08_gradio.ipynb

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   "source": [
    "# Gradio demo\n",
    "\n",
    "Remember to create a virtual environment: `python -m venv .venv` and activate it `source .venv/bin/activate`\n",
    "\n",
    "Then install the dependencies `pip install -r requirements.txt`\n",
    "\n",
    "You will also need to\n",
    "- create an account on HuggingFace: https://huggingface.co/\n",
    "- create an access key: click on your profile picture, then *Settings*, then *Access Tokens*, then *New Token*\n",
    "- save the access token in a `.env` file: HF_API_KEY=\"your access key\"\n",
    "\n",
    "To deploy this application, follow the instructions here: https://www.gradio.app/guides/sharing-your-app\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7d8c008-c2b7-4bf6-9395-549e82a1f6f9",
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   "outputs": [],
   "source": [
    "import os\n",
    "import io\n",
    "\n",
    "import base64 \n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "import gradio as gr\n",
    "from IPython.display import Image, display, HTML\n",
    "from PIL import Image\n",
    "from transformers import pipeline\n",
    "\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67c2d331-7b08-4971-b16e-5778193e520c",
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   "source": [
    "get_completion = pipeline(\"summarization\", model=\"sshleifer/distilbart-cnn-12-6\")\n",
    "\n",
    "def summarize(input):\n",
    "    output = get_completion(input)\n",
    "    return output[0]['summary_text']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a3bb09-f463-4aca-a0eb-86ae89c4f1d1",
   "metadata": {},
   "source": [
    "## Building a text summarization app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d93c78-ebaa-477f-9b7a-1e3572b0694f",
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   "outputs": [],
   "source": [
    "text = ('''The tower is 324 metres (1,063 ft) tall, about the same height\n",
    "        as an 81-storey building, and the tallest structure in Paris. \n",
    "        Its base is square, measuring 125 metres (410 ft) on each side. \n",
    "        During its construction, the Eiffel Tower surpassed the Washington \n",
    "        Monument to become the tallest man-made structure in the world,\n",
    "        a title it held for 41 years until the Chrysler Building\n",
    "        in New York City was finished in 1930. It was the first structure \n",
    "        to reach a height of 300 metres. Due to the addition of a broadcasting \n",
    "        aerial at the top of the tower in 1957, it is now taller than the \n",
    "        Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the \n",
    "        Eiffel Tower is the second tallest free-standing structure in France \n",
    "        after the Millau Viaduct.''')\n",
    "\n",
    "get_completion(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85eaf796-3927-4b9a-a80f-b156cf779a41",
   "metadata": {},
   "source": [
    "### Getting started with Gradio `gr.Interface` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e1ce0d9-8520-469d-b389-2d60bb810a83",
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   "outputs": [],
   "source": [
    "gr.close_all()\n",
    "demo = gr.Interface(fn=summarize, inputs=\"text\", outputs=\"text\")\n",
    "demo.launch(share=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95eb4cf7-ddf9-474e-98f8-6773d9a66f59",
   "metadata": {},
   "source": [
    "`demo.launch(share=True)` lets you create a public link to share with your team or friends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcf4f812-508f-406d-b25b-8cf971bfeca0",
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   "source": [
    "gr.close_all()\n",
    "demo = gr.Interface(fn=summarize, \n",
    "                    inputs=[gr.Textbox(label=\"Text to summarize\", lines=6)],\n",
    "                    outputs=[gr.Textbox(label=\"Result\", lines=3)],\n",
    "                    title=\"Text summarization with distilbart-cnn\",\n",
    "                    description=\"Summarize any text using the `shleifer/distilbart-cnn-12-6` model under the hood!\"\n",
    "                   )\n",
    "demo.launch(share=True)              "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aeac4ad-3e08-45fb-9f1f-eb481d012778",
   "metadata": {},
   "source": [
    "## Building a Named Entity Recognition app"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3ce8e31-443c-4bdb-86a2-534f9ea08cb8",
   "metadata": {},
   "source": [
    "We are using this [Inference Endpoint](https://huggingface.co/inference-endpoints) for `dslim/bert-base-NER`, a 108M parameter fine-tuned BART model on the NER task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63905ebb-9303-4673-8c51-3e8c13865025",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "get_completion = pipeline(\"ner\", model=\"dslim/bert-base-NER\")\n",
    "\n",
    "def ner(input):\n",
    "    output = get_completion(input)\n",
    "    return {\"text\": input, \"entities\": output}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c7c725-7665-470e-9951-1021df068aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"My name is Andrew, I'm building DeepLearningAI and I live in California\"\n",
    "get_completion(text)"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "#### gr.interface()\n",
    "- Notice below that we pass in a list `[]` to `inputs` and to `outputs` because the function `fn` (in this case, `ner()`, can take in more than one input and return more than one output.\n",
    "- The number of objects passed to `inputs` list should match the number of parameters that the `fn` function takes in, and the number of objects passed to the `outputs` list should match the number of objects returned by the `fn` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaeafe92-ba83-4e8a-b0e5-52436d0a5043",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr.close_all()\n",
    "demo = gr.Interface(fn=ner,\n",
    "                    inputs=[gr.Textbox(label=\"Text to find entities\", lines=2)],\n",
    "                    outputs=[gr.HighlightedText(label=\"Text with entities\")],\n",
    "                    title=\"NER with dslim/bert-base-NER\",\n",
    "                    description=\"Find entities using the `dslim/bert-base-NER` model under the hood!\",\n",
    "                    allow_flagging=\"never\",\n",
    "                    #Here we introduce a new tag, examples, easy to use examples for your application\n",
    "                    examples=[\"My name is Andrew and I live in California\", \"My name is Poli and work at HuggingFace\"])\n",
    "demo.launch(share=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc6c980-5225-45eb-bdaf-57085c488a0c",
   "metadata": {},
   "source": [
    "### Adding a helper function to merge tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0af6f83-52f9-4d29-a719-bf71fb09b3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_tokens(tokens):\n",
    "    merged_tokens = []\n",
    "    for token in tokens:\n",
    "        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):\n",
    "            # If current token continues the entity of the last one, merge them\n",
    "            last_token = merged_tokens[-1]\n",
    "            last_token['word'] += token['word'].replace('##', '')\n",
    "            last_token['end'] = token['end']\n",
    "            last_token['score'] = (last_token['score'] + token['score']) / 2\n",
    "        else:\n",
    "            # Otherwise, add the token to the list\n",
    "            merged_tokens.append(token)\n",
    "\n",
    "    return merged_tokens\n",
    "\n",
    "def ner(input):\n",
    "    output = get_completion(input)\n",
    "    merged_tokens = merge_tokens(output)\n",
    "    return {\"text\": input, \"entities\": merged_tokens}\n",
    "\n",
    "gr.close_all()\n",
    "demo = gr.Interface(fn=ner,\n",
    "                    inputs=[gr.Textbox(label=\"Text to find entities\", lines=2)],\n",
    "                    outputs=[gr.HighlightedText(label=\"Text with entities\")],\n",
    "                    title=\"NER with dslim/bert-base-NER\",\n",
    "                    description=\"Find entities using the `dslim/bert-base-NER` model under the hood!\",\n",
    "                    allow_flagging=\"never\",\n",
    "                    examples=[\"My name is Andrew, I'm building DeeplearningAI and I live in California\", \"My name is Poli, I live in Vienna and work at HuggingFace\"])\n",
    "\n",
    "demo.launch(share=True)"
   ]
  },
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   "execution_count": null,
   "id": "92babf5f-5eb3-47f4-a8e6-34aa6d116aa9",
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   "source": [
    "gr.close_all()"
   ]
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