selection,\n","        outputs=[separators_selection, separator_preset_selection],\n","    )\n","    separator_preset_selection.change(\n","        fn=change_preset_separators,\n","        inputs=separator_preset_selection,\n","        outputs=separators_selection,\n","    )\n","    gr.on(\n","        [text.change, length_unit_selection.change, separators_selection.change, split_selection.change, slider_count.change, chunk_overlap.change],\n","        chunk,\n","        [text, slider_count, split_selection, separators_selection, length_unit_selection, chunk_overlap],\n","        outputs=out\n","    )\n","    demo.load(chunk, inputs=[text, slider_count, split_selection, separators_selection, length_unit_selection, chunk_overlap], outputs=out)\n","demo.launch()"],"metadata":{"id":"Ss6OLQaT1Vqi","colab":{"base_uri":"https://localhost:8080/","height":776,"referenced_widgets":["1440e00b27f64ad59e3d65ab6a4c0553","eafb2b2c4f224bec92ba6dfa2bc19f0f","9d067b739a764c8bbe5cdcc2086acb67","e42967515eb240c8877e582afa23ab61","f9454cbe2344414faa23ce16073371f3","280b2432b6ea4dbc86a263851a19720f","ae59d3549ffa4e618c5563f9b23df656","e9cdccb45d1847ddbfeac8c1dbf3d041","e2b405f76a2e45adbd7d86a7a978fbaf","85f34c5b4bb14507b64d84a04c3a8ef5","f27c51fd89484e6cb7516a3d33d6ed5b","6497490010d840e8888c384575b25cd2","1c4fa5a6c6114a0eaee4b4f6318f8a97","3ffdc06f572b4b569f939cdecf9aa242","d42e3a6f999c477b9708ff98447c0ad6","b9848d5221024e8eb43277b20591abdb","f5f30cd45ec2436cbe0b968e25a38990","16f8df8786144c918794a9c25f250cf4","61200655197f40bd9b29d222c8a1dd37","b51418836dc74a23aad1c6d2592321b0","3bfcb281de70494d9343c5849b463a09","2af80946533e47eea68b0defde4f1c7f","235ed94004af4fee9d6462a4fc438767","83f42549fb924eefbdf002a680828c37","60a683b4b209461a958aa230319a1e68","62d7204752604973a6bf01ac49d417d7","ff425421531849a1802cad07d72b1bdd","bbcb51feacef4baab55f765fe2916c80","d9622b6aa01448409e59fa0305b241b4","1a247c68fc3545518ae94164d904639b","6496fd4a491b4b68b1c1e0de7030bfef","36f9b843ef5047d1a09ba9309c053185","39a13cbb0c1d4251bbcca20bbfc692fe","97570edbe454419daccdcc2f494de589","4c4343ac5ff3406f8d29ba29452f7a84","ea0fc1ff749f4739a57cfae91a30472c","1f67d905bd0f41f88d5aef59fe3a1b00","e733436e6361435d8759a53caf9e08fc","83fc3822a2a24feb89dd4220e16d918a","190f34dbd0ee40bcaa2d5b0005a4aa55","8549e83c9f6a4b6f9b570f7dde3f0390","b1f21bcddf4842deaaddddf954a1b336","f6c5758ef9ad41c985336ec3d2e200db","82e861f542bc44f08192a2065884806f"]},"executionInfo":{"status":"ok","timestamp":1710251064433,"user_tz":420,"elapsed":5091,"user":{"displayName":"John Willis","userId":"15271974867993570949"}},"outputId":"6e16cc95-5674-484b-bf89-111b20503135"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1440e00b27f64ad59e3d65ab6a4c0553"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6497490010d840e8888c384575b25cd2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"235ed94004af4fee9d6462a4fc438767"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"97570edbe454419daccdcc2f494de589"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n","\n","Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","Running on public URL: https://9ae9ad83d646021684.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://9ae9ad83d646021684.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"uQheRaw5YVTL"},"source":["That's all! Go ahead and open that share link in a new tab. 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