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{
"cells": [
{
"cell_type": "markdown",
"id": "f56cc5ad",
"metadata": {},
"source": [
"# NDIS Project - OpenAI - PBSP Scoring - Page 5 - Plan Implementation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8d844ea",
"metadata": {
"hide_input": false
},
"outputs": [],
"source": [
"import openai\n",
"import re\n",
"from ipywidgets import interact\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, clear_output, Javascript, HTML, Markdown\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mtick\n",
"import json\n",
"import spacy\n",
"from spacy import displacy\n",
"from dotenv import load_dotenv\n",
"import pandas as pd\n",
"import argilla as rg\n",
"from argilla.metrics.text_classification import f1\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline\n",
"pd.set_option('display.max_rows', 500)\n",
"pd.set_option('display.max_colwidth', 10000)\n",
"pd.set_option('display.width', 10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96b83a1d",
"metadata": {},
"outputs": [],
"source": [
"#initializations\n",
"openai.api_key = os.environ['API_KEY']\n",
"openai.api_base = os.environ['API_BASE']\n",
"openai.api_type = os.environ['API_TYPE']\n",
"openai.api_version = os.environ['API_VERSION']\n",
"deployment_name = os.environ['DEPLOYMENT_ID']\n",
"\n",
"#argilla\n",
"rg.init(\n",
" api_url=os.environ[\"ARGILLA_API_URL\"],\n",
" api_key=os.environ[\"ARGILLA_API_KEY\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dee25d82",
"metadata": {},
"outputs": [],
"source": [
"#sentence extraction\n",
"def extract_sentences(paragraph):\n",
" symbols = ['\\\\.', '!', '\\\\?', ';', ':', ',', '\\\\_', '\\n', '\\\\-']\n",
" pattern = '|'.join([f'{symbol}' for symbol in symbols])\n",
" sentences = re.split(pattern, paragraph)\n",
" sentences = [sentence.strip() for sentence in sentences if sentence.strip()]\n",
" return sentences"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02fda761",
"metadata": {},
"outputs": [],
"source": [
"def process_response(response, query):\n",
" sentences = []\n",
" topics = []\n",
" scores = []\n",
" lines = response.strip().split(\"\\n\")\n",
" topic = None\n",
" for line in lines:\n",
" if \"Implementors & Roles:\" in line:\n",
" topic = \"IMPLEMENTORS & ROLES\"\n",
" elif \"Training Strategies:\" in line:\n",
" topic = \"TRAINING STRATEGIES\"\n",
" elif \"Implementation Support:\" in line:\n",
" topic = \"IMPLEMENTATION SUPPORT\"\n",
" elif \"Communication Strategies:\" in line:\n",
" topic = \"COMMUNICATION STRATEGIES\"\n",
" elif \"Treatment Fidelity:\" in line:\n",
" topic = \"TREATMENT FIDELITY\"\n",
" elif \"None:\" in line:\n",
" topic = \"NONE\"\n",
" else:\n",
" try:\n",
" parts = line.split(\"(Confidence Score:\")\n",
" if len(parts) == 2:\n",
" phrase = parts[0].strip()\n",
" score = float(parts[1].strip().replace(\")\", \"\"))\n",
" sentences.append(phrase)\n",
" topics.append(topic)\n",
" scores.append(score)\n",
" except:\n",
" pass\n",
" result_df = pd.DataFrame({'Phrase': sentences, 'Topic': topics, 'Score': scores})\n",
" try:\n",
" result_df['Phrase'] = result_df['Phrase'].str.replace('\\d+\\.', '', regex=True)\n",
" result_df['Phrase'] = result_df['Phrase'].str.replace('^\\s', '', regex=True)\n",
" result_df['Phrase'] = result_df['Phrase'].str.strip('\"')\n",
" except:\n",
" sentences = extract_sentences(query)\n",
" topics = ['NONE'] * len(sentences)\n",
" scores = [0.9] * len(sentences)\n",
" result_df = pd.DataFrame({'Phrase': sentences, 'Topic': topics, 'Score': scores})\n",
" return result_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "714fafb4",
"metadata": {},
"outputs": [],
"source": [
"def get_prompt(query):\n",
" prompt = f\"\"\"\n",
" The paragraph below is written by a disability practitioner to outline the implementation of his positive behaviour support plan, where he is expected to describe the approaches and methodologies used to implement the plan. \n",
"\n",
" Paragraph:\n",
" {query}\n",
"\n",
" Requirement:\n",
" - Identify the phrases from the paragraph above that represent each of the following plan implementation topics: \"Implementors & Roles\", \"Training Strategies\", \"Implementation Support\", \"Communication Strategies\", \"Treatment Fidelity\".\n",
"\n",
" Guidelines:\n",
" - \"Implementors & Roles\": refers to the individuals who will implement the plan and their specific roles. Example keywords to look for: \"implement\", \"roles\", \"responsibility\", \"support staff\", \"parents\", and \"caregivers\".\n",
" - \"Training Strategies\": refers to proposed strategies to train relevant plan implementers and who will deliver the training. Example keywords to look for: \"workshops\", \"training sessions\", \"online resources\", and \"coaching\".\n",
" - \"Implementation Support\": refers to whether the proposed strategies to support the implementation of the plan in relevant settings. Example keywords to look for: \"mentoring\", \"support\", \"assistance\", and \"ongoing\".\n",
" - \"Communication Strategies\": refers to proposed strategies for plan implementers to communicate relevant information about the plan and its implementation with one another. Example keywords to look for: \"meetings\", \"reports\", \"progress\", and \"sharing\".\n",
" - \"Treatment Fidelity\": refers to the proposed strategies to ensure the fidelity of the plan implementation and set a criterion level of achievement. Example keywords to look for: \"monitoring\", \"assessment\", \"outcomes\", and \"feedback\".\n",
"\n",
" Specifications of a correct answer:\n",
" - Please provide a response that closely matches the information in the paragraph and does not deviate significantly from it.\n",
" - Provide your answer in numbered lists. \n",
" - All the phrases in your answer must be exact substrings in the original paragraph. without changing any characters.\n",
" - All the upper case and lower case characters in the phrases in your answer must match the upper case and lower case characters in the original paragraph.\n",
" - Start numbering the phrases under each implementation topic from number 1. \n",
" - Start each list of phrases with these titles: \"Implementors & Roles\", \"Training Strategies\", \"Implementation Support\", \"Communication Strategies\", \"Treatment Fidelity\".\n",
" - For each phrase that belongs to any of the above implementation topics, provide a confidence score that ranges between 0.50 and 1.00, where a score of 0.50 means you are very weakly confident that the phrase belongs to that specific implementation topic, whereas a score of 1.00 means you are very strongly confident that the phrase belongs to that specific implementation topic.\n",
" - Never include any phrase in your answer that does not exist in the paragraph above.\n",
" - If there are not any phrases that belong to one or more of the implementation topics, then do not include these strategies in your answer. \n",
" - Include a final numbered list titled \"None:\", which include all the remaining phrases from the paragraph above that do not belong to any of the implementation topics above. Provide a confidence score for each of these phrases as well.\n",
"\n",
" Example answer:\n",
"\n",
" Implementors & Roles:\n",
" 1. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" 2. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
"\n",
" Training Strategies:\n",
" 1. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" 2. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
"\n",
" Implementation Support:\n",
" 1. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" 2. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
"\n",
" Communication Strategies:\n",
" 1. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" 2. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
"\n",
" Treatment Fidelity:\n",
" 1. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" 2. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" \n",
" None:\n",
" 1. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" 2. <your phrase goes here>. (Confidence Score: <your score goes here>)\n",
" \"\"\"\n",
" return prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e23821b",
"metadata": {},
"outputs": [],
"source": [
"def get_response_chatgpt(prompt):\n",
" response=openai.ChatCompletion.create( \n",
" engine=deployment_name, \n",
" messages=[ \n",
" {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, \n",
" {\"role\": \"user\", \"content\": prompt} \n",
" ],\n",
" temperature=0\n",
" )\n",
" reply = response[\"choices\"][0][\"message\"][\"content\"]\n",
" return reply"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "983765bc",
"metadata": {},
"outputs": [],
"source": [
"def convert_df(result_df):\n",
" new_df = pd.DataFrame(columns=['text', 'prediction'])\n",
" new_df['text'] = result_df['Phrase']\n",
" new_df['prediction'] = result_df.apply(lambda row: [[row['Topic'], row['Score']]], axis=1)\n",
" return new_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc69cc81",
"metadata": {},
"outputs": [],
"source": [
"#query = \"\"\"\n",
"#As Eddie's caregiver, I understand the importance of developing a comprehensive positive behavior support plan that is tailored to his specific needs. In terms of implementation, I plan to involve myself, Eddie's parents, and his support staff as the primary implementers of the plan. To ensure that everyone is well-equipped to implement the plan, I plan to organize a series of training sessions that will be delivered by experienced behavior support professionals. These sessions will cover a range of topics, including identifying triggers for challenging behaviors, responding to these behaviors in a positive and effective manner, and tracking progress over time. Additionally, I plan to provide ongoing support and assistance to all implementers, particularly during the initial stages of implementation. To promote effective communication and collaboration, I plan to organize regular meetings where all implementers can share information about Eddie's progress, any challenges they have encountered, and strategies that have proven successful. Finally, to ensure treatment fidelity, I plan to monitor progress closely using a range of assessment tools, including behavior tracking forms and outcome measures.\n",
"#\"\"\"\n",
"#prompt = get_prompt(query)\n",
"#response = get_response_chatgpt(prompt)\n",
"#result_df = process_response(response, query)\n",
"#result_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "905eaf2a",
"metadata": {},
"outputs": [],
"source": [
"topic_color_dict = {\n",
" 'IMPLEMENTORS & ROLES': '#FFCCCC',\n",
" 'TRAINING STRATEGIES': '#CCFFFF',\n",
" 'IMPLEMENTATION SUPPORT': '#FF69B4',\n",
" 'COMMUNICATION STRATEGIES': '#FFFF00',\n",
" 'TREATMENT FIDELITY': '#CCCCFF',\n",
" 'NONE': '#ECECEC'\n",
" }\n",
"\n",
"def color(df, color):\n",
" return df.style.format({'Score': '{:,.2%}'.format}).bar(subset=['Score'], color=color)\n",
"\n",
"def annotate_query(highlights, query, topics):\n",
" ents = []\n",
" for h, t in zip(highlights, topics):\n",
" pattern = re.escape(h)\n",
" pattern = re.sub(r'\\\\(.)', r'[\\1\\\\W]*', pattern) # optional non-alphanumeric characters\n",
" for match in re.finditer(pattern, query, re.IGNORECASE):\n",
" ent_dict = {\"start\": match.start(), \"end\": match.end(), \"label\": t}\n",
" ents.append(ent_dict)\n",
" return ents\n",
"\n",
"def path_to_image_html(path):\n",
" return '<img src=\"'+ path + '\" width=\"30\" height=\"15\" />'\n",
"\n",
"passing_score = 0.7\n",
"final_passing = 0.0\n",
"def display_final_df(agg_df):\n",
" tags = []\n",
" crits = [\n",
" 'IMPLEMENTORS & ROLES',\n",
" 'TRAINING STRATEGIES',\n",
" 'IMPLEMENTATION SUPPORT',\n",
" 'COMMUNICATION STRATEGIES',\n",
" 'TREATMENT FIDELITY'\n",
" ]\n",
" orig_crits = crits\n",
" crits = [x for x in crits if x in agg_df.index.tolist()]\n",
" bools = [agg_df.loc[crit, 'Final_Score'] > final_passing for crit in crits]\n",
" paths = ['./thumbs_up.png' if x else './thumbs_down.png' for x in bools]\n",
" df = pd.DataFrame({'Plan Implementation Topic': crits, 'USED': paths})\n",
" rem_crits = [x for x in orig_crits if x not in crits]\n",
" if len(rem_crits) > 0:\n",
" df2 = pd.DataFrame({'Plan Implementation Topic': rem_crits, 'USED': ['./thumbs_down.png'] * len(rem_crits)})\n",
" df = pd.concat([df, df2])\n",
" df = df.set_index('Plan Implementation Topic')\n",
" pd.set_option('display.max_colwidth', None)\n",
" display(HTML('<div style=\"text-align: center;\">' + df.to_html(classes=[\"align-center\"], index=True, escape=False ,formatters=dict(USED=path_to_image_html)) + '</div>'))\n",
" "
]
},
{
"cell_type": "markdown",
"id": "2c6e9fe7",
"metadata": {},
"source": [
"### Please outline your implementation plan, including training, specific strategies, treatment fidelity and communication with relevant people."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76dd8cab",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"#demo with Voila\n",
"\n",
"bhvr_label = widgets.Label(value='Please type your answer:')\n",
"bhvr_text_input = widgets.Textarea(\n",
" value='',\n",
" placeholder='Type your answer',\n",
" description='',\n",
" disabled=False,\n",
" layout={'height': '300px', 'width': '90%'}\n",
")\n",
"\n",
"bhvr_nlp_btn = widgets.Button(\n",
" description='Score Answer',\n",
" disabled=False,\n",
" button_style='success', # 'success', 'info', 'warning', 'danger' or ''\n",
" tooltip='Score Answer',\n",
" icon='check',\n",
" layout={'height': '70px', 'width': '250px'}\n",
")\n",
"bhvr_agr_btn = widgets.Button(\n",
" description='Validate Data',\n",
" disabled=False,\n",
" button_style='success', # 'success', 'info', 'warning', 'danger' or ''\n",
" tooltip='Validate Data',\n",
" icon='check',\n",
" layout={'height': '70px', 'width': '250px'}\n",
")\n",
"bhvr_eval_btn = widgets.Button(\n",
" description='Evaluate Model',\n",
" disabled=False,\n",
" button_style='success', # 'success', 'info', 'warning', 'danger' or ''\n",
" tooltip='Evaluate Model',\n",
" icon='check',\n",
" layout={'height': '70px', 'width': '250px'}\n",
")\n",
"btn_box = widgets.HBox([bhvr_nlp_btn, bhvr_agr_btn, bhvr_eval_btn], \n",
" layout={'width': '100%', 'height': '160%'})\n",
"bhvr_outt = widgets.Output()\n",
"bhvr_outt.layout.height = '100%'\n",
"bhvr_outt.layout.width = '100%'\n",
"bhvr_box = widgets.VBox([bhvr_text_input, btn_box, bhvr_outt], \n",
" layout={'width': '100%', 'height': '160%'})\n",
"dataset_rg_name = 'pbsp-page5-plan-implementation-argilla-ds'\n",
"agrilla_df = None\n",
"annotated = False\n",
"def on_bhvr_button_next(b):\n",
" global agrilla_df\n",
" with bhvr_outt:\n",
" clear_output()\n",
" query = bhvr_text_input.value\n",
" prompt = get_prompt(query)\n",
" response = get_response_chatgpt(prompt)\n",
" result_df = process_response(response, query)\n",
" sub_result_df = result_df[(result_df['Score'] >= passing_score) & (result_df['Topic'] != 'NONE')]\n",
" sub_2_result_df = result_df[result_df['Topic'] == 'NONE']\n",
" highlights = []\n",
" if len(sub_result_df) > 0:\n",
" highlights = sub_result_df['Phrase'].tolist()\n",
" highlight_topics = sub_result_df['Topic'].tolist() \n",
" ents = annotate_query(highlights, query, highlight_topics)\n",
" colors = {}\n",
" for ent, ht in zip(ents, highlight_topics):\n",
" colors[ent['label']] = topic_color_dict[ht]\n",
"\n",
" ex = [{\"text\": query,\n",
" \"ents\": ents,\n",
" \"title\": None}]\n",
" title = \"Plan Implementation Topic Highlights\"\n",
" display(HTML(f'<center><h1>{title}</h1></center>'))\n",
" html = displacy.render(ex, style=\"ent\", manual=True, jupyter=True, options={'colors': colors})\n",
" display(HTML(html))\n",
" title = \"Plan Implementation Topic Classifications\"\n",
" display(HTML(f'<center><h1>{title}</h1></center>'))\n",
" for top in topic_color_dict.keys():\n",
" top_result_df = sub_result_df[sub_result_df['Topic'] == top]\n",
" if len(top_result_df) > 0:\n",
" top_result_df = top_result_df.sort_values(by='Score', ascending=False).reset_index(drop=True)\n",
" top_result_df = top_result_df.set_index('Phrase')\n",
" top_result_df = top_result_df[['Score']]\n",
" display(HTML(\n",
" f'<left><h2 style=\"text-decoration: underline; text-decoration-color:{topic_color_dict[top]};\">{top}</h2></left>'))\n",
" display(color(top_result_df, topic_color_dict[top]))\n",
"\n",
" agg_df = sub_result_df.groupby('Topic')['Score'].sum()\n",
" agg_df = agg_df.to_frame()\n",
" agg_df.index.name = 'Topic'\n",
" agg_df.columns = ['Total Score']\n",
" agg_df = agg_df.assign(\n",
" Final_Score=lambda x: x['Total Score'] / x['Total Score'].sum() * 100.00\n",
" )\n",
" agg_df = agg_df.sort_values(by='Final_Score', ascending=False)\n",
" title = \"Plan Implementation Topic Coverage\"\n",
" display(HTML(f'<center><h1>{title}</h1></center>'))\n",
" agg_df['Topic'] = agg_df.index\n",
" rem_topics= [x for x in list(topic_color_dict.keys()) if not x in agg_df.Topic.tolist()]\n",
" if len(rem_topics) > 0:\n",
" rem_agg_df = pd.DataFrame({'Topic': rem_topics, 'Final_Score': 0.0, 'Total Score': 0.0})\n",
" agg_df = pd.concat([agg_df, rem_agg_df])\n",
" labels = agg_df['Final_Score'].round(1).astype('str') + '%'\n",
" ax = agg_df.plot.bar(x='Topic', y='Final_Score', rot=0, figsize=(20, 5), align='center')\n",
" for container in ax.containers:\n",
" ax.bar_label(container, labels=labels)\n",
" ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
" ax.legend([\"Final Score (%)\"])\n",
" ax.set_xlabel('')\n",
" plt.show()\n",
" title = \"Final Scores\"\n",
" display(HTML(f'<left><h1>{title}</h1></left>'))\n",
" display_final_df(agg_df)\n",
" if len(sub_2_result_df) > 0:\n",
" sub_result_df = pd.concat([sub_result_df, sub_2_result_df]).reset_index(drop=True)\n",
" agrilla_df = sub_result_df.copy()\n",
" else:\n",
" print(query)\n",
" \n",
"def on_agr_button_next(b):\n",
" global agrilla_df, annotated\n",
" with bhvr_outt:\n",
" clear_output()\n",
" if agrilla_df is not None:\n",
" # convert the dataframe to the structure accepted by argilla\n",
" converted_df = convert_df(agrilla_df)\n",
" # convert pandas dataframe to DatasetForTextClassification\n",
" dataset_rg = rg.DatasetForTextClassification.from_pandas(converted_df)\n",
" # delete the old DatasetForTextClassification from the Argilla web app if exists\n",
" rg.delete(dataset_rg_name, workspace=\"admin\")\n",
" # load the new DatasetForTextClassification into the Argilla web app\n",
" rg.log(dataset_rg, name=dataset_rg_name, workspace=\"admin\")\n",
" # Make sure all classes are present for annotation\n",
" rg_settings = rg.TextClassificationSettings(label_schema=list(topic_color_dict.keys()))\n",
" rg.configure_dataset(name=dataset_rg_name, workspace=\"admin\", settings=rg_settings)\n",
" annotated = True\n",
" else:\n",
" display(Markdown(\"<h2 style='color:red; text-align:center;'>Please score the answer first!</h2>\"))\n",
" \n",
"def on_eval_button_next(b):\n",
" global annotated\n",
" with bhvr_outt:\n",
" clear_output()\n",
" if annotated:\n",
" display(f1(dataset_rg_name).visualize())\n",
" else:\n",
" display(Markdown(\"<h2 style='color:red; text-align:center;'>Please score the answer and validate the data first!</h2>\"))\n",
"\n",
"bhvr_nlp_btn.on_click(on_bhvr_button_next)\n",
"bhvr_agr_btn.on_click(on_agr_button_next)\n",
"bhvr_eval_btn.on_click(on_eval_button_next)\n",
"\n",
"display(bhvr_label, bhvr_box)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed551eba",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3.9 (Argilla)",
"language": "python",
"name": "argilla"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
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"width": "258.097px"
},
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}
},
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}
|