feat: add HRHUB notebook with corrected paths and data structure

.gitignore

@@ -4,4 +4,5 @@ __pycache__/
 *.pyo
 .DS_Store
 *.log
-.streamlit/
+.streamlit/
+*.csv

README.md.backup

@@ -0,0 +1,292 @@
+---
+title: HRHUB
+emoji: 💼
+colorFrom: green
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.34.0"
+app_file: app.py
+pinned: true
+---
+
+# 🏢 HRHUB - HR Matching System
+
+**Bilateral Matching Engine for Candidates & Companies**
+
+A professional HR matching system using NLP embeddings and cosine similarity to connect job candidates with relevant companies based on skills, experience, and requirements.
+
+---
+
+
+
+HRHUB solves a fundamental inefficiency in hiring: candidates and companies use different vocabularies when describing skills and requirements. Our system bridges this gap using **job postings** as a translator, enriching company profiles to speak the same "skills language" as candidates.
+
+### Key Innovation
+- **Candidates** describe: "Python, Machine Learning, Data Science"
+- **Companies** describe: "Tech company, innovation, growth"
+- **Job Postings** translate: "We need Python, AWS, TensorFlow"
+- **Result**: Accurate matching in the same embedding space ℝ³⁸⁴
+
+---
+
+## 🚀 Features
+
+- ✅ **Bilateral Matching**: Both candidates and companies get matched recommendations
+- ✅ **NLP-Powered**: Uses sentence transformers for semantic understanding
+- ✅ **Interactive Visualization**: Network graphs showing match connections
+- ✅ **Scalable**: Handles 9,544 candidates × 180,000 companies
+- ✅ **Real-time**: Fast similarity computation using cosine similarity
+- ✅ **Professional UI**: Clean Streamlit interface
+
+---
+
+## 📁 Project Structure
+
+```
+hrhub/
+├── app.py                      # Main Streamlit application
+├── config.py                   # Configuration settings
+├── requirements.txt            # Python dependencies
+├── README.md                   # This file
+├── data/
+│   ├── mock_data.py           # Demo data (MVP)
+│   ├── data_loader.py         # Real data loader (future)
+│   └── embeddings/            # Saved embeddings (future)
+├── utils/
+│   ├── matching.py            # Cosine similarity algorithms
+│   ├── visualization.py       # Network graph generation
+│   └── display.py             # UI components
+└── assets/
+    └── style.css              # Custom CSS (optional)
+```
+
+---
+
+## 🛠️ Installation & Setup
+
+### Prerequisites
+- Python 3.8+
+- pip package manager
+- Git
+
+### Local Development
+
+1. **Clone the repository**
+```bash
+git clone https://github.com/your-username/hrhub.git
+cd hrhub
+```
+
+2. **Create virtual environment** (recommended)
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. **Install dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+4. **Run the app**
+```bash
+streamlit run app.py
+```
+
+5. **Open browser**
+Navigate to `http://localhost:8501`
+
+---
+
+## 🌐 Deployment (Streamlit Cloud)
+
+### Step 1: Push to GitHub
+```bash
+git add .
+git commit -m "Initial commit"
+git push origin main
+```
+
+### Step 2: Deploy on Streamlit Cloud
+1. Go to [share.streamlit.io](https://share.streamlit.io)
+2. Sign in with GitHub
+3. Click "New app"
+4. Select your repository: `hrhub`
+5. Main file path: `app.py`
+6. Click "Deploy"
+
+**That's it!** Your app will be live at `https://your-app.streamlit.app`
+
+---
+
+## 📊 Data Pipeline
+
+### Current (MVP - Hardcoded)
+```
+mock_data.py → app.py → Display
+```
+
+### Future (Production)
+```
+CSV Files → Data Processing → Embeddings → Saved Files
+                ↓
+            app.py loads embeddings → Real-time matching
+```
+
+### Files to Generate (Next Phase)
+```python
+# After running your main code, save these:
+1. candidate_embeddings.npy      # 9,544 × 384 array
+2. company_embeddings.npy        # 180,000 × 384 array
+3. candidates_processed.pkl      # Full candidate data
+4. companies_processed.pkl       # Full company data
+```
+
+---
+
+## 🔄 Switching from Mock to Real Data
+
+### Current Code (MVP)
+```python
+# app.py
+from data.mock_data import get_candidate_data, get_company_matches
+```
+
+### After Generating Embeddings
+```python
+# app.py
+from data.data_loader import get_candidate_data, get_company_matches
+```
+
+**That's it!** No other code changes needed. The UI stays the same.
+
+---
+
+## 🎨 Configuration
+
+Edit `config.py` to customize:
+
+```python
+# Matching Settings
+DEFAULT_TOP_K = 10              # Number of matches to show
+MIN_SIMILARITY_SCORE = 0.5      # Minimum score threshold
+EMBEDDING_DIMENSION = 384       # Vector dimension
+
+# UI Settings
+NETWORK_GRAPH_HEIGHT = 600      # Graph height in pixels
+
+# Demo Mode
+DEMO_MODE = True                # Set False for production
+```
+
+---
+
+## 📈 Technical Details
+
+### Algorithm
+1. **Text Representation**: Convert candidate/company data to structured text
+2. **Embedding**: Use sentence transformers (`all-MiniLM-L6-v2`)
+3. **Similarity**: Compute cosine similarity between vectors
+4. **Ranking**: Sort by similarity score, return top K
+
+### Why Cosine Similarity?
+- ✅ **Scale-invariant**: Focuses on direction, not magnitude
+- ✅ **Profile shape matching**: Captures proportional skill distributions
+- ✅ **Fast computation**: Optimized for large-scale matching
+- ✅ **Proven in NLP**: Standard metric for semantic similarity
+
+### Performance
+- **Loading time**: < 5 seconds (with pre-computed embeddings)
+- **Matching speed**: < 1 second for 180K companies
+- **Memory usage**: ~500MB (embeddings loaded)
+
+---
+
+## 🧪 Testing
+
+### Test Mock Data
+```bash
+cd hrhub
+python data/mock_data.py
+```
+
+Expected output:
+```
+✅ Candidate: Demo Candidate #0
+✅ Top 5 matches loaded
+✅ Graph data: 6 nodes, 5 edges
+```
+
+### Test Streamlit App
+```bash
+streamlit run app.py
+```
+
+---
+
+## 🎯 Roadmap
+
+### ✅ Phase 1: MVP (Current)
+- [x] Basic matching logic
+- [x] Streamlit UI
+- [x] Network visualization
+- [x] Hardcoded demo data
+
+### 🔄 Phase 2: Production (Next)
+- [ ] Generate real embeddings
+- [ ] Load embeddings from files
+- [ ] Dynamic candidate selection
+- [ ] Search functionality
+
+### 🚀 Phase 3: Advanced (Future)
+- [ ] User authentication
+- [ ] Company login view
+- [ ] Weighted matching (different dimensions)
+- [ ] RAG-powered recommendations
+- [ ] Email notifications
+- [ ] Analytics dashboard
+
+---
+
+## 👥 Team
+
+**Master's in Business Data Science - Aalborg University**
+
+- Roger - Project Lead & Deployment
+- Eskil - [Role]
+- [Team Member 3] - [Role]
+- [Team Member 4] - [Role]
+
+---
+
+## 📝 License
+
+This project is part of an academic course at Aalborg University.
+
+---
+
+## 🤝 Contributing
+
+This is an academic project. Contributions are welcome after project submission (December 14, 2024).
+
+---
+
+## 📧 Contact
+
+For questions or feedback:
+- Create an issue on GitHub
+- Contact via Moodle course forum
+
+---
+
+## 🙏 Acknowledgments
+
+- **Sentence Transformers**: Hugging Face team
+- **Streamlit**: Amazing framework for data apps
+- **PyVis**: Interactive network visualization
+- **Course Instructors**: For guidance and support
+
+---
+
+**Last Updated**: December 2024  
+**Status**: 🟢 Active Development

data/notebooks/HRHUB_Complete_With_Postings.ipynb

@@ -0,0 +1,865 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🚀 HRHUB - Complete Bilateral Matching System\n",
+    "\n",
+    "## 🎯 System Architecture:\n",
+    "\n",
+    "```\n",
+    "Candidates (9.5K) ←→ Postings (700) ←→ Companies (180K)\n",
+    "         ↓                ↓                  ↓\n",
+    "    Skills text    Job requirements    Enriched profiles\n",
+    "         ↓                ↓                  ↓\n",
+    "    Embeddings ←―――――― SAME SPACE ℝ³⁸⁴ ―――――→\n",
+    "```\n",
+    "\n",
+    "## 🔑 Key Innovation:\n",
+    "\n",
+    "**Use postings to enrich company profiles** so they speak the same language as candidates!\n",
+    "\n",
+    "- Companies describe: \"We are in tech industry\"\n",
+    "- Postings translate: \"We need Python, AWS, React\"\n",
+    "- Result: Companies can match with candidates!\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📦 Step 1: Install & Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -q sentence-transformers plotly anthropic scikit-learn umap-learn\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "from sklearn.manifold import TSNE\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "print(\"✅ All packages ready!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📂 Step 2: Load ALL Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"📂 Loading all datasets...\\n\")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "# Load candidates\n",
+    "candidates = pd.read_csv('resume_data.csv')\n",
+    "print(f\"✅ Candidates: {len(candidates):,} rows × {len(candidates.columns)} columns\")\n",
+    "\n",
+    "# Load companies base\n",
+    "companies_base = pd.read_csv('companies/companies.csv')\n",
+    "print(f\"✅ Companies (base): {len(companies_base):,} rows\")\n",
+    "\n",
+    "# Load company enrichment data\n",
+    "company_industries = pd.read_csv('companies/company_industries.csv')\n",
+    "print(f\"✅ Company industries: {len(company_industries):,} rows\")\n",
+    "\n",
+    "company_specialties = pd.read_csv('companies/company_specialties.csv')\n",
+    "print(f\"✅ Company specialties: {len(company_specialties):,} rows\")\n",
+    "\n",
+    "employee_counts = pd.read_csv('companies/employee_counts.csv')\n",
+    "print(f\"✅ Employee counts: {len(employee_counts):,} rows\")\n",
+    "\n",
+    "# Load POSTINGS (THE BRIDGE!)\n",
+    "postings = pd.read_csv('postings.csv', on_bad_lines='skip')\n",
+    "print(f\"✅ Postings: {len(postings):,} rows × {len(postings.columns)} columns\")\n",
+    "\n",
+    "# Load job-related tables\n",
+    "try:\n",
+    "    job_skills = pd.read_csv('jobs/job_skills.csv')\n",
+    "    print(f\"✅ Job skills: {len(job_skills):,} rows\")\n",
+    "except:\n",
+    "    job_skills = None\n",
+    "    print(\"⚠️  Job skills not found (optional)\")\n",
+    "\n",
+    "try:\n",
+    "    job_industries = pd.read_csv('jobs/job_industries.csv')\n",
+    "    print(f\"✅ Job industries: {len(job_industries):,} rows\")\n",
+    "except:\n",
+    "    job_industries = None\n",
+    "    print(\"⚠️  Job industries not found (optional)\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 70)\n",
+    "print(\"✅ All datasets loaded!\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔗 Step 3: Merge Company Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🔗 Merging company data...\\n\")\n",
+    "\n",
+    "# Aggregate industries\n",
+    "company_industries_agg = company_industries.groupby('company_id')['industry_id'].apply(\n",
+    "    lambda x: ', '.join(map(str, x.tolist()))\n",
+    ").reset_index()\n",
+    "company_industries_agg.columns = ['company_id', 'industries_list']\n",
+    "print(f\"✅ Aggregated industries for {len(company_industries_agg):,} companies\")\n",
+    "\n",
+    "# Aggregate specialties\n",
+    "company_specialties_agg = company_specialties.groupby('company_id')['specialty'].apply(\n",
+    "    lambda x: ' | '.join(x.astype(str).tolist())\n",
+    ").reset_index()\n",
+    "company_specialties_agg.columns = ['company_id', 'specialties_list']\n",
+    "print(f\"✅ Aggregated specialties for {len(company_specialties_agg):,} companies\")\n",
+    "\n",
+    "# Start with base\n",
+    "companies_merged = companies_base.copy()\n",
+    "\n",
+    "# Merge industries\n",
+    "companies_merged = companies_merged.merge(\n",
+    "    company_industries_agg, \n",
+    "    on='company_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# Merge specialties\n",
+    "companies_merged = companies_merged.merge(\n",
+    "    company_specialties_agg, \n",
+    "    on='company_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# Merge employee counts\n",
+    "companies_merged = companies_merged.merge(\n",
+    "    employee_counts, \n",
+    "    on='company_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "print(f\"\\n✅ Base company merge complete: {len(companies_merged):,} companies\")\n",
+    "print(f\"📊 Columns: {companies_merged.columns.tolist()[:10]}...\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🌉 Step 4: Enrich Companies with Postings (THE BRIDGE!)\n",
+    "\n",
+    "**This is the key step!** Postings tell us what companies actually need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🌉 Enriching companies with job posting data...\\n\")\n",
+    "print(\"=\" * 70)\n",
+    "print(\"KEY INSIGHT: Postings contain the 'requirements language'\")\n",
+    "print(\"that bridges companies and candidates!\")\n",
+    "print(\"=\" * 70 + \"\\n\")\n",
+    "\n",
+    "# Clean postings\n",
+    "postings = postings.fillna('')\n",
+    "\n",
+    "# Aggregate postings per company\n",
+    "postings_agg = postings.groupby('company_id').agg({\n",
+    "    'title': lambda x: ' | '.join(x.astype(str).tolist()[:10]),  # Top 10 job titles\n",
+    "    'description': lambda x: ' '.join(x.astype(str).tolist()[:5]),  # Top 5 descriptions (truncated)\n",
+    "    'skills_desc': lambda x: ' | '.join(x.dropna().astype(str).tolist()),  # All skills\n",
+    "    'formatted_experience_level': lambda x: ' | '.join(x.dropna().unique().astype(str)),\n",
+    "    'formatted_work_type': lambda x: ' | '.join(x.dropna().unique().astype(str))\n",
+    "}).reset_index()\n",
+    "\n",
+    "postings_agg.columns = [\n",
+    "    'company_id', \n",
+    "    'posted_job_titles', \n",
+    "    'posted_descriptions',\n",
+    "    'required_skills',\n",
+    "    'experience_levels',\n",
+    "    'work_types'\n",
+    "]\n",
+    "\n",
+    "print(f\"✅ Aggregated postings for {len(postings_agg):,} companies\")\n",
+    "print(f\"\\n💡 These {len(postings_agg):,} companies have explicit requirements!\\n\")\n",
+    "\n",
+    "# Merge postings into companies\n",
+    "companies_full = companies_merged.merge(\n",
+    "    postings_agg,\n",
+    "    on='company_id',\n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# Fill NaN\n",
+    "companies_full = companies_full.fillna('')\n",
+    "\n",
+    "print(f\"✅ ENRICHED COMPANIES CREATED!\")\n",
+    "print(f\"📊 Final: {len(companies_full):,} companies × {len(companies_full.columns)} columns\")\n",
+    "print(f\"\\n📋 New columns from postings:\")\n",
+    "print(f\"   - posted_job_titles\")\n",
+    "print(f\"   - posted_descriptions\")\n",
+    "print(f\"   - required_skills ← KEY FOR MATCHING!\")\n",
+    "print(f\"   - experience_levels\")\n",
+    "print(f\"   - work_types\\n\")\n",
+    "\n",
+    "# Show sample\n",
+    "print(\"👀 Sample enriched company:\")\n",
+    "sample_with_postings = companies_full[companies_full['required_skills'] != ''].iloc[0]\n",
+    "print(f\"\\nCompany: {sample_with_postings.get('name', 'N/A')}\")\n",
+    "print(f\"Industries: {str(sample_with_postings.get('industries_list', ''))[:100]}...\")\n",
+    "print(f\"Required Skills: {str(sample_with_postings.get('required_skills', ''))[:100]}...\")\n",
+    "print(f\"Job Titles Posted: {str(sample_with_postings.get('posted_job_titles', ''))[:100]}...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📂 Step 5: Load & Clean Candidates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean candidates\n",
+    "candidates = candidates.fillna('')\n",
+    "\n",
+    "print(f\"✅ Candidates cleaned: {len(candidates):,} rows\")\n",
+    "print(f\"📋 Columns: {candidates.columns.tolist()[:10]}...\")\n",
+    "candidates.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📝 Step 6: Create Aligned Text Representations\n",
+    "\n",
+    "**CRITICAL:** Both entities must use the same vocabulary!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"📝 Creating ALIGNED text representations...\\n\")\n",
+    "print(\"=\" * 70)\n",
+    "print(\"ALIGNMENT STRATEGY:\")\n",
+    "print(\"• Candidates: Describe skills, experience, education\")\n",
+    "print(\"• Companies: Describe what they NEED (from postings!)\")\n",
+    "print(\"• Result: Both use 'skills language' → same vector space!\")\n",
+    "print(\"=\" * 70 + \"\\n\")\n",
+    "\n",
+    "# ========================================================================\n",
+    "# CANDIDATE TEXT - Professional offering\n",
+    "# ========================================================================\n",
+    "def make_candidate_text(row):\n",
+    "    \"\"\"\n",
+    "    Candidate text focuses on:\n",
+    "    - What skills I have\n",
+    "    - What experience I bring\n",
+    "    - What value I offer\n",
+    "    \"\"\"\n",
+    "    parts = []\n",
+    "    \n",
+    "    # Professional identity\n",
+    "    if row.get('career_objective'):\n",
+    "        parts.append(f\"Professional seeking: {row['career_objective']}\")\n",
+    "    \n",
+    "    if row.get('job_position_name'):\n",
+    "        parts.append(f\"Target role: {row['job_position_name']}\")\n",
+    "    \n",
+    "    # SKILLS (most important for matching!)\n",
+    "    all_skills = []\n",
+    "    if row.get('skills'): \n",
+    "        all_skills.append(row['skills'])\n",
+    "    if row.get('related_skills_in_job'): \n",
+    "        all_skills.append(row['related_skills_in_job'])\n",
+    "    if row.get('certification_skills'): \n",
+    "        all_skills.append(row['certification_skills'])\n",
+    "    if row.get('skills_required'):  # Skills they're looking for in jobs\n",
+    "        all_skills.append(row['skills_required'])\n",
+    "    \n",
+    "    if all_skills:\n",
+    "        parts.append(f\"Skills and expertise: {' | '.join(all_skills)}\")\n",
+    "    \n",
+    "    # EXPERIENCE\n",
+    "    if row.get('positions'):\n",
+    "        parts.append(f\"Experience in roles: {row['positions']}\")\n",
+    "    \n",
+    "    if row.get('professional_company_names'):\n",
+    "        parts.append(f\"Companies worked at: {row['professional_company_names']}\")\n",
+    "    \n",
+    "    if row.get('responsibilities'):\n",
+    "        resp = str(row['responsibilities'])[:250]\n",
+    "        parts.append(f\"Responsibilities: {resp}\")\n",
+    "    \n",
+    "    # EDUCATION\n",
+    "    edu_parts = []\n",
+    "    if row.get('degree_names'): \n",
+    "        edu_parts.append(row['degree_names'])\n",
+    "    if row.get('major_field_of_studies'): \n",
+    "        edu_parts.append(f\"in {row['major_field_of_studies']}\")\n",
+    "    if row.get('educational_institution_name'): \n",
+    "        edu_parts.append(f\"from {row['educational_institution_name']}\")\n",
+    "    \n",
+    "    if edu_parts:\n",
+    "        parts.append(f\"Education: {' '.join(edu_parts)}\")\n",
+    "    \n",
+    "    # ADDITIONAL\n",
+    "    if row.get('languages'):\n",
+    "        parts.append(f\"Languages: {row['languages']}\")\n",
+    "    \n",
+    "    if row.get('certification_providers'):\n",
+    "        parts.append(f\"Certifications from: {row['certification_providers']}\")\n",
+    "    \n",
+    "    if row.get('extra_curricular_activity_types'):\n",
+    "        parts.append(f\"Activities: {row['extra_curricular_activity_types']}\")\n",
+    "    \n",
+    "    return ' || '.join(parts) if parts else \"Professional profile\"\n",
+    "\n",
+    "\n",
+    "# ========================================================================\n",
+    "# COMPANY TEXT - Job requirements (enriched with postings!)\n",
+    "# ========================================================================\n",
+    "def make_company_text(row):\n",
+    "    \"\"\"\n",
+    "    Company text focuses on:\n",
+    "    - What skills we need (from postings!)\n",
+    "    - What roles we're hiring for\n",
+    "    - What our company does\n",
+    "    \"\"\"\n",
+    "    parts = []\n",
+    "    \n",
+    "    # Company identity\n",
+    "    if row.get('name'):\n",
+    "        parts.append(f\"Company: {row['name']}\")\n",
+    "    \n",
+    "    # REQUIRED SKILLS (from postings - KEY!)\n",
+    "    if row.get('required_skills'):\n",
+    "        parts.append(f\"Looking for skills: {row['required_skills']}\")\n",
+    "    \n",
+    "    # JOB TITLES (from postings)\n",
+    "    if row.get('posted_job_titles'):\n",
+    "        parts.append(f\"Hiring for roles: {row['posted_job_titles']}\")\n",
+    "    \n",
+    "    # EXPERIENCE LEVELS (from postings)\n",
+    "    if row.get('experience_levels'):\n",
+    "        parts.append(f\"Experience levels: {row['experience_levels']}\")\n",
+    "    \n",
+    "    # Industries & specialties\n",
+    "    if row.get('industries_list'):\n",
+    "        parts.append(f\"Industries: {row['industries_list']}\")\n",
+    "    \n",
+    "    if row.get('specialties_list'):\n",
+    "        parts.append(f\"Specialties: {row['specialties_list']}\")\n",
+    "    \n",
+    "    # Company description\n",
+    "    if row.get('description'):\n",
+    "        desc = str(row['description'])[:300]\n",
+    "        parts.append(f\"About: {desc}\")\n",
+    "    \n",
+    "    # Posted descriptions (gives context)\n",
+    "    if row.get('posted_descriptions'):\n",
+    "        posted_desc = str(row['posted_descriptions'])[:200]\n",
+    "        parts.append(f\"Job descriptions: {posted_desc}\")\n",
+    "    \n",
+    "    # Company size\n",
+    "    if row.get('employee_count'):\n",
+    "        parts.append(f\"Company size: {row['employee_count']} employees\")\n",
+    "    \n",
+    "    # Location\n",
+    "    loc = []\n",
+    "    if row.get('city'): loc.append(row['city'])\n",
+    "    if row.get('state'): loc.append(row['state'])\n",
+    "    if row.get('country'): loc.append(row['country'])\n",
+    "    if loc:\n",
+    "        parts.append(f\"Location: {', '.join(loc)}\")\n",
+    "    \n",
+    "    # Work types\n",
+    "    if row.get('work_types'):\n",
+    "        parts.append(f\"Work arrangement: {row['work_types']}\")\n",
+    "    \n",
+    "    return ' || '.join(parts) if parts else \"Company profile\"\n",
+    "\n",
+    "\n",
+    "# ========================================================================\n",
+    "# APPLY TO DATAFRAMES\n",
+    "# ========================================================================\n",
+    "print(\"🔄 Generating candidate texts...\")\n",
+    "candidates['text'] = candidates.apply(make_candidate_text, axis=1)\n",
+    "\n",
+    "print(\"🔄 Generating company texts...\")\n",
+    "companies_full['text'] = companies_full.apply(make_company_text, axis=1)\n",
+    "\n",
+    "print(\"\\n✅ ALIGNED texts created!\\n\")\n",
+    "\n",
+    "# Compare vocabularies\n",
+    "print(\"=\" * 70)\n",
+    "print(\"CANDIDATE SAMPLE:\")\n",
+    "print(candidates['text'].iloc[0][:500])\n",
+    "print(\"\\n\" + \"=\" * 70)\n",
+    "print(\"COMPANY SAMPLE (with postings data):\")\n",
+    "# Find company with postings\n",
+    "company_with_postings = companies_full[companies_full['required_skills'] != ''].iloc[0]\n",
+    "print(company_with_postings['text'][:500])\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "print(\"\\n💡 Notice: Both now use SKILLS LANGUAGE!\")\n",
+    "print(\"   Candidate: 'Skills and expertise: Python, Java'\")\n",
+    "print(\"   Company: 'Looking for skills: Python, AWS'\")\n",
+    "print(\"   → They can now be compared in the same space!\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧠 Step 7: Generate Embeddings (ℝ³⁸⁴)\n",
+    "\n",
+    "Transform aligned text → vectors in same mathematical space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🧠 Loading embedding model...\\n\")\n",
+    "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
+    "\n",
+    "embedding_dim = model.get_sentence_embedding_dimension()\n",
+    "print(f\"✅ Model loaded! Embedding dimension: ℝ^{embedding_dim}\\n\")\n",
+    "\n",
+    "print(\"🔄 Generating candidate vectors...\")\n",
+    "print(f\"   ({len(candidates):,} candidates × ~2-3 minutes)\\n\")\n",
+    "cand_vectors = model.encode(\n",
+    "    candidates['text'].tolist(), \n",
+    "    show_progress_bar=True,\n",
+    "    batch_size=32\n",
+    ")\n",
+    "\n",
+    "print(\"\\n🔄 Generating company vectors...\")\n",
+    "print(f\"   ({len(companies_full):,} companies × ~15-20 minutes)\\n\")\n",
+    "comp_vectors = model.encode(\n",
+    "    companies_full['text'].tolist(), \n",
+    "    show_progress_bar=True,\n",
+    "    batch_size=64\n",
+    ")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 70)\n",
+    "print(\"✅ VECTORS CREATED IN SAME SPACE!\")\n",
+    "print(\"=\" * 70)\n",
+    "print(f\"📊 Candidate vectors: {cand_vectors.shape}\")\n",
+    "print(f\"📊 Company vectors: {comp_vectors.shape}\")\n",
+    "print(f\"\\n🎯 Both live in ℝ^{embedding_dim}!\")\n",
+    "print(f\"🎯 Now companies with 'Python' requirements will be\")\n",
+    "print(f\"   CLOSE to candidates with 'Python' skills!\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎯 Step 8: Matching Engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cosine_similarity(a, b):\n",
+    "    \"\"\"Calculate cosine similarity between two vectors.\"\"\"\n",
+    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n",
+    "\n",
+    "def find_top_matches(candidate_idx, top_k=10):\n",
+    "    \"\"\"\n",
+    "    Find top K company matches for a candidate.\n",
+    "    \n",
+    "    Returns: List of (company_idx, similarity_score)\n",
+    "    \"\"\"\n",
+    "    cand_vec = cand_vectors[candidate_idx]\n",
+    "    \n",
+    "    scores = []\n",
+    "    for i, comp_vec in enumerate(comp_vectors):\n",
+    "        score = cosine_similarity(cand_vec, comp_vec)\n",
+    "        scores.append((i, score))\n",
+    "    \n",
+    "    scores.sort(key=lambda x: x[1], reverse=True)\n",
+    "    return scores[:top_k]\n",
+    "\n",
+    "print(\"✅ Matching engine ready!\")\n",
+    "print(f\"📊 Can match {len(candidates):,} candidates with {len(companies_full):,} companies\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔍 Step 9: Test Matching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🔍 Finding top 10 matches for Candidate #0...\\n\")\n",
+    "\n",
+    "matches = find_top_matches(0, top_k=10)\n",
+    "\n",
+    "print(\"🎯 Top 10 Company Matches:\\n\")\n",
+    "print(\"=\" * 90)\n",
+    "print(f\"{'Rank':<6} {'Score':<8} {'Company':<35} {'Skills Needed':<40}\")\n",
+    "print(\"=\" * 90)\n",
+    "\n",
+    "for rank, (comp_idx, score) in enumerate(matches, 1):\n",
+    "    company = companies_full.iloc[comp_idx]\n",
+    "    name = company.get('name', 'N/A')[:33]\n",
+    "    skills = company.get('required_skills', 'N/A')[:38]\n",
+    "    print(f\"{rank:<6} {score:.4f}   {name:<35} {skills}\")\n",
+    "\n",
+    "print(\"=\" * 90)\n",
+    "\n",
+    "print(\"\\n💡 If scores are good (>0.5), the alignment worked!\")\n",
+    "print(\"   High scores = Company needs match candidate skills\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Step 10: Visualize Vector Space\n",
+    "\n",
+    "See where candidates and companies live in ℝ³⁸⁴ (projected to ℝ²)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🎨 VECTOR SPACE VISUALIZATION\\n\")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "# Sample for visualization\n",
+    "n_cand_viz = min(500, len(candidates))\n",
+    "n_comp_viz = min(2000, len(companies_full))\n",
+    "\n",
+    "print(f\"📊 Visualizing:\")\n",
+    "print(f\"   • {n_cand_viz} candidates\")\n",
+    "print(f\"   • {n_comp_viz} companies\")\n",
+    "print(f\"   • From ℝ^{embedding_dim} → ℝ² (t-SNE projection)\\n\")\n",
+    "\n",
+    "# Sample vectors\n",
+    "cand_sample = cand_vectors[:n_cand_viz]\n",
+    "comp_sample = comp_vectors[:n_comp_viz]\n",
+    "\n",
+    "# Combine\n",
+    "all_vectors = np.vstack([cand_sample, comp_sample])\n",
+    "\n",
+    "print(\"🔄 Running t-SNE (2-3 minutes)...\")\n",
+    "tsne = TSNE(\n",
+    "    n_components=2,\n",
+    "    perplexity=30,\n",
+    "    random_state=42,\n",
+    "    n_iter=1000,\n",
+    "    verbose=1\n",
+    ")\n",
+    "\n",
+    "vectors_2d = tsne.fit_transform(all_vectors)\n",
+    "\n",
+    "# Split\n",
+    "cand_2d = vectors_2d[:n_cand_viz]\n",
+    "comp_2d = vectors_2d[n_cand_viz:]\n",
+    "\n",
+    "print(\"\\n✅ t-SNE complete!\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create plot\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "# Companies (red)\n",
+    "fig.add_trace(go.Scatter(\n",
+    "    x=comp_2d[:, 0],\n",
+    "    y=comp_2d[:, 1],\n",
+    "    mode='markers',\n",
+    "    name='Companies',\n",
+    "    marker=dict(\n",
+    "        size=6,\n",
+    "        color='#ff6b6b',\n",
+    "        opacity=0.6\n",
+    "    ),\n",
+    "    text=[f\"Company {i}: {companies_full.iloc[i].get('name', 'N/A')[:30]}\" \n",
+    "          for i in range(n_comp_viz)],\n",
+    "    hovertemplate='<b>%{text}</b><extra></extra>'\n",
+    "))\n",
+    "\n",
+    "# Candidates (green)\n",
+    "fig.add_trace(go.Scatter(\n",
+    "    x=cand_2d[:, 0],\n",
+    "    y=cand_2d[:, 1],\n",
+    "    mode='markers',\n",
+    "    name='Candidates',\n",
+    "    marker=dict(\n",
+    "        size=10,\n",
+    "        color='#00ff00',\n",
+    "        opacity=0.8,\n",
+    "        line=dict(width=1, color='white')\n",
+    "    ),\n",
+    "    text=[f\"Candidate {i}\" for i in range(n_cand_viz)],\n",
+    "    hovertemplate='<b>%{text}</b><extra></extra>'\n",
+    "))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title='Vector Space: Candidates & Companies (with Postings Enrichment)',\n",
+    "    xaxis_title='Dimension 1',\n",
+    "    yaxis_title='Dimension 2',\n",
+    "    width=1200,\n",
+    "    height=800,\n",
+    "    plot_bgcolor='#1a1a1a',\n",
+    "    paper_bgcolor='#0d0d0d',\n",
+    "    font=dict(color='white')\n",
+    ")\n",
+    "\n",
+    "fig.show()\n",
+    "\n",
+    "print(\"✅ Visualization complete!\\n\")\n",
+    "print(\"💡 KEY OBSERVATIONS:\")\n",
+    "print(\"   • Green = Candidates | Red = Companies\")\n",
+    "print(\"   • If they OVERLAP → Good! Alignment worked!\")\n",
+    "print(\"   • If still separated → Need more postings data\")\n",
+    "print(\"   • Clusters = Similar skill profiles grouped\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔍 Step 11: Highlight Specific Candidate + Matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_candidate = 0\n",
+    "\n",
+    "print(f\"🔍 Analyzing Candidate #{target_candidate}...\\n\")\n",
+    "\n",
+    "matches = find_top_matches(target_candidate, top_k=10)\n",
+    "match_indices = [comp_idx for comp_idx, score in matches if comp_idx < n_comp_viz]\n",
+    "\n",
+    "# Create highlighted plot\n",
+    "fig2 = go.Figure()\n",
+    "\n",
+    "# All companies (background)\n",
+    "fig2.add_trace(go.Scatter(\n",
+    "    x=comp_2d[:, 0],\n",
+    "    y=comp_2d[:, 1],\n",
+    "    mode='markers',\n",
+    "    name='All Companies',\n",
+    "    marker=dict(size=4, color='#ff6b6b', opacity=0.3),\n",
+    "    showlegend=True\n",
+    "))\n",
+    "\n",
+    "# Top matches (highlighted)\n",
+    "if match_indices:\n",
+    "    match_positions = comp_2d[match_indices]\n",
+    "    fig2.add_trace(go.Scatter(\n",
+    "        x=match_positions[:, 0],\n",
+    "        y=match_positions[:, 1],\n",
+    "        mode='markers',\n",
+    "        name='Top Matches',\n",
+    "        marker=dict(\n",
+    "            size=15,\n",
+    "            color='#ff0000',\n",
+    "            line=dict(width=2, color='white')\n",
+    "        ),\n",
+    "        text=[f\"Match #{i+1}: {companies_full.iloc[match_indices[i]].get('name', 'N/A')[:30]}<br>Score: {matches[i][1]:.3f}\" \n",
+    "              for i in range(len(match_indices))],\n",
+    "        hovertemplate='<b>%{text}</b><extra></extra>'\n",
+    "    ))\n",
+    "\n",
+    "# Target candidate\n",
+    "fig2.add_trace(go.Scatter(\n",
+    "    x=[cand_2d[target_candidate, 0]],\n",
+    "    y=[cand_2d[target_candidate, 1]],\n",
+    "    mode='markers',\n",
+    "    name=f'Candidate #{target_candidate}',\n",
+    "    marker=dict(\n",
+    "        size=25,\n",
+    "        color='#00ff00',\n",
+    "        symbol='star',\n",
+    "        line=dict(width=3, color='white')\n",
+    "    )\n",
+    "))\n",
+    "\n",
+    "# Connection lines\n",
+    "for i, match_idx in enumerate(match_indices[:5]):\n",
+    "    fig2.add_trace(go.Scatter(\n",
+    "        x=[cand_2d[target_candidate, 0], comp_2d[match_idx, 0]],\n",
+    "        y=[cand_2d[target_candidate, 1], comp_2d[match_idx, 1]],\n",
+    "        mode='lines',\n",
+    "        line=dict(color='yellow', width=1, dash='dot'),\n",
+    "        opacity=0.5,\n",
+    "        showlegend=False\n",
+    "    ))\n",
+    "\n",
+    "fig2.update_layout(\n",
+    "    title=f'Candidate #{target_candidate} and Top Matches',\n",
+    "    xaxis_title='Dimension 1',\n",
+    "    yaxis_title='Dimension 2',\n",
+    "    width=1200,\n",
+    "    height=800,\n",
+    "    plot_bgcolor='#1a1a1a',\n",
+    "    paper_bgcolor='#0d0d0d',\n",
+    "    font=dict(color='white')\n",
+    ")\n",
+    "\n",
+    "fig2.show()\n",
+    "\n",
+    "print(\"✅ Highlighted visualization created!\")\n",
+    "print(f\"   ⭐ Green star = Candidate #{target_candidate}\")\n",
+    "print(f\"   🔴 Red dots = Top matches\")\n",
+    "print(f\"   💛 Yellow lines = Connections in vector space\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💾 Step 12: Export Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate matches for sample\n",
+    "results = []\n",
+    "export_sample = min(500, len(candidates))\n",
+    "\n",
+    "print(f\"💾 Generating matches for {export_sample} candidates...\\n\")\n",
+    "\n",
+    "for i in range(export_sample):\n",
+    "    if i % 50 == 0:\n",
+    "        print(f\"   Progress: {i}/{export_sample}\")\n",
+    "    \n",
+    "    matches = find_top_matches(i, top_k=10)\n",
+    "    \n",
+    "    for rank, (comp_idx, score) in enumerate(matches, 1):\n",
+    "        company = companies_full.iloc[comp_idx]\n",
+    "        results.append({\n",
+    "            'candidate_id': i,\n",
+    "            'company_id': company.get('company_id'),\n",
+    "            'company_name': company.get('name', 'N/A'),\n",
+    "            'rank': rank,\n",
+    "            'similarity_score': float(score),\n",
+    "            'required_skills': company.get('required_skills', 'N/A')[:100],\n",
+    "            'posted_jobs': company.get('posted_job_titles', 'N/A')[:100]\n",
+    "        })\n",
+    "\n",
+    "results_df = pd.DataFrame(results)\n",
+    "results_df.to_csv('hrhub_matches_with_postings.csv', index=False)\n",
+    "\n",
+    "print(f\"\\n✅ Exported {len(results_df):,} matches!\")\n",
+    "print(f\"📄 File: hrhub_matches_with_postings.csv\\n\")\n",
+    "results_df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎉 COMPLETE!\n",
+    "\n",
+    "### ✅ What you have:\n",
+    "\n",
+    "1. **Enriched companies** with job posting data (requirements, skills needed)\n",
+    "2. **Aligned text representations** (both use \"skills language\")\n",
+    "3. **Vectors in same space** ℝ³⁸⁴\n",
+    "4. **Cosine similarity matching**\n",
+    "5. **Vector space visualization**\n",
+    "6. **Exported results**\n",
+    "\n",
+    "### 🚀 Next steps:\n",
+    "\n",
+    "1. **Train LLM on patterns:** \"Company in industry X historically needs skills Y\"\n",
+    "2. **Predict for companies without postings:** Use learned patterns\n",
+    "3. **Add weights:** Let users tune dimension importance\n",
+    "4. **Build UI:** Interactive matching interface\n",
+    "5. **LLM explanations:** Why these matches make sense\n",
+    "\n",
+    "### 💡 Key insight achieved:\n",
+    "\n",
+    "**Postings bridge the gap!** They translate \"what companies are\" into \"what companies need\" - the same language candidates speak!\n",
+    "\n",
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/notebooks/HRHUB_Full_180K.ipynb

@@ -0,0 +1,471 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🚀 HRHUB - Bilateral Matching System\n",
+    "\n",
+    "## 🎯 Mathematical Framework:\n",
+    "\n",
+    "```\n",
+    "Candidate ∈ ℝⁿ (multidimensional vector)\n",
+    "Company ∈ ℝⁿ   (multidimensional vector)\n",
+    "\n",
+    "Both live in the SAME vector space!\n",
+    "\n",
+    "Match Score = cosine_similarity(v_candidate, v_company)\n",
+    "```\n",
+    "\n",
+    "## 📊 Dataset:\n",
+    "- **9,544 candidates** (35 dimensions)\n",
+    "- **180,000 companies** (multiple dimensions from merged data)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📦 Step 1: Install & Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -q sentence-transformers plotly anthropic\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "import plotly.express as px\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "print(\"✅ Ready!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📂 Step 2: Load & Merge Company Data\n",
+    "\n",
+    "Building rich 180K company entities by merging multiple tables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"📂 Loading company datasets...\\n\")\n",
+    "\n",
+    "# Load base companies table\n",
+    "companies_base = pd.read_csv('companies/companies.csv')\n",
+    "print(f\"✅ Base companies: {len(companies_base):,} rows\")\n",
+    "\n",
+    "# Load additional company dimensions\n",
+    "company_industries = pd.read_csv('companies/company_industries.csv')\n",
+    "print(f\"✅ Company industries: {len(company_industries):,} rows\")\n",
+    "\n",
+    "company_specialties = pd.read_csv('companies/company_specialties.csv')\n",
+    "print(f\"✅ Company specialties: {len(company_specialties):,} rows\")\n",
+    "\n",
+    "employee_counts = pd.read_csv('companies/employee_counts.csv')\n",
+    "print(f\"✅ Employee counts: {len(employee_counts):,} rows\")\n",
+    "\n",
+    "# Load mappings (for reference)\n",
+    "industries_map = pd.read_csv('mappings/industries.csv')\n",
+    "skills_map = pd.read_csv('mappings/skills.csv')\n",
+    "print(f\"✅ Mappings loaded\")\n",
+    "\n",
+    "print(f\"\\n📊 Base company columns: {companies_base.columns.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔗 Step 3: Merge Company Data (Create Rich Entities)\n",
+    "\n",
+    "Aggregate multiple dimensions into single company profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🔗 Merging company data...\\n\")\n",
+    "\n",
+    "# Aggregate industries per company (many-to-many)\n",
+    "company_industries_agg = company_industries.groupby('company_id')['industry_id'].apply(\n",
+    "    lambda x: ', '.join(map(str, x.tolist()))\n",
+    ").reset_index()\n",
+    "company_industries_agg.columns = ['company_id', 'industries_list']\n",
+    "\n",
+    "print(f\"✅ Aggregated industries for {len(company_industries_agg):,} companies\")\n",
+    "\n",
+    "# Aggregate specialties per company\n",
+    "company_specialties_agg = company_specialties.groupby('company_id')['specialty'].apply(\n",
+    "    lambda x: ' | '.join(x.tolist())\n",
+    ").reset_index()\n",
+    "company_specialties_agg.columns = ['company_id', 'specialties_list']\n",
+    "\n",
+    "print(f\"✅ Aggregated specialties for {len(company_specialties_agg):,} companies\")\n",
+    "\n",
+    "# Merge everything into companies_base\n",
+    "companies_full = companies_base.copy()\n",
+    "\n",
+    "# Merge industries\n",
+    "companies_full = companies_full.merge(\n",
+    "    company_industries_agg, \n",
+    "    on='company_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# Merge specialties\n",
+    "companies_full = companies_full.merge(\n",
+    "    company_specialties_agg, \n",
+    "    on='company_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# Merge employee counts\n",
+    "companies_full = companies_full.merge(\n",
+    "    employee_counts, \n",
+    "    on='company_id', \n",
+    "    how='left'\n",
+    ")\n",
+    "\n",
+    "# Fill NaN\n",
+    "companies_full = companies_full.fillna('')\n",
+    "\n",
+    "print(f\"\\n✅ MERGED DATASET CREATED!\")\n",
+    "print(f\"📊 Final companies: {len(companies_full):,} rows × {len(companies_full.columns)} columns\")\n",
+    "print(f\"\\n📋 Columns: {companies_full.columns.tolist()}\")\n",
+    "\n",
+    "# Show sample\n",
+    "print(f\"\\n👀 Sample company:\")\n",
+    "companies_full.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📂 Step 4: Load Candidates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load candidates\n",
+    "candidates = pd.read_csv('resume_data.csv')\n",
+    "candidates = candidates.fillna('')\n",
+    "\n",
+    "print(f\"✅ Loaded {len(candidates):,} candidates × {len(candidates.columns)} columns\")\n",
+    "print(f\"\\n📋 Candidate columns: {candidates.columns.tolist()[:10]}...\")\n",
+    "candidates.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📝 Step 5: Create Text Representations (ℝⁿ preparation)\n",
+    "\n",
+    "Transform structured data → unified text → embeddings → vectors ∈ ℝⁿ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"📝 Creating text representations...\\n\")\n",
+    "\n",
+    "# Candidate text\n",
+    "def make_candidate_text(row):\n",
+    "    parts = []\n",
+    "    \n",
+    "    if row.get('skills'): \n",
+    "        parts.append(f\"Skills: {row['skills']}\")\n",
+    "    if row.get('career_objective'): \n",
+    "        parts.append(f\"Objective: {row['career_objective']}\")\n",
+    "    if row.get('educational_institution_name'): \n",
+    "        parts.append(f\"Education: {row['educational_institution_name']}\")\n",
+    "    if row.get('degree_names'): \n",
+    "        parts.append(f\"Degree: {row['degree_names']}\")\n",
+    "    if row.get('major_field_of_studies'): \n",
+    "        parts.append(f\"Field: {row['major_field_of_studies']}\")\n",
+    "    if row.get('positions'): \n",
+    "        parts.append(f\"Experience: {row['positions']}\")\n",
+    "    if row.get('responsibilities'): \n",
+    "        parts.append(f\"Responsibilities: {str(row['responsibilities'])[:200]}\")\n",
+    "    \n",
+    "    return ' | '.join(parts) if parts else \"No info\"\n",
+    "\n",
+    "# Company text (from merged data!)\n",
+    "def make_company_text(row):\n",
+    "    parts = []\n",
+    "    \n",
+    "    if row.get('name'): \n",
+    "        parts.append(f\"Company: {row['name']}\")\n",
+    "    if row.get('description'): \n",
+    "        parts.append(f\"Description: {str(row['description'])[:300]}\")\n",
+    "    if row.get('industries_list'): \n",
+    "        parts.append(f\"Industries: {row['industries_list']}\")\n",
+    "    if row.get('specialties_list'): \n",
+    "        parts.append(f\"Specialties: {row['specialties_list']}\")\n",
+    "    if row.get('employee_count'): \n",
+    "        parts.append(f\"Size: {row['employee_count']} employees\")\n",
+    "    if row.get('follower_count'): \n",
+    "        parts.append(f\"Followers: {row['follower_count']}\")\n",
+    "    if row.get('city') or row.get('state') or row.get('country'): \n",
+    "        loc = f\"{row.get('city', '')}, {row.get('state', '')}, {row.get('country', '')}\"\n",
+    "        parts.append(f\"Location: {loc}\")\n",
+    "    \n",
+    "    return ' | '.join(parts) if parts else \"No info\"\n",
+    "\n",
+    "# Apply\n",
+    "candidates['text'] = candidates.apply(make_candidate_text, axis=1)\n",
+    "companies_full['text'] = companies_full.apply(make_company_text, axis=1)\n",
+    "\n",
+    "print(\"✅ Text created!\")\n",
+    "print(f\"\\n📄 Sample candidate text:\\n{candidates['text'].iloc[0][:300]}...\")\n",
+    "print(f\"\\n📄 Sample company text:\\n{companies_full['text'].iloc[0][:300]}...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧠 Step 6: Generate Embeddings (Transform to ℝⁿ)\n",
+    "\n",
+    "**CRITICAL:** This creates vectors in the SAME mathematical space!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🧠 Loading embedding model...\")\n",
+    "model = SentenceTransformer('all-MiniLM-L6-v2')  # Creates 384-dim vectors\n",
+    "\n",
+    "print(f\"✅ Model loaded! Embedding dimension: {model.get_sentence_embedding_dimension()}\")\n",
+    "print(f\"\\n🔄 Generating candidate vectors (this may take a few minutes)...\")\n",
+    "cand_vectors = model.encode(candidates['text'].tolist(), show_progress_bar=True)\n",
+    "\n",
+    "print(f\"\\n🔄 Generating company vectors (180K companies - this will take time!)...\")\n",
+    "comp_vectors = model.encode(companies_full['text'].tolist(), show_progress_bar=True, batch_size=64)\n",
+    "\n",
+    "print(f\"\\n✅ VECTORS CREATED!\")\n",
+    "print(f\"📊 Candidate vectors: {cand_vectors.shape}\")\n",
+    "print(f\"📊 Company vectors: {comp_vectors.shape}\")\n",
+    "print(f\"\\n🎯 Both live in ℝ^{model.get_sentence_embedding_dimension()} !\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎯 Step 7: Matching Engine (Cosine Similarity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cosine_similarity(a, b):\n",
+    "    \"\"\"Calculate cosine similarity between two vectors.\"\"\"\n",
+    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n",
+    "\n",
+    "def find_top_matches(candidate_idx, top_k=10):\n",
+    "    \"\"\"\n",
+    "    Find top K company matches for a candidate.\n",
+    "    \n",
+    "    Returns: List of (company_idx, similarity_score)\n",
+    "    \"\"\"\n",
+    "    cand_vec = cand_vectors[candidate_idx]\n",
+    "    \n",
+    "    # Calculate similarities with ALL 180K companies\n",
+    "    scores = []\n",
+    "    for i, comp_vec in enumerate(comp_vectors):\n",
+    "        score = cosine_similarity(cand_vec, comp_vec)\n",
+    "        scores.append((i, score))\n",
+    "    \n",
+    "    # Sort by score (descending)\n",
+    "    scores.sort(key=lambda x: x[1], reverse=True)\n",
+    "    \n",
+    "    return scores[:top_k]\n",
+    "\n",
+    "print(\"✅ Matching engine ready!\")\n",
+    "print(f\"📊 Ready to match {len(candidates):,} candidates with {len(companies_full):,} companies!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔍 Step 8: Test - Find Matches for Candidate #0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🔍 Finding top 10 matches for Candidate #0...\\n\")\n",
+    "\n",
+    "matches = find_top_matches(0, top_k=10)\n",
+    "\n",
+    "print(\"🎯 Top 10 Company Matches:\\n\")\n",
+    "print(\"=\" * 80)\n",
+    "print(f\"{'Rank':<6} {'Score':<8} {'Company Name':<40} {'Industry'}\")\n",
+    "print(\"=\" * 80)\n",
+    "\n",
+    "for rank, (comp_idx, score) in enumerate(matches, 1):\n",
+    "    company_name = companies_full.iloc[comp_idx].get('name', 'N/A')[:40]\n",
+    "    industry = companies_full.iloc[comp_idx].get('industries_list', 'N/A')[:30]\n",
+    "    print(f\"{rank:<6} {score:.4f}   {company_name:<40} {industry}\")\n",
+    "\n",
+    "print(\"=\" * 80)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 📊 Step 9: Visualize Match Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get scores for sample\n",
+    "all_scores = []\n",
+    "sample_size = min(100, len(candidates))\n",
+    "\n",
+    "print(f\"📊 Computing match scores for {sample_size} candidates...\")\n",
+    "\n",
+    "for i in range(sample_size):\n",
+    "    if i % 20 == 0:\n",
+    "        print(f\"   Progress: {i}/{sample_size}\")\n",
+    "    matches = find_top_matches(i, top_k=10)\n",
+    "    for comp_idx, score in matches:\n",
+    "        all_scores.append(score)\n",
+    "\n",
+    "# Plot\n",
+    "fig = px.histogram(\n",
+    "    x=all_scores,\n",
+    "    nbins=50,\n",
+    "    title=f'Distribution of Match Scores ({len(candidates):,} candidates × {len(companies_full):,} companies)',\n",
+    "    labels={'x': 'Cosine Similarity Score'}\n",
+    ")\n",
+    "fig.show()\n",
+    "\n",
+    "print(f\"\\n📊 Statistics:\")\n",
+    "print(f\"   Mean: {np.mean(all_scores):.4f}\")\n",
+    "print(f\"   Median: {np.median(all_scores):.4f}\")\n",
+    "print(f\"   Std: {np.std(all_scores):.4f}\")\n",
+    "print(f\"   Max: {np.max(all_scores):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💾 Step 10: Export Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate matches for sample\n",
+    "results = []\n",
+    "export_sample = min(500, len(candidates))  # Export matches for 500 candidates\n",
+    "\n",
+    "print(f\"💾 Generating matches for {export_sample} candidates...\\n\")\n",
+    "\n",
+    "for i in range(export_sample):\n",
+    "    if i % 50 == 0:\n",
+    "        print(f\"   Progress: {i}/{export_sample}\")\n",
+    "    \n",
+    "    matches = find_top_matches(i, top_k=10)\n",
+    "    \n",
+    "    for rank, (comp_idx, score) in enumerate(matches, 1):\n",
+    "        results.append({\n",
+    "            'candidate_id': i,\n",
+    "            'company_id': companies_full.iloc[comp_idx].get('company_id'),\n",
+    "            'company_name': companies_full.iloc[comp_idx].get('name', 'N/A'),\n",
+    "            'rank': rank,\n",
+    "            'similarity_score': float(score),\n",
+    "            'industry': companies_full.iloc[comp_idx].get('industries_list', 'N/A')[:50]\n",
+    "        })\n",
+    "\n",
+    "# Create DataFrame\n",
+    "results_df = pd.DataFrame(results)\n",
+    "results_df.to_csv('hrhub_matches.csv', index=False)\n",
+    "\n",
+    "print(f\"\\n✅ Exported {len(results_df):,} matches to hrhub_matches.csv\")\n",
+    "print(f\"\\n👀 Preview:\")\n",
+    "results_df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎉 DONE!\n",
+    "\n",
+    "### ✅ What you have:\n",
+    "- **9,544 candidates** as vectors ∈ ℝ³⁸⁴\n",
+    "- **180,000 companies** as vectors ∈ ℝ³⁸⁴\n",
+    "- Both in the SAME mathematical space!\n",
+    "- Cosine similarity matching\n",
+    "- Exported results\n",
+    "\n",
+    "### 🚀 Next steps:\n",
+    "1. Add LLM explanations (optional - needs API key)\n",
+    "2. Implement user weights for dimensions\n",
+    "3. Build UI/API on top"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/notebooks/lib/bindings/utils.js

@@ -0,0 +1,189 @@
+function neighbourhoodHighlight(params) {
+  // console.log("in nieghbourhoodhighlight");
+  allNodes = nodes.get({ returnType: "Object" });
+  // originalNodes = JSON.parse(JSON.stringify(allNodes));
+  // if something is selected:
+  if (params.nodes.length > 0) {
+    highlightActive = true;
+    var i, j;
+    var selectedNode = params.nodes[0];
+    var degrees = 2;
+
+    // mark all nodes as hard to read.
+    for (let nodeId in allNodes) {
+      // nodeColors[nodeId] = allNodes[nodeId].color;
+      allNodes[nodeId].color = "rgba(200,200,200,0.5)";
+      if (allNodes[nodeId].hiddenLabel === undefined) {
+        allNodes[nodeId].hiddenLabel = allNodes[nodeId].label;
+        allNodes[nodeId].label = undefined;
+      }
+    }
+    var connectedNodes = network.getConnectedNodes(selectedNode);
+    var allConnectedNodes = [];
+
+    // get the second degree nodes
+    for (i = 1; i < degrees; i++) {
+      for (j = 0; j < connectedNodes.length; j++) {
+        allConnectedNodes = allConnectedNodes.concat(
+          network.getConnectedNodes(connectedNodes[j])
+        );
+      }
+    }
+
+    // all second degree nodes get a different color and their label back
+    for (i = 0; i < allConnectedNodes.length; i++) {
+      // allNodes[allConnectedNodes[i]].color = "pink";
+      allNodes[allConnectedNodes[i]].color = "rgba(150,150,150,0.75)";
+      if (allNodes[allConnectedNodes[i]].hiddenLabel !== undefined) {
+        allNodes[allConnectedNodes[i]].label =
+          allNodes[allConnectedNodes[i]].hiddenLabel;
+        allNodes[allConnectedNodes[i]].hiddenLabel = undefined;
+      }
+    }
+
+    // all first degree nodes get their own color and their label back
+    for (i = 0; i < connectedNodes.length; i++) {
+      // allNodes[connectedNodes[i]].color = undefined;
+      allNodes[connectedNodes[i]].color = nodeColors[connectedNodes[i]];
+      if (allNodes[connectedNodes[i]].hiddenLabel !== undefined) {
+        allNodes[connectedNodes[i]].label =
+          allNodes[connectedNodes[i]].hiddenLabel;
+        allNodes[connectedNodes[i]].hiddenLabel = undefined;
+      }
+    }
+
+    // the main node gets its own color and its label back.
+    // allNodes[selectedNode].color = undefined;
+    allNodes[selectedNode].color = nodeColors[selectedNode];
+    if (allNodes[selectedNode].hiddenLabel !== undefined) {
+      allNodes[selectedNode].label = allNodes[selectedNode].hiddenLabel;
+      allNodes[selectedNode].hiddenLabel = undefined;
+    }
+  } else if (highlightActive === true) {
+    // console.log("highlightActive was true");
+    // reset all nodes
+    for (let nodeId in allNodes) {
+      // allNodes[nodeId].color = "purple";
+      allNodes[nodeId].color = nodeColors[nodeId];
+      // delete allNodes[nodeId].color;
+      if (allNodes[nodeId].hiddenLabel !== undefined) {
+        allNodes[nodeId].label = allNodes[nodeId].hiddenLabel;
+        allNodes[nodeId].hiddenLabel = undefined;
+      }
+    }
+    highlightActive = false;
+  }
+
+  // transform the object into an array
+  var updateArray = [];
+  if (params.nodes.length > 0) {
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        // console.log(allNodes[nodeId]);
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  } else {
+    // console.log("Nothing was selected");
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        // console.log(allNodes[nodeId]);
+        // allNodes[nodeId].color = {};
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  }
+}
+
+function filterHighlight(params) {
+  allNodes = nodes.get({ returnType: "Object" });
+  // if something is selected:
+  if (params.nodes.length > 0) {
+    filterActive = true;
+    let selectedNodes = params.nodes;
+
+    // hiding all nodes and saving the label
+    for (let nodeId in allNodes) {
+      allNodes[nodeId].hidden = true;
+      if (allNodes[nodeId].savedLabel === undefined) {
+        allNodes[nodeId].savedLabel = allNodes[nodeId].label;
+        allNodes[nodeId].label = undefined;
+      }
+    }
+
+    for (let i=0; i < selectedNodes.length; i++) {
+      allNodes[selectedNodes[i]].hidden = false;
+      if (allNodes[selectedNodes[i]].savedLabel !== undefined) {
+        allNodes[selectedNodes[i]].label = allNodes[selectedNodes[i]].savedLabel;
+        allNodes[selectedNodes[i]].savedLabel = undefined;
+      }
+    }
+
+  } else if (filterActive === true) {
+    // reset all nodes
+    for (let nodeId in allNodes) {
+      allNodes[nodeId].hidden = false;
+      if (allNodes[nodeId].savedLabel !== undefined) {
+        allNodes[nodeId].label = allNodes[nodeId].savedLabel;
+        allNodes[nodeId].savedLabel = undefined;
+      }
+    }
+    filterActive = false;
+  }
+
+  // transform the object into an array
+  var updateArray = [];
+  if (params.nodes.length > 0) {
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  } else {
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  }
+}
+
+function selectNode(nodes) {
+  network.selectNodes(nodes);
+  neighbourhoodHighlight({ nodes: nodes });
+  return nodes;
+}
+
+function selectNodes(nodes) {
+  network.selectNodes(nodes);
+  filterHighlight({nodes: nodes});
+  return nodes;
+}
+
+function highlightFilter(filter) {
+  let selectedNodes = []
+  let selectedProp = filter['property']
+  if (filter['item'] === 'node') {
+    let allNodes = nodes.get({ returnType: "Object" });
+    for (let nodeId in allNodes) {
+      if (allNodes[nodeId][selectedProp] && filter['value'].includes((allNodes[nodeId][selectedProp]).toString())) {
+        selectedNodes.push(nodeId)
+      }
+    }
+  }
+  else if (filter['item'] === 'edge'){
+    let allEdges = edges.get({returnType: 'object'});
+    // check if the selected property exists for selected edge and select the nodes connected to the edge
+    for (let edge in allEdges) {
+      if (allEdges[edge][selectedProp] && filter['value'].includes((allEdges[edge][selectedProp]).toString())) {
+        selectedNodes.push(allEdges[edge]['from'])
+        selectedNodes.push(allEdges[edge]['to'])
+      }
+    }
+  }
+  selectNodes(selectedNodes)
+}

data/notebooks/lib/tom-select/tom-select.complete.min.js

@@ -0,0 +1,356 @@
+/**
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+var m=()=>{e.isOpen&&e.positionDropdown()}
+B(document,"mousedown",f),B(window,"scroll",m,c),B(window,"resize",m,c),this._destroy=()=>{document.removeEventListener("mousedown",f),window.removeEventListener("sroll",m),window.removeEventListener("resize",m),h&&h.removeEventListener("click",g)},this.revertSettings={innerHTML:l.innerHTML,tabIndex:l.tabIndex},l.tabIndex=-1,l.insertAdjacentElement("afterend",e.wrapper),e.sync(!1),t.items=[],delete t.optgroups,delete t.options,B(l,"invalid",(t=>{e.isValid&&(e.isValid=!1,e.isInvalid=!0,e.refreshState())})),e.updateOriginalInput(),e.refreshItems(),e.close(!1),e.inputState(),e.isSetup=!0,l.disabled?e.disable():e.enable(),e.on("change",this.onChange),C(l,"tomselected","ts-hidden-accessible"),e.trigger("initialize"),!0===t.preload&&e.preload()}setupOptions(e=[],t=[]){this.addOptions(e),y(t,(e=>{this.registerOptionGroup(e)}))}setupTemplates(){var e=this,t=e.settings.labelField,i=e.settings.optgroupLabelField,s={optgroup:e=>{let t=document.createElement("div")
+return t.className="optgroup",t.appendChild(e.options),t},optgroup_header:(e,t)=>'<div class="optgroup-header">'+t(e[i])+"</div>",option:(e,i)=>"<div>"+i(e[t])+"</div>",item:(e,i)=>"<div>"+i(e[t])+"</div>",option_create:(e,t)=>'<div class="create">Add <strong>'+t(e.input)+"</strong>&hellip;</div>",no_results:()=>'<div class="no-results">No results found</div>',loading:()=>'<div class="spinner"></div>',not_loading:()=>{},dropdown:()=>"<div></div>"}
+e.settings.render=Object.assign({},s,e.settings.render)}setupCallbacks(){var e,t,i={initialize:"onInitialize",change:"onChange",item_add:"onItemAdd",item_remove:"onItemRemove",item_select:"onItemSelect",clear:"onClear",option_add:"onOptionAdd",option_remove:"onOptionRemove",option_clear:"onOptionClear",optgroup_add:"onOptionGroupAdd",optgroup_remove:"onOptionGroupRemove",optgroup_clear:"onOptionGroupClear",dropdown_open:"onDropdownOpen",dropdown_close:"onDropdownClose",type:"onType",load:"onLoad",focus:"onFocus",blur:"onBlur"}
+for(e in i)(t=this.settings[i[e]])&&this.on(e,t)}sync(e=!0){const t=this,i=e?U(t.input,{delimiter:t.settings.delimiter}):t.settings
+t.setupOptions(i.options,i.optgroups),t.setValue(i.items,!0),t.lastQuery=null}onClick(){var e=this
+if(e.activeItems.length>0)return e.clearActiveItems(),void e.focus()
+e.isFocused&&e.isOpen?e.blur():e.focus()}onMouseDown(){}onChange(){_(this.input,"input"),_(this.input,"change")}onPaste(e){var t=this
+t.isFull()||t.isInputHidden||t.isLocked?H(e):t.settings.splitOn&&setTimeout((()=>{var e=t.inputValue()
+if(e.match(t.settings.splitOn)){var i=e.trim().split(t.settings.splitOn)
+y(i,(e=>{t.createItem(e)}))}}),0)}onKeyPress(e){var t=this
+if(!t.isLocked){var i=String.fromCharCode(e.keyCode||e.which)
+return t.settings.create&&"multi"===t.settings.mode&&i===t.settings.delimiter?(t.createItem(),void H(e)):void 0}H(e)}onKeyDown(e){var t=this
+if(t.isLocked)9!==e.keyCode&&H(e)
+else{switch(e.keyCode){case 65:if(K(V,e))return H(e),void t.selectAll()
+break
+case 27:return t.isOpen&&(H(e,!0),t.close()),void t.clearActiveItems()
+case 40:if(!t.isOpen&&t.hasOptions)t.open()
+else if(t.activeOption){let e=t.getAdjacent(t.activeOption,1)
+e&&t.setActiveOption(e)}return void H(e)
+case 38:if(t.activeOption){let e=t.getAdjacent(t.activeOption,-1)
+e&&t.setActiveOption(e)}return void H(e)
+case 13:return void(t.isOpen&&t.activeOption?(t.onOptionSelect(e,t.activeOption),H(e)):t.settings.create&&t.createItem()&&H(e))
+case 37:return void t.advanceSelection(-1,e)
+case 39:return void t.advanceSelection(1,e)
+case 9:return void(t.settings.selectOnTab&&(t.isOpen&&t.activeOption&&(t.onOptionSelect(e,t.activeOption),H(e)),t.settings.create&&t.createItem()&&H(e)))
+case 8:case 46:return void t.deleteSelection(e)}t.isInputHidden&&!K(V,e)&&H(e)}}onInput(e){var t=this
+if(!t.isLocked){var i=t.inputValue()
+t.lastValue!==i&&(t.lastValue=i,t.settings.shouldLoad.call(t,i)&&t.load(i),t.refreshOptions(),t.trigger("type",i))}}onFocus(e){var t=this,i=t.isFocused
+if(t.isDisabled)return t.blur(),void H(e)
+t.ignoreFocus||(t.isFocused=!0,"focus"===t.settings.preload&&t.preload(),i||t.trigger("focus"),t.activeItems.length||(t.showInput(),t.refreshOptions(!!t.settings.openOnFocus)),t.refreshState())}onBlur(e){if(!1!==document.hasFocus()){var t=this
+if(t.isFocused){t.isFocused=!1,t.ignoreFocus=!1
+var i=()=>{t.close(),t.setActiveItem(),t.setCaret(t.items.length),t.trigger("blur")}
+t.settings.create&&t.settings.createOnBlur?t.createItem(null,!1,i):i()}}}onOptionSelect(e,t){var i,s=this
+t&&(t.parentElement&&t.parentElement.matches("[data-disabled]")||(t.classList.contains("create")?s.createItem(null,!0,(()=>{s.settings.closeAfterSelect&&s.close()})):void 0!==(i=t.dataset.value)&&(s.lastQuery=null,s.addItem(i),s.settings.closeAfterSelect&&s.close(),!s.settings.hideSelected&&e.type&&/click/.test(e.type)&&s.setActiveOption(t))))}onItemSelect(e,t){var i=this
+return!i.isLocked&&"multi"===i.settings.mode&&(H(e),i.setActiveItem(t,e),!0)}canLoad(e){return!!this.settings.load&&!this.loadedSearches.hasOwnProperty(e)}load(e){const t=this
+if(!t.canLoad(e))return
+C(t.wrapper,t.settings.loadingClass),t.loading++
+const i=t.loadCallback.bind(t)
+t.settings.load.call(t,e,i)}loadCallback(e,t){const i=this
+i.loading=Math.max(i.loading-1,0),i.lastQuery=null,i.clearActiveOption(),i.setupOptions(e,t),i.refreshOptions(i.isFocused&&!i.isInputHidden),i.loading||S(i.wrapper,i.settings.loadingClass),i.trigger("load",e,t)}preload(){var e=this.wrapper.classList
+e.contains("preloaded")||(e.add("preloaded"),this.load(""))}setTextboxValue(e=""){var t=this.control_input
+t.value!==e&&(t.value=e,_(t,"update"),this.lastValue=e)}getValue(){return this.is_select_tag&&this.input.hasAttribute("multiple")?this.items:this.items.join(this.settings.delimiter)}setValue(e,t){R(this,t?[]:["change"],(()=>{this.clear(t),this.addItems(e,t)}))}setMaxItems(e){0===e&&(e=null),this.settings.maxItems=e,this.refreshState()}setActiveItem(e,t){var i,s,n,o,r,l,a=this
+if("single"!==a.settings.mode){if(!e)return a.clearActiveItems(),void(a.isFocused&&a.showInput())
+if("click"===(i=t&&t.type.toLowerCase())&&K("shiftKey",t)&&a.activeItems.length){for(l=a.getLastActive(),(n=Array.prototype.indexOf.call(a.control.children,l))>(o=Array.prototype.indexOf.call(a.control.children,e))&&(r=n,n=o,o=r),s=n;s<=o;s++)e=a.control.children[s],-1===a.activeItems.indexOf(e)&&a.setActiveItemClass(e)
+H(t)}else"click"===i&&K(V,t)||"keydown"===i&&K("shiftKey",t)?e.classList.contains("active")?a.removeActiveItem(e):a.setActiveItemClass(e):(a.clearActiveItems(),a.setActiveItemClass(e))
+a.hideInput(),a.isFocused||a.focus()}}setActiveItemClass(e){const t=this,i=t.control.querySelector(".last-active")
+i&&S(i,"last-active"),C(e,"active last-active"),t.trigger("item_select",e),-1==t.activeItems.indexOf(e)&&t.activeItems.push(e)}removeActiveItem(e){var t=this.activeItems.indexOf(e)
+this.activeItems.splice(t,1),S(e,"active")}clearActiveItems(){S(this.activeItems,"active"),this.activeItems=[]}setActiveOption(e){e!==this.activeOption&&(this.clearActiveOption(),e&&(this.activeOption=e,P(this.focus_node,{"aria-activedescendant":e.getAttribute("id")}),P(e,{"aria-selected":"true"}),C(e,"active"),this.scrollToOption(e)))}scrollToOption(e,t){if(!e)return
+const i=this.dropdown_content,s=i.clientHeight,n=i.scrollTop||0,o=e.offsetHeight,r=e.getBoundingClientRect().top-i.getBoundingClientRect().top+n
+r+o>s+n?this.scroll(r-s+o,t):r<n&&this.scroll(r,t)}scroll(e,t){const i=this.dropdown_content
+t&&(i.style.scrollBehavior=t),i.scrollTop=e,i.style.scrollBehavior=""}clearActiveOption(){this.activeOption&&(S(this.activeOption,"active"),P(this.activeOption,{"aria-selected":null})),this.activeOption=null,P(this.focus_node,{"aria-activedescendant":null})}selectAll(){if("single"===this.settings.mode)return
+const e=this.controlChildren()
+e.length&&(this.hideInput(),this.close(),this.activeItems=e,C(e,"active"))}inputState(){var e=this
+e.control.contains(e.control_input)&&(P(e.control_input,{placeholder:e.settings.placeholder}),e.activeItems.length>0||!e.isFocused&&e.settings.hidePlaceholder&&e.items.length>0?(e.setTextboxValue(),e.isInputHidden=!0):(e.settings.hidePlaceholder&&e.items.length>0&&P(e.control_input,{placeholder:""}),e.isInputHidden=!1),e.wrapper.classList.toggle("input-hidden",e.isInputHidden))}hideInput(){this.inputState()}showInput(){this.inputState()}inputValue(){return this.control_input.value.trim()}focus(){var e=this
+e.isDisabled||(e.ignoreFocus=!0,e.control_input.offsetWidth?e.control_input.focus():e.focus_node.focus(),setTimeout((()=>{e.ignoreFocus=!1,e.onFocus()}),0))}blur(){this.focus_node.blur(),this.onBlur()}getScoreFunction(e){return this.sifter.getScoreFunction(e,this.getSearchOptions())}getSearchOptions(){var e=this.settings,t=e.sortField
+return"string"==typeof e.sortField&&(t=[{field:e.sortField}]),{fields:e.searchField,conjunction:e.searchConjunction,sort:t,nesting:e.nesting}}search(e){var t,i,s,n=this,o=this.getSearchOptions()
+if(n.settings.score&&"function"!=typeof(s=n.settings.score.call(n,e)))throw new Error('Tom Select "score" setting must be a function that returns a function')
+if(e!==n.lastQuery?(n.lastQuery=e,i=n.sifter.search(e,Object.assign(o,{score:s})),n.currentResults=i):i=Object.assign({},n.currentResults),n.settings.hideSelected)for(t=i.items.length-1;t>=0;t--){let e=q(i.items[t].id)
+e&&-1!==n.items.indexOf(e)&&i.items.splice(t,1)}return i}refreshOptions(e=!0){var t,i,s,n,o,r,l,a,c,d,p
+const u={},h=[]
+var g,f=this,v=f.inputValue(),m=f.search(v),O=f.activeOption,b=f.settings.shouldOpen||!1,w=f.dropdown_content
+for(O&&(c=O.dataset.value,d=O.closest("[data-group]")),n=m.items.length,"number"==typeof f.settings.maxOptions&&(n=Math.min(n,f.settings.maxOptions)),n>0&&(b=!0),t=0;t<n;t++){let e=m.items[t].id,n=f.options[e],l=f.getOption(e,!0)
+for(f.settings.hideSelected||l.classList.toggle("selected",f.items.includes(e)),o=n[f.settings.optgroupField]||"",i=0,s=(r=Array.isArray(o)?o:[o])&&r.length;i<s;i++)o=r[i],f.optgroups.hasOwnProperty(o)||(o=""),u.hasOwnProperty(o)||(u[o]=document.createDocumentFragment(),h.push(o)),i>0&&(l=l.cloneNode(!0),P(l,{id:n.$id+"-clone-"+i,"aria-selected":null}),l.classList.add("ts-cloned"),S(l,"active")),c==e&&d&&d.dataset.group===o&&(O=l),u[o].appendChild(l)}this.settings.lockOptgroupOrder&&h.sort(((e,t)=>(f.optgroups[e]&&f.optgroups[e].$order||0)-(f.optgroups[t]&&f.optgroups[t].$order||0))),l=document.createDocumentFragment(),y(h,(e=>{if(f.optgroups.hasOwnProperty(e)&&u[e].children.length){let t=document.createDocumentFragment(),i=f.render("optgroup_header",f.optgroups[e])
+G(t,i),G(t,u[e])
+let s=f.render("optgroup",{group:f.optgroups[e],options:t})
+G(l,s)}else G(l,u[e])})),w.innerHTML="",G(w,l),f.settings.highlight&&(g=w.querySelectorAll("span.highlight"),Array.prototype.forEach.call(g,(function(e){var t=e.parentNode
+t.replaceChild(e.firstChild,e),t.normalize()})),m.query.length&&m.tokens.length&&y(m.tokens,(e=>{T(w,e.regex)})))
+var _=e=>{let t=f.render(e,{input:v})
+return t&&(b=!0,w.insertBefore(t,w.firstChild)),t}
+if(f.loading?_("loading"):f.settings.shouldLoad.call(f,v)?0===m.items.length&&_("no_results"):_("not_loading"),(a=f.canCreate(v))&&(p=_("option_create")),f.hasOptions=m.items.length>0||a,b){if(m.items.length>0){if(!w.contains(O)&&"single"===f.settings.mode&&f.items.length&&(O=f.getOption(f.items[0])),!w.contains(O)){let e=0
+p&&!f.settings.addPrecedence&&(e=1),O=f.selectable()[e]}}else p&&(O=p)
+e&&!f.isOpen&&(f.open(),f.scrollToOption(O,"auto")),f.setActiveOption(O)}else f.clearActiveOption(),e&&f.isOpen&&f.close(!1)}selectable(){return this.dropdown_content.querySelectorAll("[data-selectable]")}addOption(e,t=!1){const i=this
+if(Array.isArray(e))return i.addOptions(e,t),!1
+const s=q(e[i.settings.valueField])
+return null!==s&&!i.options.hasOwnProperty(s)&&(e.$order=e.$order||++i.order,e.$id=i.inputId+"-opt-"+e.$order,i.options[s]=e,i.lastQuery=null,t&&(i.userOptions[s]=t,i.trigger("option_add",s,e)),s)}addOptions(e,t=!1){y(e,(e=>{this.addOption(e,t)}))}registerOption(e){return this.addOption(e)}registerOptionGroup(e){var t=q(e[this.settings.optgroupValueField])
+return null!==t&&(e.$order=e.$order||++this.order,this.optgroups[t]=e,t)}addOptionGroup(e,t){var i
+t[this.settings.optgroupValueField]=e,(i=this.registerOptionGroup(t))&&this.trigger("optgroup_add",i,t)}removeOptionGroup(e){this.optgroups.hasOwnProperty(e)&&(delete this.optgroups[e],this.clearCache(),this.trigger("optgroup_remove",e))}clearOptionGroups(){this.optgroups={},this.clearCache(),this.trigger("optgroup_clear")}updateOption(e,t){const i=this
+var s,n
+const o=q(e),r=q(t[i.settings.valueField])
+if(null===o)return
+if(!i.options.hasOwnProperty(o))return
+if("string"!=typeof r)throw new Error("Value must be set in option data")
+const l=i.getOption(o),a=i.getItem(o)
+if(t.$order=t.$order||i.options[o].$order,delete i.options[o],i.uncacheValue(r),i.options[r]=t,l){if(i.dropdown_content.contains(l)){const e=i._render("option",t)
+E(l,e),i.activeOption===l&&i.setActiveOption(e)}l.remove()}a&&(-1!==(n=i.items.indexOf(o))&&i.items.splice(n,1,r),s=i._render("item",t),a.classList.contains("active")&&C(s,"active"),E(a,s)),i.lastQuery=null}removeOption(e,t){const i=this
+e=D(e),i.uncacheValue(e),delete i.userOptions[e],delete i.options[e],i.lastQuery=null,i.trigger("option_remove",e),i.removeItem(e,t)}clearOptions(){this.loadedSearches={},this.userOptions={},this.clearCache()
+var e={}
+y(this.options,((t,i)=>{this.items.indexOf(i)>=0&&(e[i]=this.options[i])})),this.options=this.sifter.items=e,this.lastQuery=null,this.trigger("option_clear")}getOption(e,t=!1){const i=q(e)
+if(null!==i&&this.options.hasOwnProperty(i)){const e=this.options[i]
+if(e.$div)return e.$div
+if(t)return this._render("option",e)}return null}getAdjacent(e,t,i="option"){var s
+if(!e)return null
+s="item"==i?this.controlChildren():this.dropdown_content.querySelectorAll("[data-selectable]")
+for(let i=0;i<s.length;i++)if(s[i]==e)return t>0?s[i+1]:s[i-1]
+return null}getItem(e){if("object"==typeof e)return e
+var t=q(e)
+return null!==t?this.control.querySelector(`[data-value="${Q(t)}"]`):null}addItems(e,t){var i=this,s=Array.isArray(e)?e:[e]
+for(let e=0,n=(s=s.filter((e=>-1===i.items.indexOf(e)))).length;e<n;e++)i.isPending=e<n-1,i.addItem(s[e],t)}addItem(e,t){R(this,t?[]:["change"],(()=>{var i,s
+const n=this,o=n.settings.mode,r=q(e)
+if((!r||-1===n.items.indexOf(r)||("single"===o&&n.close(),"single"!==o&&n.settings.duplicates))&&null!==r&&n.options.hasOwnProperty(r)&&("single"===o&&n.clear(t),"multi"!==o||!n.isFull())){if(i=n._render("item",n.options[r]),n.control.contains(i)&&(i=i.cloneNode(!0)),s=n.isFull(),n.items.splice(n.caretPos,0,r),n.insertAtCaret(i),n.isSetup){if(!n.isPending&&n.settings.hideSelected){let e=n.getOption(r),t=n.getAdjacent(e,1)
+t&&n.setActiveOption(t)}n.isPending||n.refreshOptions(n.isFocused&&"single"!==o),0!=n.settings.closeAfterSelect&&n.isFull()?n.close():n.isPending||n.positionDropdown(),n.trigger("item_add",r,i),n.isPending||n.updateOriginalInput({silent:t})}(!n.isPending||!s&&n.isFull())&&(n.inputState(),n.refreshState())}}))}removeItem(e=null,t){const i=this
+if(!(e=i.getItem(e)))return
+var s,n
+const o=e.dataset.value
+s=L(e),e.remove(),e.classList.contains("active")&&(n=i.activeItems.indexOf(e),i.activeItems.splice(n,1),S(e,"active")),i.items.splice(s,1),i.lastQuery=null,!i.settings.persist&&i.userOptions.hasOwnProperty(o)&&i.removeOption(o,t),s<i.caretPos&&i.setCaret(i.caretPos-1),i.updateOriginalInput({silent:t}),i.refreshState(),i.positionDropdown(),i.trigger("item_remove",o,e)}createItem(e=null,t=!0,i=(()=>{})){var s,n=this,o=n.caretPos
+if(e=e||n.inputValue(),!n.canCreate(e))return i(),!1
+n.lock()
+var r=!1,l=e=>{if(n.unlock(),!e||"object"!=typeof e)return i()
+var s=q(e[n.settings.valueField])
+if("string"!=typeof s)return i()
+n.setTextboxValue(),n.addOption(e,!0),n.setCaret(o),n.addItem(s),n.refreshOptions(t&&"single"!==n.settings.mode),i(e),r=!0}
+return s="function"==typeof n.settings.create?n.settings.create.call(this,e,l):{[n.settings.labelField]:e,[n.settings.valueField]:e},r||l(s),!0}refreshItems(){var e=this
+e.lastQuery=null,e.isSetup&&e.addItems(e.items),e.updateOriginalInput(),e.refreshState()}refreshState(){const e=this
+e.refreshValidityState()
+const t=e.isFull(),i=e.isLocked
+e.wrapper.classList.toggle("rtl",e.rtl)
+const s=e.wrapper.classList
+var n
+s.toggle("focus",e.isFocused),s.toggle("disabled",e.isDisabled),s.toggle("required",e.isRequired),s.toggle("invalid",!e.isValid),s.toggle("locked",i),s.toggle("full",t),s.toggle("input-active",e.isFocused&&!e.isInputHidden),s.toggle("dropdown-active",e.isOpen),s.toggle("has-options",(n=e.options,0===Object.keys(n).length)),s.toggle("has-items",e.items.length>0)}refreshValidityState(){var e=this
+e.input.checkValidity&&(e.isValid=e.input.checkValidity(),e.isInvalid=!e.isValid)}isFull(){return null!==this.settings.maxItems&&this.items.length>=this.settings.maxItems}updateOriginalInput(e={}){const t=this
+var i,s
+const n=t.input.querySelector('option[value=""]')
+if(t.is_select_tag){const e=[]
+function o(i,s,o){return i||(i=w('<option value="'+N(s)+'">'+N(o)+"</option>")),i!=n&&t.input.append(i),e.push(i),i.selected=!0,i}t.input.querySelectorAll("option:checked").forEach((e=>{e.selected=!1})),0==t.items.length&&"single"==t.settings.mode?o(n,"",""):t.items.forEach((n=>{if(i=t.options[n],s=i[t.settings.labelField]||"",e.includes(i.$option)){o(t.input.querySelector(`option[value="${Q(n)}"]:not(:checked)`),n,s)}else i.$option=o(i.$option,n,s)}))}else t.input.value=t.getValue()
+t.isSetup&&(e.silent||t.trigger("change",t.getValue()))}open(){var e=this
+e.isLocked||e.isOpen||"multi"===e.settings.mode&&e.isFull()||(e.isOpen=!0,P(e.focus_node,{"aria-expanded":"true"}),e.refreshState(),I(e.dropdown,{visibility:"hidden",display:"block"}),e.positionDropdown(),I(e.dropdown,{visibility:"visible",display:"block"}),e.focus(),e.trigger("dropdown_open",e.dropdown))}close(e=!0){var t=this,i=t.isOpen
+e&&(t.setTextboxValue(),"single"===t.settings.mode&&t.items.length&&t.hideInput()),t.isOpen=!1,P(t.focus_node,{"aria-expanded":"false"}),I(t.dropdown,{display:"none"}),t.settings.hideSelected&&t.clearActiveOption(),t.refreshState(),i&&t.trigger("dropdown_close",t.dropdown)}positionDropdown(){if("body"===this.settings.dropdownParent){var e=this.control,t=e.getBoundingClientRect(),i=e.offsetHeight+t.top+window.scrollY,s=t.left+window.scrollX
+I(this.dropdown,{width:t.width+"px",top:i+"px",left:s+"px"})}}clear(e){var t=this
+if(t.items.length){var i=t.controlChildren()
+y(i,(e=>{t.removeItem(e,!0)})),t.showInput(),e||t.updateOriginalInput(),t.trigger("clear")}}insertAtCaret(e){const t=this,i=t.caretPos,s=t.control
+s.insertBefore(e,s.children[i]),t.setCaret(i+1)}deleteSelection(e){var t,i,s,n,o,r=this
+t=e&&8===e.keyCode?-1:1,i={start:(o=r.control_input).selectionStart||0,length:(o.selectionEnd||0)-(o.selectionStart||0)}
+const l=[]
+if(r.activeItems.length)n=F(r.activeItems,t),s=L(n),t>0&&s++,y(r.activeItems,(e=>l.push(e)))
+else if((r.isFocused||"single"===r.settings.mode)&&r.items.length){const e=r.controlChildren()
+t<0&&0===i.start&&0===i.length?l.push(e[r.caretPos-1]):t>0&&i.start===r.inputValue().length&&l.push(e[r.caretPos])}const a=l.map((e=>e.dataset.value))
+if(!a.length||"function"==typeof r.settings.onDelete&&!1===r.settings.onDelete.call(r,a,e))return!1
+for(H(e,!0),void 0!==s&&r.setCaret(s);l.length;)r.removeItem(l.pop())
+return r.showInput(),r.positionDropdown(),r.refreshOptions(!1),!0}advanceSelection(e,t){var i,s,n=this
+n.rtl&&(e*=-1),n.inputValue().length||(K(V,t)||K("shiftKey",t)?(s=(i=n.getLastActive(e))?i.classList.contains("active")?n.getAdjacent(i,e,"item"):i:e>0?n.control_input.nextElementSibling:n.control_input.previousElementSibling)&&(s.classList.contains("active")&&n.removeActiveItem(i),n.setActiveItemClass(s)):n.moveCaret(e))}moveCaret(e){}getLastActive(e){let t=this.control.querySelector(".last-active")
+if(t)return t
+var i=this.control.querySelectorAll(".active")
+return i?F(i,e):void 0}setCaret(e){this.caretPos=this.items.length}controlChildren(){return Array.from(this.control.querySelectorAll("[data-ts-item]"))}lock(){this.close(),this.isLocked=!0,this.refreshState()}unlock(){this.isLocked=!1,this.refreshState()}disable(){var e=this
+e.input.disabled=!0,e.control_input.disabled=!0,e.focus_node.tabIndex=-1,e.isDisabled=!0,e.lock()}enable(){var e=this
+e.input.disabled=!1,e.control_input.disabled=!1,e.focus_node.tabIndex=e.tabIndex,e.isDisabled=!1,e.unlock()}destroy(){var e=this,t=e.revertSettings
+e.trigger("destroy"),e.off(),e.wrapper.remove(),e.dropdown.remove(),e.input.innerHTML=t.innerHTML,e.input.tabIndex=t.tabIndex,S(e.input,"tomselected","ts-hidden-accessible"),e._destroy(),delete e.input.tomselect}render(e,t){return"function"!=typeof this.settings.render[e]?null:this._render(e,t)}_render(e,t){var i,s,n=""
+const o=this
+return"option"!==e&&"item"!=e||(n=D(t[o.settings.valueField])),null==(s=o.settings.render[e].call(this,t,N))||(s=w(s),"option"===e||"option_create"===e?t[o.settings.disabledField]?P(s,{"aria-disabled":"true"}):P(s,{"data-selectable":""}):"optgroup"===e&&(i=t.group[o.settings.optgroupValueField],P(s,{"data-group":i}),t.group[o.settings.disabledField]&&P(s,{"data-disabled":""})),"option"!==e&&"item"!==e||(P(s,{"data-value":n}),"item"===e?(C(s,o.settings.itemClass),P(s,{"data-ts-item":""})):(C(s,o.settings.optionClass),P(s,{role:"option",id:t.$id}),o.options[n].$div=s))),s}clearCache(){y(this.options,((e,t)=>{e.$div&&(e.$div.remove(),delete e.$div)}))}uncacheValue(e){const t=this.getOption(e)
+t&&t.remove()}canCreate(e){return this.settings.create&&e.length>0&&this.settings.createFilter.call(this,e)}hook(e,t,i){var s=this,n=s[t]
+s[t]=function(){var t,o
+return"after"===e&&(t=n.apply(s,arguments)),o=i.apply(s,arguments),"instead"===e?o:("before"===e&&(t=n.apply(s,arguments)),t)}}}return J.define("change_listener",(function(){B(this.input,"change",(()=>{this.sync()}))})),J.define("checkbox_options",(function(){var e=this,t=e.onOptionSelect
+e.settings.hideSelected=!1
+var i=function(e){setTimeout((()=>{var t=e.querySelector("input")
+e.classList.contains("selected")?t.checked=!0:t.checked=!1}),1)}
+e.hook("after","setupTemplates",(()=>{var t=e.settings.render.option
+e.settings.render.option=(i,s)=>{var n=w(t.call(e,i,s)),o=document.createElement("input")
+o.addEventListener("click",(function(e){H(e)})),o.type="checkbox"
+const r=q(i[e.settings.valueField])
+return r&&e.items.indexOf(r)>-1&&(o.checked=!0),n.prepend(o),n}})),e.on("item_remove",(t=>{var s=e.getOption(t)
+s&&(s.classList.remove("selected"),i(s))})),e.hook("instead","onOptionSelect",((s,n)=>{if(n.classList.contains("selected"))return n.classList.remove("selected"),e.removeItem(n.dataset.value),e.refreshOptions(),void H(s,!0)
+t.call(e,s,n),i(n)}))})),J.define("clear_button",(function(e){const t=this,i=Object.assign({className:"clear-button",title:"Clear All",html:e=>`<div class="${e.className}" title="${e.title}">&times;</div>`},e)
+t.on("initialize",(()=>{var e=w(i.html(i))
+e.addEventListener("click",(e=>{t.clear(),"single"===t.settings.mode&&t.settings.allowEmptyOption&&t.addItem(""),e.preventDefault(),e.stopPropagation()})),t.control.appendChild(e)}))})),J.define("drag_drop",(function(){var e=this
+if(!$.fn.sortable)throw new Error('The "drag_drop" plugin requires jQuery UI "sortable".')
+if("multi"===e.settings.mode){var t=e.lock,i=e.unlock
+e.hook("instead","lock",(()=>{var i=$(e.control).data("sortable")
+return i&&i.disable(),t.call(e)})),e.hook("instead","unlock",(()=>{var t=$(e.control).data("sortable")
+return t&&t.enable(),i.call(e)})),e.on("initialize",(()=>{var t=$(e.control).sortable({items:"[data-value]",forcePlaceholderSize:!0,disabled:e.isLocked,start:(e,i)=>{i.placeholder.css("width",i.helper.css("width")),t.css({overflow:"visible"})},stop:()=>{t.css({overflow:"hidden"})
+var i=[]
+t.children("[data-value]").each((function(){this.dataset.value&&i.push(this.dataset.value)})),e.setValue(i)}})}))}})),J.define("dropdown_header",(function(e){const t=this,i=Object.assign({title:"Untitled",headerClass:"dropdown-header",titleRowClass:"dropdown-header-title",labelClass:"dropdown-header-label",closeClass:"dropdown-header-close",html:e=>'<div class="'+e.headerClass+'"><div class="'+e.titleRowClass+'"><span class="'+e.labelClass+'">'+e.title+'</span><a class="'+e.closeClass+'">&times;</a></div></div>'},e)
+t.on("initialize",(()=>{var e=w(i.html(i)),s=e.querySelector("."+i.closeClass)
+s&&s.addEventListener("click",(e=>{H(e,!0),t.close()})),t.dropdown.insertBefore(e,t.dropdown.firstChild)}))})),J.define("caret_position",(function(){var e=this
+e.hook("instead","setCaret",(t=>{"single"!==e.settings.mode&&e.control.contains(e.control_input)?(t=Math.max(0,Math.min(e.items.length,t)))==e.caretPos||e.isPending||e.controlChildren().forEach(((i,s)=>{s<t?e.control_input.insertAdjacentElement("beforebegin",i):e.control.appendChild(i)})):t=e.items.length,e.caretPos=t})),e.hook("instead","moveCaret",(t=>{if(!e.isFocused)return
+const i=e.getLastActive(t)
+if(i){const s=L(i)
+e.setCaret(t>0?s+1:s),e.setActiveItem()}else e.setCaret(e.caretPos+t)}))})),J.define("dropdown_input",(function(){var e=this
+e.settings.shouldOpen=!0,e.hook("before","setup",(()=>{e.focus_node=e.control,C(e.control_input,"dropdown-input")
+const t=w('<div class="dropdown-input-wrap">')
+t.append(e.control_input),e.dropdown.insertBefore(t,e.dropdown.firstChild)})),e.on("initialize",(()=>{e.control_input.addEventListener("keydown",(t=>{switch(t.keyCode){case 27:return e.isOpen&&(H(t,!0),e.close()),void e.clearActiveItems()
+case 9:e.focus_node.tabIndex=-1}return e.onKeyDown.call(e,t)})),e.on("blur",(()=>{e.focus_node.tabIndex=e.isDisabled?-1:e.tabIndex})),e.on("dropdown_open",(()=>{e.control_input.focus()}))
+const t=e.onBlur
+e.hook("instead","onBlur",(i=>{if(!i||i.relatedTarget!=e.control_input)return t.call(e)})),B(e.control_input,"blur",(()=>e.onBlur())),e.hook("before","close",(()=>{e.isOpen&&e.focus_node.focus()}))}))})),J.define("input_autogrow",(function(){var e=this
+e.on("initialize",(()=>{var t=document.createElement("span"),i=e.control_input
+t.style.cssText="position:absolute; top:-99999px; left:-99999px; width:auto; padding:0; white-space:pre; ",e.wrapper.appendChild(t)
+for(const e of["letterSpacing","fontSize","fontFamily","fontWeight","textTransform"])t.style[e]=i.style[e]
+var s=()=>{e.items.length>0?(t.textContent=i.value,i.style.width=t.clientWidth+"px"):i.style.width=""}
+s(),e.on("update item_add item_remove",s),B(i,"input",s),B(i,"keyup",s),B(i,"blur",s),B(i,"update",s)}))})),J.define("no_backspace_delete",(function(){var e=this,t=e.deleteSelection
+this.hook("instead","deleteSelection",(i=>!!e.activeItems.length&&t.call(e,i)))})),J.define("no_active_items",(function(){this.hook("instead","setActiveItem",(()=>{})),this.hook("instead","selectAll",(()=>{}))})),J.define("optgroup_columns",(function(){var e=this,t=e.onKeyDown
+e.hook("instead","onKeyDown",(i=>{var s,n,o,r
+if(!e.isOpen||37!==i.keyCode&&39!==i.keyCode)return t.call(e,i)
+r=k(e.activeOption,"[data-group]"),s=L(e.activeOption,"[data-selectable]"),r&&(r=37===i.keyCode?r.previousSibling:r.nextSibling)&&(n=(o=r.querySelectorAll("[data-selectable]"))[Math.min(o.length-1,s)])&&e.setActiveOption(n)}))})),J.define("remove_button",(function(e){const t=Object.assign({label:"&times;",title:"Remove",className:"remove",append:!0},e)
+var i=this
+if(t.append){var s='<a href="javascript:void(0)" class="'+t.className+'" tabindex="-1" title="'+N(t.title)+'">'+t.label+"</a>"
+i.hook("after","setupTemplates",(()=>{var e=i.settings.render.item
+i.settings.render.item=(t,n)=>{var o=w(e.call(i,t,n)),r=w(s)
+return o.appendChild(r),B(r,"mousedown",(e=>{H(e,!0)})),B(r,"click",(e=>{if(H(e,!0),!i.isLocked){var t=o.dataset.value
+i.removeItem(t),i.refreshOptions(!1)}})),o}}))}})),J.define("restore_on_backspace",(function(e){const t=this,i=Object.assign({text:e=>e[t.settings.labelField]},e)
+t.on("item_remove",(function(e){if(""===t.control_input.value.trim()){var s=t.options[e]
+s&&t.setTextboxValue(i.text.call(t,s))}}))})),J.define("virtual_scroll",(function(){const e=this,t=e.canLoad,i=e.clearActiveOption,s=e.loadCallback
+var n,o={},r=!1
+if(!e.settings.firstUrl)throw"virtual_scroll plugin requires a firstUrl() method"
+function l(t){return!("number"==typeof e.settings.maxOptions&&n.children.length>=e.settings.maxOptions)&&!(!(t in o)||!o[t])}e.settings.sortField=[{field:"$order"},{field:"$score"}],e.setNextUrl=function(e,t){o[e]=t},e.getUrl=function(t){if(t in o){const e=o[t]
+return o[t]=!1,e}return o={},e.settings.firstUrl(t)},e.hook("instead","clearActiveOption",(()=>{if(!r)return i.call(e)})),e.hook("instead","canLoad",(i=>i in o?l(i):t.call(e,i))),e.hook("instead","loadCallback",((t,i)=>{r||e.clearOptions(),s.call(e,t,i),r=!1})),e.hook("after","refreshOptions",(()=>{const t=e.lastValue
+var i
+l(t)?(i=e.render("loading_more",{query:t}))&&i.setAttribute("data-selectable",""):t in o&&!n.querySelector(".no-results")&&(i=e.render("no_more_results",{query:t})),i&&(C(i,e.settings.optionClass),n.append(i))})),e.on("initialize",(()=>{n=e.dropdown_content,e.settings.render=Object.assign({},{loading_more:function(){return'<div class="loading-more-results">Loading more results ... </div>'},no_more_results:function(){return'<div class="no-more-results">No more results</div>'}},e.settings.render),n.addEventListener("scroll",(function(){n.clientHeight/(n.scrollHeight-n.scrollTop)<.95||l(e.lastValue)&&(r||(r=!0,e.load.call(e,e.lastValue)))}))}))})),J}))
+var tomSelect=function(e,t){return new TomSelect(e,t)}
+//# sourceMappingURL=tom-select.complete.min.js.map

data/notebooks/lib/tom-select/tom-select.css

@@ -0,0 +1,334 @@
+/**
+ * tom-select.css (v2.0.0-rc.4)
+ * Copyright (c) contributors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this
+ * file except in compliance with the License. You may obtain a copy of the License at:
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under
+ * the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+ * ANY KIND, either express or implied. See the License for the specific language
+ * governing permissions and limitations under the License.
+ *
+ */
+.ts-wrapper.plugin-drag_drop.multi > .ts-control > div.ui-sortable-placeholder {
+  visibility: visible !important;
+  background: #f2f2f2 !important;
+  background: rgba(0, 0, 0, 0.06) !important;
+  border: 0 none !important;
+  box-shadow: inset 0 0 12px 4px #fff; }
+
+.ts-wrapper.plugin-drag_drop .ui-sortable-placeholder::after {
+  content: '!';
+  visibility: hidden; }
+
+.ts-wrapper.plugin-drag_drop .ui-sortable-helper {
+  box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2); }
+
+.plugin-checkbox_options .option input {
+  margin-right: 0.5rem; }
+
+.plugin-clear_button .ts-control {
+  padding-right: calc( 1em + (3 * 6px)) !important; }
+
+.plugin-clear_button .clear-button {
+  opacity: 0;
+  position: absolute;
+  top: 8px;
+  right: calc(8px - 6px);
+  margin-right: 0 !important;
+  background: transparent !important;
+  transition: opacity 0.5s;
+  cursor: pointer; }
+
+.plugin-clear_button.single .clear-button {
+  right: calc(8px - 6px +  2rem); }
+
+.plugin-clear_button.focus.has-items .clear-button,
+.plugin-clear_button:hover.has-items .clear-button {
+  opacity: 1; }
+
+.ts-wrapper .dropdown-header {
+  position: relative;
+  padding: 10px 8px;
+  border-bottom: 1px solid #d0d0d0;
+  background: #f8f8f8;
+  border-radius: 3px 3px 0 0; }
+
+.ts-wrapper .dropdown-header-close {
+  position: absolute;
+  right: 8px;
+  top: 50%;
+  color: #303030;
+  opacity: 0.4;
+  margin-top: -12px;
+  line-height: 20px;
+  font-size: 20px !important; }
+
+.ts-wrapper .dropdown-header-close:hover {
+  color: black; }
+
+.plugin-dropdown_input.focus.dropdown-active .ts-control {
+  box-shadow: none;
+  border: 1px solid #d0d0d0; }
+
+.plugin-dropdown_input .dropdown-input {
+  border: 1px solid #d0d0d0;
+  border-width: 0 0 1px 0;
+  display: block;
+  padding: 8px 8px;
+  box-shadow: none;
+  width: 100%;
+  background: transparent; }
+
+.ts-wrapper.plugin-input_autogrow.has-items .ts-control > input {
+  min-width: 0; }
+
+.ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input {
+  flex: none;
+  min-width: 4px; }
+  .ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input::-webkit-input-placeholder {
+    color: transparent; }
+  .ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input::-ms-input-placeholder {
+    color: transparent; }
+  .ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input::placeholder {
+    color: transparent; }
+
+.ts-dropdown.plugin-optgroup_columns .ts-dropdown-content {
+  display: flex; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup {
+  border-right: 1px solid #f2f2f2;
+  border-top: 0 none;
+  flex-grow: 1;
+  flex-basis: 0;
+  min-width: 0; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup:last-child {
+  border-right: 0 none; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup:before {
+  display: none; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup-header {
+  border-top: 0 none; }
+
+.ts-wrapper.plugin-remove_button .item {
+  display: inline-flex;
+  align-items: center;
+  padding-right: 0 !important; }
+
+.ts-wrapper.plugin-remove_button .item .remove {
+  color: inherit;
+  text-decoration: none;
+  vertical-align: middle;
+  display: inline-block;
+  padding: 2px 6px;
+  border-left: 1px solid #d0d0d0;
+  border-radius: 0 2px 2px 0;
+  box-sizing: border-box;
+  margin-left: 6px; }
+
+.ts-wrapper.plugin-remove_button .item .remove:hover {
+  background: rgba(0, 0, 0, 0.05); }
+
+.ts-wrapper.plugin-remove_button .item.active .remove {
+  border-left-color: #cacaca; }
+
+.ts-wrapper.plugin-remove_button.disabled .item .remove:hover {
+  background: none; }
+
+.ts-wrapper.plugin-remove_button.disabled .item .remove {
+  border-left-color: white; }
+
+.ts-wrapper.plugin-remove_button .remove-single {
+  position: absolute;
+  right: 0;
+  top: 0;
+  font-size: 23px; }
+
+.ts-wrapper {
+  position: relative; }
+
+.ts-dropdown,
+.ts-control,
+.ts-control input {
+  color: #303030;
+  font-family: inherit;
+  font-size: 13px;
+  line-height: 18px;
+  font-smoothing: inherit; }
+
+.ts-control,
+.ts-wrapper.single.input-active .ts-control {
+  background: #fff;
+  cursor: text; }
+
+.ts-control {
+  border: 1px solid #d0d0d0;
+  padding: 8px 8px;
+  width: 100%;
+  overflow: hidden;
+  position: relative;
+  z-index: 1;
+  box-sizing: border-box;
+  box-shadow: none;
+  border-radius: 3px;
+  display: flex;
+  flex-wrap: wrap; }
+  .ts-wrapper.multi.has-items .ts-control {
+    padding: calc( 8px - 2px - 0) 8px calc( 8px - 2px - 3px - 0); }
+  .full .ts-control {
+    background-color: #fff; }
+  .disabled .ts-control,
+  .disabled .ts-control * {
+    cursor: default !important; }
+  .focus .ts-control {
+    box-shadow: none; }
+  .ts-control > * {
+    vertical-align: baseline;
+    display: inline-block; }
+  .ts-wrapper.multi .ts-control > div {
+    cursor: pointer;
+    margin: 0 3px 3px 0;
+    padding: 2px 6px;
+    background: #f2f2f2;
+    color: #303030;
+    border: 0 solid #d0d0d0; }
+    .ts-wrapper.multi .ts-control > div.active {
+      background: #e8e8e8;
+      color: #303030;
+      border: 0 solid #cacaca; }
+  .ts-wrapper.multi.disabled .ts-control > div, .ts-wrapper.multi.disabled .ts-control > div.active {
+    color: #7d7c7c;
+    background: white;
+    border: 0 solid white; }
+  .ts-control > input {
+    flex: 1 1 auto;
+    min-width: 7rem;
+    display: inline-block !important;
+    padding: 0 !important;
+    min-height: 0 !important;
+    max-height: none !important;
+    max-width: 100% !important;
+    margin: 0 !important;
+    text-indent: 0 !important;
+    border: 0 none !important;
+    background: none !important;
+    line-height: inherit !important;
+    -webkit-user-select: auto !important;
+       -moz-user-select: auto !important;
+        -ms-user-select: auto !important;
+            user-select: auto !important;
+    box-shadow: none !important; }
+    .ts-control > input::-ms-clear {
+      display: none; }
+    .ts-control > input:focus {
+      outline: none !important; }
+  .has-items .ts-control > input {
+    margin: 0 4px !important; }
+  .ts-control.rtl {
+    text-align: right; }
+    .ts-control.rtl.single .ts-control:after {
+      left: 15px;
+      right: auto; }
+    .ts-control.rtl .ts-control > input {
+      margin: 0 4px 0 -2px !important; }
+  .disabled .ts-control {
+    opacity: 0.5;
+    background-color: #fafafa; }
+  .input-hidden .ts-control > input {
+    opacity: 0;
+    position: absolute;
+    left: -10000px; }
+
+.ts-dropdown {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  width: 100%;
+  z-index: 10;
+  border: 1px solid #d0d0d0;
+  background: #fff;
+  margin: 0.25rem 0 0 0;
+  border-top: 0 none;
+  box-sizing: border-box;
+  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+  border-radius: 0 0 3px 3px; }
+  .ts-dropdown [data-selectable] {
+    cursor: pointer;
+    overflow: hidden; }
+    .ts-dropdown [data-selectable] .highlight {
+      background: rgba(125, 168, 208, 0.2);
+      border-radius: 1px; }
+  .ts-dropdown .option,
+  .ts-dropdown .optgroup-header,
+  .ts-dropdown .no-results,
+  .ts-dropdown .create {
+    padding: 5px 8px; }
+  .ts-dropdown .option, .ts-dropdown [data-disabled], .ts-dropdown [data-disabled] [data-selectable].option {
+    cursor: inherit;
+    opacity: 0.5; }
+  .ts-dropdown [data-selectable].option {
+    opacity: 1;
+    cursor: pointer; }
+  .ts-dropdown .optgroup:first-child .optgroup-header {
+    border-top: 0 none; }
+  .ts-dropdown .optgroup-header {
+    color: #303030;
+    background: #fff;
+    cursor: default; }
+  .ts-dropdown .create:hover,
+  .ts-dropdown .option:hover,
+  .ts-dropdown .active {
+    background-color: #f5fafd;
+    color: #495c68; }
+    .ts-dropdown .create:hover.create,
+    .ts-dropdown .option:hover.create,
+    .ts-dropdown .active.create {
+      color: #495c68; }
+  .ts-dropdown .create {
+    color: rgba(48, 48, 48, 0.5); }
+  .ts-dropdown .spinner {
+    display: inline-block;
+    width: 30px;
+    height: 30px;
+    margin: 5px 8px; }
+    .ts-dropdown .spinner:after {
+      content: " ";
+      display: block;
+      width: 24px;
+      height: 24px;
+      margin: 3px;
+      border-radius: 50%;
+      border: 5px solid #d0d0d0;
+      border-color: #d0d0d0 transparent #d0d0d0 transparent;
+      animation: lds-dual-ring 1.2s linear infinite; }
+
+@keyframes lds-dual-ring {
+  0% {
+    transform: rotate(0deg); }
+  100% {
+    transform: rotate(360deg); } }
+
+.ts-dropdown-content {
+  overflow-y: auto;
+  overflow-x: hidden;
+  max-height: 200px;
+  overflow-scrolling: touch;
+  scroll-behavior: smooth; }
+
+.ts-hidden-accessible {
+  border: 0 !important;
+  clip: rect(0 0 0 0) !important;
+  -webkit-clip-path: inset(50%) !important;
+          clip-path: inset(50%) !important;
+  height: 1px !important;
+  overflow: hidden !important;
+  padding: 0 !important;
+  position: absolute !important;
+  width: 1px !important;
+  white-space: nowrap !important; }
+
+/*# sourceMappingURL=tom-select.css.map */

data/results/network_graph.html

@@ -0,0 +1,127 @@
+<html>
+    <head>
+        <meta charset="utf-8">
+        
+            <script src="lib/bindings/utils.js"></script>
+            <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/dist/vis-network.min.css" integrity="sha512-WgxfT5LWjfszlPHXRmBWHkV2eceiWTOBvrKCNbdgDYTHrT2AeLCGbF4sZlZw3UMN3WtL0tGUoIAKsu8mllg/XA==" crossorigin="anonymous" referrerpolicy="no-referrer" />
+            <script src="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/vis-network.min.js" integrity="sha512-LnvoEWDFrqGHlHmDD2101OrLcbsfkrzoSpvtSQtxK3RMnRV0eOkhhBN2dXHKRrUU8p2DGRTk35n4O8nWSVe1mQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
+            
+        
+<center>
+<h1></h1>
+</center>
+
+<!-- <link rel="stylesheet" href="../node_modules/vis/dist/vis.min.css" type="text/css" />
+<script type="text/javascript" src="../node_modules/vis/dist/vis.js"> </script>-->
+        <link
+          href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"
+          rel="stylesheet"
+          integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6"
+          crossorigin="anonymous"
+        />
+        <script
+          src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"
+          integrity="sha384-JEW9xMcG8R+pH31jmWH6WWP0WintQrMb4s7ZOdauHnUtxwoG2vI5DkLtS3qm9Ekf"
+          crossorigin="anonymous"
+        ></script>
+
+
+        <center>
+          <h1></h1>
+        </center>
+        <style type="text/css">
+
+             #mynetwork {
+                 width: 100%;
+                 height: 800px;
+                 background-color: #222;
+                 border: 1px solid lightgray;
+                 position: relative;
+                 float: left;
+             }
+
+             
+
+             
+
+             
+        </style>
+    </head>
+
+
+    <body>
+        <div class="card" style="width: 100%">
+            
+            
+            <div id="mynetwork" class="card-body"></div>
+        </div>
+
+        
+        
+
+        <script type="text/javascript">
+
+              // initialize global variables.
+              var edges;
+              var nodes;
+              var allNodes;
+              var allEdges;
+              var nodeColors;
+              var originalNodes;
+              var network;
+              var container;
+              var options, data;
+              var filter = {
+                  item : '',
+                  property : '',
+                  value : []
+              };
+
+              
+
+              
+
+              // This method is responsible for drawing the graph, returns the drawn network
+              function drawGraph() {
+                  var container = document.getElementById('mynetwork');
+
+                  
+
+                  // parsing and collecting nodes and edges from the python
+                  nodes = new vis.DataSet([{"color": "green", "font": {"color": "white"}, "id": "C0", "label": "Cand 0", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C1", "label": "Cand 1", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C2", "label": "Cand 2", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C3", "label": "Cand 3", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C4", "label": "Cand 4", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C5", "label": "Cand 5", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C6", "label": "Cand 6", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C7", "label": "Cand 7", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C8", "label": "Cand 8", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C9", "label": "Cand 9", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C10", "label": "Cand 10", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C11", "label": "Cand 11", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C12", "label": "Cand 12", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C13", "label": "Cand 13", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C14", "label": "Cand 14", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C15", "label": "Cand 15", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C16", "label": "Cand 16", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C17", "label": "Cand 17", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C18", "label": "Cand 18", "shape": "dot", "size": 25}, {"color": "green", "font": {"color": "white"}, "id": "C19", "label": "Cand 19", "shape": "dot", "size": 25}, {"color": "red", "font": {"color": "white"}, "id": "J9418", "label": "Anblicks", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J30989", "label": "iO Associates - US", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J9417", "label": "Anblicks", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J30990", "label": "iO Associates - US", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J9416", "label": "Anblicks", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J10140", "label": "Analytic Recruiting ", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J20529", "label": "Logikk", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J30163", "label": "Heliosz.AI", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J17134", "label": "BPO Recruit", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J28802", "label": "IntellectFaces, Inc", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J21756", "label": "maven", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J9454", "label": "Digital Prospectors", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J18817", "label": "Tekniforce", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J27159", "label": "Neva Recruiting", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J33214", "label": "micro1", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J26293", "label": "Ampstek", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J26294", "label": "Ampstek", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J26295", "label": "Ampstek", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J26296", "label": "Ampstek", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J27249", "label": "CivicMinds, Inc", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J23927", "label": "Swift Strategic Solu", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J237", "label": "National Security Ag", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J23926", "label": "Swift Strategic Solu", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J25745", "label": "AcctPositions", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J35525", "label": "Family Estate Planni", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J13170", "label": "Understanding Recrui", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J27248", "label": "CivicMinds, Inc", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J32967", "label": "Protechture, LLC", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J31028", "label": "GAC Solutions", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J31029", "label": "GAC Solutions", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J29286", "label": "Ram Mechanical Inc.", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J20702", "label": " EGN Consult \u0026 Recru", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J35720", "label": "Industrial Technical", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J29568", "label": "Rhino Tool House", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J15399", "label": "Professional Enginee", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J34106", "label": "Vedan Technologies", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J33708", "label": "Hire Python Develope", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J16166", "label": "Udemy", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J17102", "label": "OpenSesame", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J23925", "label": "Cephas Consultancy S", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J13500", "label": "HSI", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J10100", "label": "USM Business Systems", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J18151", "label": "SolidProfessor", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J18454", "label": "Google DeepMind", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J26422", "label": "Virtual Labs Inc.", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J20970", "label": "Lambda", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J10750", "label": "The Finders", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J34422", "label": "Cross Platform Devel", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J24818", "label": "Appzlogic ", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J13786", "label": "Datavail", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J16152", "label": "Infomatics Corp", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J16035", "label": "MongoDB", "shape": "dot", "size": 15}, {"color": "red", "font": {"color": "white"}, "id": "J9778", "label": "Pluralsight", "shape": "dot", "size": 15}]);
+                  edges = new vis.DataSet([{"from": "C0", "title": "0.703", "to": "J9418", "value": 7.028056979179382}, {"from": "C0", "title": "0.703", "to": "J30989", "value": 7.0262110233306885}, {"from": "C0", "title": "0.703", "to": "J9417", "value": 7.02572226524353}, {"from": "C0", "title": "0.702", "to": "J30990", "value": 7.019376754760742}, {"from": "C0", "title": "0.701", "to": "J9416", "value": 7.010321617126465}, {"from": "C1", "title": "0.620", "to": "J10140", "value": 6.20457649230957}, {"from": "C1", "title": "0.620", "to": "J20529", "value": 6.201968193054199}, {"from": "C1", "title": "0.589", "to": "J30163", "value": 5.894620418548584}, {"from": "C1", "title": "0.577", "to": "J17134", "value": 5.765535235404968}, {"from": "C1", "title": "0.576", "to": "J28802", "value": 5.755815505981445}, {"from": "C2", "title": "0.619", "to": "J21756", "value": 6.18832528591156}, {"from": "C2", "title": "0.617", "to": "J9454", "value": 6.172588467597961}, {"from": "C2", "title": "0.613", "to": "J18817", "value": 6.127530932426453}, {"from": "C2", "title": "0.608", "to": "J27159", "value": 6.082639694213867}, {"from": "C2", "title": "0.600", "to": "J33214", "value": 6.003042459487915}, {"from": "C3", "title": "0.589", "to": "J26293", "value": 5.888209939002991}, {"from": "C3", "title": "0.589", "to": "J26294", "value": 5.888209939002991}, {"from": "C3", "title": "0.589", "to": "J26295", "value": 5.888209939002991}, {"from": "C3", "title": "0.589", "to": "J26296", "value": 5.888209939002991}, {"from": "C3", "title": "0.534", "to": "J27249", "value": 5.344255566596985}, {"from": "C4", "title": "0.515", "to": "J23927", "value": 5.154691934585571}, {"from": "C4", "title": "0.515", "to": "J237", "value": 5.150743722915649}, {"from": "C4", "title": "0.514", "to": "J23926", "value": 5.135759115219116}, {"from": "C4", "title": "0.506", "to": "J25745", "value": 5.0571417808532715}, {"from": "C4", "title": "0.495", "to": "J35525", "value": 4.94664192199707}, {"from": "C5", "title": "0.624", "to": "J13170", "value": 6.2363749742507935}, {"from": "C5", "title": "0.590", "to": "J27249", "value": 5.904499888420105}, {"from": "C5", "title": "0.589", "to": "J27248", "value": 5.891878008842468}, {"from": "C5", "title": "0.587", "to": "J32967", "value": 5.873211622238159}, {"from": "C5", "title": "0.567", "to": "J18817", "value": 5.669974684715271}, {"from": "C6", "title": "0.528", "to": "J23927", "value": 5.282142162322998}, {"from": "C6", "title": "0.528", "to": "J23926", "value": 5.279108881950378}, {"from": "C6", "title": "0.504", "to": "J237", "value": 5.036947131156921}, {"from": "C6", "title": "0.501", "to": "J31028", "value": 5.008084774017334}, {"from": "C6", "title": "0.501", "to": "J31029", "value": 5.008084774017334}, {"from": "C7", "title": "0.541", "to": "J29286", "value": 5.409826636314392}, {"from": "C7", "title": "0.513", "to": "J20702", "value": 5.132828950881958}, {"from": "C7", "title": "0.511", "to": "J35720", "value": 5.112812519073486}, {"from": "C7", "title": "0.511", "to": "J29568", "value": 5.1084864139556885}, {"from": "C7", "title": "0.510", "to": "J15399", "value": 5.098806619644165}, {"from": "C8", "title": "0.523", "to": "J34106", "value": 5.231214761734009}, {"from": "C8", "title": "0.521", "to": "J9454", "value": 5.210273861885071}, {"from": "C8", "title": "0.517", "to": "J20529", "value": 5.1720041036605835}, {"from": "C8", "title": "0.516", "to": "J13170", "value": 5.163518190383911}, {"from": "C8", "title": "0.514", "to": "J28802", "value": 5.135617256164551}, {"from": "C9", "title": "0.608", "to": "J33708", "value": 6.077218055725098}, {"from": "C9", "title": "0.585", "to": "J9454", "value": 5.8520495891571045}, {"from": "C9", "title": "0.570", "to": "J30990", "value": 5.695323944091797}, {"from": "C9", "title": "0.569", "to": "J30989", "value": 5.691220760345459}, {"from": "C9", "title": "0.566", "to": "J13170", "value": 5.657694339752197}, {"from": "C10", "title": "0.586", "to": "J27159", "value": 5.86123526096344}, {"from": "C10", "title": "0.586", "to": "J16166", "value": 5.860676169395447}, {"from": "C10", "title": "0.577", "to": "J26293", "value": 5.769027471542358}, {"from": "C10", "title": "0.577", "to": "J26294", "value": 5.769027471542358}, {"from": "C10", "title": "0.577", "to": "J26295", "value": 5.769027471542358}, {"from": "C11", "title": "0.593", "to": "J9454", "value": 5.932710766792297}, {"from": "C11", "title": "0.582", "to": "J17102", "value": 5.818200707435608}, {"from": "C11", "title": "0.564", "to": "J23925", "value": 5.643590092658997}, {"from": "C11", "title": "0.559", "to": "J13500", "value": 5.586053133010864}, {"from": "C11", "title": "0.553", "to": "J26293", "value": 5.532607436180115}, {"from": "C12", "title": "0.629", "to": "J20529", "value": 6.294257044792175}, {"from": "C12", "title": "0.623", "to": "J30163", "value": 6.232224702835083}, {"from": "C12", "title": "0.623", "to": "J10140", "value": 6.227796673774719}, {"from": "C12", "title": "0.592", "to": "J28802", "value": 5.915536284446716}, {"from": "C12", "title": "0.587", "to": "J10100", "value": 5.873672366142273}, {"from": "C13", "title": "0.511", "to": "J18151", "value": 5.108709931373596}, {"from": "C13", "title": "0.509", "to": "J13170", "value": 5.0925469398498535}, {"from": "C13", "title": "0.507", "to": "J18454", "value": 5.072507858276367}, {"from": "C13", "title": "0.491", "to": "J26422", "value": 4.905799925327301}, {"from": "C13", "title": "0.489", "to": "J20970", "value": 4.894821345806122}, {"from": "C14", "title": "0.545", "to": "J10750", "value": 5.44858992099762}, {"from": "C14", "title": "0.530", "to": "J30989", "value": 5.3042155504226685}, {"from": "C14", "title": "0.529", "to": "J30990", "value": 5.2928584814071655}, {"from": "C14", "title": "0.526", "to": "J34422", "value": 5.2603888511657715}, {"from": "C14", "title": "0.508", "to": "J24818", "value": 5.0799620151519775}, {"from": "C15", "title": "0.595", "to": "J13786", "value": 5.954039692878723}, {"from": "C15", "title": "0.566", "to": "J16152", "value": 5.656808614730835}, {"from": "C15", "title": "0.565", "to": "J30989", "value": 5.64922571182251}, {"from": "C15", "title": "0.564", "to": "J30990", "value": 5.64068078994751}, {"from": "C15", "title": "0.561", "to": "J16035", "value": 5.613973140716553}, {"from": "C16", "title": "0.654", "to": "J9454", "value": 6.5383148193359375}, {"from": "C16", "title": "0.629", "to": "J30990", "value": 6.292245984077454}, {"from": "C16", "title": "0.628", "to": "J26293", "value": 6.2770432233810425}, {"from": "C16", "title": "0.628", "to": "J26294", "value": 6.2770432233810425}, {"from": "C16", "title": "0.628", "to": "J26295", "value": 6.2770432233810425}, {"from": "C17", "title": "0.594", "to": "J21756", "value": 5.936995148658752}, {"from": "C17", "title": "0.573", "to": "J20529", "value": 5.727492570877075}, {"from": "C17", "title": "0.569", "to": "J13170", "value": 5.693559050559998}, {"from": "C17", "title": "0.569", "to": "J33214", "value": 5.69225549697876}, {"from": "C17", "title": "0.567", "to": "J18817", "value": 5.669708847999573}, {"from": "C18", "title": "0.587", "to": "J10750", "value": 5.866515636444092}, {"from": "C18", "title": "0.578", "to": "J30989", "value": 5.7807475328445435}, {"from": "C18", "title": "0.577", "to": "J30990", "value": 5.771228671073914}, {"from": "C18", "title": "0.562", "to": "J34422", "value": 5.615531802177429}, {"from": "C18", "title": "0.549", "to": "J9454", "value": 5.493091940879822}, {"from": "C19", "title": "0.626", "to": "J9454", "value": 6.2596118450164795}, {"from": "C19", "title": "0.596", "to": "J18817", "value": 5.959857702255249}, {"from": "C19", "title": "0.578", "to": "J34106", "value": 5.777912735939026}, {"from": "C19", "title": "0.574", "to": "J13170", "value": 5.7445597648620605}, {"from": "C19", "title": "0.572", "to": "J9778", "value": 5.723878741264343}]);
+
+                  nodeColors = {};
+                  allNodes = nodes.get({ returnType: "Object" });
+                  for (nodeId in allNodes) {
+                    nodeColors[nodeId] = allNodes[nodeId].color;
+                  }
+                  allEdges = edges.get({ returnType: "Object" });
+                  // adding nodes and edges to the graph
+                  data = {nodes: nodes, edges: edges};
+
+                  var options = {"physics": {"enabled": true, "solver": "forceAtlas2Based"}};
+
+                  
+
+
+                  
+
+                  network = new vis.Network(container, data, options);
+
+                  
+
+                  
+
+                  
+
+
+                  
+
+                  return network;
+
+              }
+              drawGraph();
+        </script>
+    </body>
+</html>