adds demo script

notebooks/Named_Entity_Recognition_Demo.ipynb

@@ -2,223 +2,255 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1417f02c",
+   "execution_count": 2,
+   "id": "aa1a8952",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+    "#import libraries\n",
+    "from transformers import pipeline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "3d95e47f",
+   "execution_count": 1,
+   "id": "5493cee5",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "\u001b[?25hInstalling collected packages: xxhash, safetensors, regex, pyarrow-hotfix, pyarrow, multidict, fsspec, frozenlist, dill, yarl, responses, multiprocess, aiosignal, tokenizers, aiohttp, transformers, datasets, evaluate\n",
-      "  Attempting uninstall: fsspec\n",
-      "    Found existing installation: fsspec 2023.12.2\n",
-      "    Uninstalling fsspec-2023.12.2:\n",
-      "      Successfully uninstalled fsspec-2023.12.2\n",
-      "Successfully installed aiohttp-3.9.1 aiosignal-1.3.1 datasets-2.16.1 dill-0.3.7 evaluate-0.4.1 frozenlist-1.4.1 fsspec-2023.10.0 multidict-6.0.4 multiprocess-0.70.15 pyarrow-14.0.2 pyarrow-hotfix-0.6 regex-2023.12.25 responses-0.18.0 safetensors-0.4.1 tokenizers-0.15.0 transformers-4.36.2 xxhash-3.4.1 yarl-1.9.4\n",
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-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "586b43b1b9e2484c9b8636f9b41ab5e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.19k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6c722044e074fd58b9cf1c67fa48551",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95002c472f794e3b9b96dcfba697395e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/669k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b71e5e8241de454c9bd5f7066b3f5413",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#reference appropriate Hugging Face model\n",
+    "model_name = 'koakande/bert-finetuned-ner'\n",
+    "\n",
+    "# Load token classification pipeline modelfrom Hugging Face\n",
+    "model = pipeline(\"token-classification\", model=model_name, aggregation_strategy=\"simple\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f59488e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'koakande/bert-finetuned-ner'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e6b97a0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'entity_group': 'PER',\n",
+       "  'score': 0.99741244,\n",
+       "  'word': 'Kabeer',\n",
+       "  'start': 12,\n",
+       "  'end': 18},\n",
+       " {'entity_group': 'ORG',\n",
+       "  'score': 0.9985826,\n",
+       "  'word': 'OVO',\n",
+       "  'start': 61,\n",
+       "  'end': 64},\n",
+       " {'entity_group': 'LOC',\n",
+       "  'score': 0.99884343,\n",
+       "  'word': 'UK',\n",
+       "  'start': 72,\n",
+       "  'end': 74}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "msg = \"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK\"\n",
+    "model(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7c54c0ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Hello, I am <span style='border: 2px solid green;'>Kabeer</span>. I work as a machine learning engineer at <span style='border: 2px solid green;'>OVO</span> in the <span style='border: 2px solid green;'>UK</span>\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write a prediction method for the model\n",
+    "def predict_entities(text):\n",
+    "    # Use the loaded model to identify entities in the text\n",
+    "    entities = model(text)\n",
+    "    # Highlight identified entities in the input text\n",
+    "    highlighted_text = text\n",
+    "    for entity in entities:\n",
+    "        entity_text = text[entity['start']:entity['end']]\n",
+    "        replacement = f\"<span style='border: 2px solid green;'>{entity_text}</span>\"\n",
+    "        highlighted_text = highlighted_text.replace(entity_text, replacement)\n",
+    "    return highlighted_text\n",
+    "\n",
+    "predict_entities(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d784554",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/kabeerakande/Library/Caches/pypoetry/virtualenvs/mlplatform-NR7YTMyt-py3.11/lib/python3.11/site-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (2.17.2)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/kabeerakande/Library/Caches/pypoetry/virtualenvs/mlplatform-NR7YTMyt-py3.11/lib/python3.11/site-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/kabeerakande/Library/Caches/pypoetry/virtualenvs/mlplatform-NR7YTMyt-py3.11/lib/python3.11/site-packages (from requests->huggingface-hub>=0.19.3->gradio) (1.26.18)\n",
-      "Requirement already satisfied: mdurl~=0.1 in /Users/kabeerakande/Library/Caches/pypoetry/virtualenvs/mlplatform-NR7YTMyt-py3.11/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (0.1.2)\n",
+      "Running on local URL:  http://127.0.0.1:7860\n",
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "To create a public link, set `share=True` in `launch()`.\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "!pip install datasets evaluate transformers\n",
-    "!pip install gradio"
+    "# gradio interface\n",
+    "import gradio as gr\n",
+    "\n",
+    "title = \"Named Entity Recognizer\"\n",
+    "\n",
+    "description = \"\"\"\n",
+    "This model has been trained to identify entities in a given text. It returns the input text with the entities highlighted in green. Give it a try!\n",
+    "\"\"\"\n",
+    "\n",
+    "article = \"The model is trained using bert-finetuned-ner.\"\n",
+    "\n",
+    "iface  = gr.Interface(\n",
+    "    fn=predict_entities,\n",
+    "    inputs=gr.Textbox(lines=5, placeholder=\"Enter text...\"),\n",
+    "    outputs=gr.HTML(),\n",
+    "    title=title,\n",
+    "    description=description,\n",
+    "    article=article,\n",
+    "    examples=[[\"Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK\"], [\"This is Maryam who is a Leicester based NHS Doctor\"]],\n",
+    ")\n",
+    "\n",
+    "iface.launch()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3543ef02",
+   "id": "4f930b57",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -226,9 +258,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (Poetry Env)",
+   "display_name": "named_entity__kernel",
    "language": "python",
-   "name": "ml-platform-py3.11"
+   "name": "named_entity__kernel"
   },
   "language_info": {
    "codemirror_mode": {