Create notebook for generating headlines

apps/openchallenges/notebook/notebooks/openai-challenge-headline.ipynb

@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "34a39b07-385c-49b7-931f-3631a1aff129",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# OpenChallenges REST API Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed72ae29-13e0-4533-8e7e-f13c9b34da1a",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b876938b-ae3a-49ba-b416-b9fd59f88ae7",
+   "metadata": {},
+   "source": [
+    "This notebook generates challenge headlines for challenges fetched from OpenChallenges (OC)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e95e24d-3f3a-47d0-bb52-35e718eb7ac6",
+   "metadata": {},
+   "source": [
+    "## Requirements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc586a81-e5b4-4021-9c6b-aa8a69ef221c",
+   "metadata": {},
+   "source": [
+    "- Access to OpenChallenges REST API\n",
+    "- Access to OpenAI API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c90710c2-f053-44ae-a3c2-610eecff9073",
+   "metadata": {},
+   "source": [
+    "## List challenges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c41a64a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openchallenges_client\n",
+    "from pprint import pprint\n",
+    "from openchallenges_client.api import challenge_api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8fc3ac4c-2ceb-4bbc-bdb8-3bb8be08dfc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See configuration.py for a list of all supported configuration parameters.\n",
+    "configuration = openchallenges_client.Configuration(\n",
+    "    host = \"https://openchallenges.io/api/v1\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b2f952f5-9140-4702-8a96-3457ca4df841",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Enter a context with an instance of the API client\n",
+    "challenges = []\n",
+    "with openchallenges_client.ApiClient(configuration) as api_client:\n",
+    "    api_instance = challenge_api.ChallengeApi(api_client)\n",
+    "    \n",
+    "    query = openchallenges_client.ChallengeSearchQuery(page_number=1, page_size=1)\n",
+    "\n",
+    "    try:\n",
+    "        # Get the first page of the list of challenges\n",
+    "        page = api_instance.list_challenges(query)\n",
+    "        challenges.extend(page.challenges)\n",
+    "    except openchallenges_client.ApiException as e:\n",
+    "        print(\"Exception when calling ChallengeApi->list_challenges: %s\\n\" % e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d0c0b308-0b58-44a7-8ff6-4987dfbccb17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Challenge(id=279, slug='niddk-central-repository-data-centric-challenge', name='NIDDK Central Repository Data-Centric Challenge', headline='Enhancing NIDDK datasets for future Artificial Intelligence (AI) applications.', description='The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository (https://repository.niddk.nih.gov/home/) is conducting a Data Centric Challenge aimed at augmenting existing Repository data for future secondary research including data-driven discovery by artificial intelligence (AI) researchers. The NIDDK Central Repository (NIDDK-CR) program strives to increase the utilization and impact of the resources under its guardianship. However, lack of standardization and consistent metadata within and across studies limit the ability of secondary researchers to easily combine datasets from related studies to generate new insights using data science methods. In the fall of 2021, the NIDDK-CR began implementing approaches to augment data quality to improve AI-readiness by making research data FAIR (findable, accessible, interoperable, and reusable) via a small pilot project utilizing Natural Language Processing (NLP) to tag study variables. In 2022, the NIDD...', doi='', status=<ChallengeStatus.ACTIVE: 'active'>, difficulty=<ChallengeDifficulty.INTERMEDIATE: 'intermediate'>, platform=SimpleChallengePlatform(id=14, slug='other', name='Other'), website_url='https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge', avatar_url='', incentives=[<ChallengeIncentive.PUBLICATION: 'publication'>, <ChallengeIncentive.SPEAKING_ENGAGEMENT: 'speaking_engagement'>, <ChallengeIncentive.OTHER: 'other'>], submission_types=[<ChallengeSubmissionType.PREDICTION_FILE: 'prediction_file'>, <ChallengeSubmissionType.NOTEBOOK: 'notebook'>], input_data_types=[], start_date=datetime.date(2023, 9, 20), end_date=datetime.date(2023, 11, 3), starred_count=0, created_at=datetime.datetime(2023, 10, 18, 16, 58, 17, tzinfo=datetime.timezone.utc), updated_at=datetime.datetime(2023, 10, 18, 20, 52, 49, tzinfo=datetime.timezone.utc))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "challenge = challenges[0]\n",
+    "challenge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfaaa94a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}