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NoraNotDoraNoraNotDora
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fix: fixed paper manager
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‎.github/workflows/deploy.yml‎

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- name: Render README and docs from database
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run: python view/render_from_db.py
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- name: Export database to static admin JSON
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run: python scripts/export_admin_json.py
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- name: Build MkDocs site
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run: mkdocs build
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‎docs/admin/data.json‎

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‎docs/admin/index.html‎

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‎docs/admin/index.md‎

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# Admin Interface
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<div class="admonition info" markdown="1">
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The **Admin Interface** is a local web application that runs on your own machine. It cannot be hosted on GitHub Pages because it requires a live database and Python backend.
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</div>
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## What it does
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The admin panel (served by Flask at `http://localhost:5000/admin`) provides:
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- **Browse & search** all 196+ papers, datasets, and methods
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- **Inline editing** — click any cell to edit it directly in the table
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- **Add new entries** with arXiv auto-fill (paste an arXiv URL to auto-populate metadata)
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- **Multi-select export** — select papers and export as CSV or copy links for NotebookLM
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- **Sync & build** — export DB → YAML/CSV, or rebuild the static documentation site
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## How to run it locally
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```bash
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# 1. Clone the repository
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git clone https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution.git
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cd Awesome-Issue-Resolution
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# 2. Install dependencies
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pip install -r requirements.txt
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# 3. Start the admin server
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# (automatically refreshes news, re-renders docs, and builds the site)
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python start.py
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```
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Then open **[http://localhost:5000/admin](http://localhost:5000/admin)** in your browser.
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## Quick reference
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| Command | Description |
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|---------|-------------|
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| `python start.py` | Full update + start server (port 5000) |
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| `python start.py --init` | Re-import from YAML/CSV, then full update + start |
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| `python start.py --no-update` | Start server immediately without update steps |
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| `python start.py --news` | Refresh "This Month's Papers" only |
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| `python start.py --render` | Re-render README/docs from DB only |
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| `python start.py --build` | Build static site only |
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## Looking for the paper database?
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Use the **[Tables & Resources](../tables/)** page to browse statistical tables, or the **[Full Paper List](../#-complete-paper-list)** in the main page.
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To search and export papers interactively, run the admin locally as described above.

‎mkdocs.yml‎

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images/README.md
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**/README.md
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# Copy admin HTML and generated JSON as-is (not processed as markdown)
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hooks: []
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plugins:
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- search
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- Home: index.md
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- Paper: paper.md
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- Tables & Resources: tables.md
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- Admin: admin/index.md
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- About: about.md
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- Cite: cite.md

‎scripts/export_admin_json.py‎

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#!/usr/bin/env python3
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"""
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Export the database to docs/admin/data.json for the static admin page.
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Run automatically during CI before mkdocs build.
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Usage:
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python scripts/export_admin_json.py
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"""
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import sys
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import json
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import os
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from pathlib import Path
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from datetime import date
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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if sys.platform == 'win32':
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os.system('chcp 65001 >nul 2>&1')
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sys.stdout.reconfigure(encoding='utf-8')
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from models import Paper, Dataset, TrainingDataset, SFTMethod, RLMethod, FoundationModel, get_session
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OUT_PATH = ROOT / 'docs' / 'admin' / 'data.json'
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def export():
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session = get_session()
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papers = []
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for p in session.query(Paper).order_by(Paper.month.desc().nullslast(), Paper.id.desc()).all():
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links = {}
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if p.arxiv_link: links['arxiv'] = p.arxiv_link
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if p.github_link: links['github'] = p.github_link
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if p.huggingface_link: links['huggingface'] = p.huggingface_link
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if p.openreview_link: links['openreview'] = p.openreview_link
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if p.website_link: links['website'] = p.website_link
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if p.doi_link: links['doi'] = p.doi_link
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papers.append({
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'id': p.id,
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'short_name': p.short_name or '',
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'title': p.title or '',
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'authors': p.authors or '',
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'venue': p.venue or '',
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'month': p.month or '',
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'category': p.category or '',
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'links': links,
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})
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datasets = []
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for d in session.query(Dataset).order_by(Dataset.name).all():
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links = {}
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if d.github_link: links['github'] = d.github_link
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if d.huggingface_link: links['huggingface'] = d.huggingface_link
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if d.website_link: links['website'] = d.website_link
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datasets.append({
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'id': d.id,
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'name': d.name or '',
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'language': d.language or '',
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'multimodal': d.multimodal or '',
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'environment': d.environment or '',
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'repos': d.repos or '',
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'amount': d.amount or '',
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'category': d.category or '',
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'links': links,
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})
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training = []
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for t in session.query(TrainingDataset).order_by(TrainingDataset.name).all():
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links = {}
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if t.github_link: links['github'] = t.github_link
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if t.huggingface_link: links['huggingface'] = t.huggingface_link
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training.append({
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'id': t.id,
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'name': t.name or '',
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'language': t.language or '',
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'repos': t.repos or '',
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'amount': t.amount or '',
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'links': links,
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})
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sft = []
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for m in session.query(SFTMethod).order_by(SFTMethod.resolution_percent.desc().nullslast()).all():
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links = {}
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if m.code_link: links['github'] = m.code_link
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if m.model_link: links['huggingface'] = m.model_link
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sft.append({
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'id': m.id,
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'model_name': m.model_name or '',
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'base_model': m.base_model or '',
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'training_scaffold': m.training_scaffold or '',
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'resolution_percent': str(m.resolution_percent) if m.resolution_percent is not None else '',
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'links': links,
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})
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rl = []
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for m in session.query(RLMethod).order_by(RLMethod.resolution_percent.desc().nullslast()).all():
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links = {}
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if m.code_link: links['github'] = m.code_link
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if m.model_link: links['huggingface'] = m.model_link
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rl.append({
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'id': m.id,
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'model_name': m.model_name or '',
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'size': m.size or '',
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'architecture': m.architecture or '',
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'training_scaffold': m.training_scaffold or '',
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'reward_type': m.reward_type or '',
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'resolution_percent': str(m.resolution_percent) if m.resolution_percent is not None else '',
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'links': links,
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})
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foundation = []
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for m in session.query(FoundationModel).order_by(FoundationModel.resolution_percent.desc().nullslast()).all():
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links = {}
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if m.code_link: links['github'] = m.code_link
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if m.model_link: links['huggingface'] = m.model_link
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foundation.append({
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'id': m.id,
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'model_name': m.model_name or '',
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'size': m.size or '',
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'architecture': m.architecture or '',
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'inference_scaffold': m.inference_scaffold or '',
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'reward_type': m.reward_type or '',
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'resolution_percent': str(m.resolution_percent) if m.resolution_percent is not None else '',
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'links': links,
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})
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session.close()
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# Category counts from papers
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from collections import Counter
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DATA_CATS = {'evaluation_datasets','training_datasets','data_collection','data_synthesis'}
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METHODS_CATS = {'sft','rl','single_agent','multi_agent','tool','workflow','memory','inference_scaling'}
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ANALYSIS_CATS = {'data_analysis','methods_analysis'}
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def count_cat(cat_set):
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return sum(1 for p in papers if {t.strip() for t in p['category'].split(',') if t.strip()} & cat_set)
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data = {
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'generated': date.today().isoformat(),
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'stats': {
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'total': len(papers),
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'data': count_cat(DATA_CATS),
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'methods': count_cat(METHODS_CATS),
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'analysis': count_cat(ANALYSIS_CATS),
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},
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'papers': papers,
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'datasets': datasets,
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'training_datasets': training,
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'sft_methods': sft,
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'rl_methods': rl,
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'foundation_models': foundation,
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}
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OUT_PATH.parent.mkdir(parents=True, exist_ok=True)
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OUT_PATH.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding='utf-8')
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print(f'[OK] Exported {len(papers)} papers, {len(datasets)} datasets, '
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f'{len(sft)+len(rl)} methods → {OUT_PATH.relative_to(ROOT)}')
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if __name__ == '__main__':
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export()

‎site/404.html‎

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<li class="md-tabs__item">
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<a href="/Awesome-Issue-Resolution/admin/" class="md-tabs__link">
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Admin
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</a>
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</li>
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<a href="/Awesome-Issue-Resolution/about/" class="md-tabs__link">
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<li class="md-nav__item">
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<a href="/Awesome-Issue-Resolution/admin/" class="md-nav__link">
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<span class="md-ellipsis">
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Admin
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</span>
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</a>
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</li>
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<a href="/Awesome-Issue-Resolution/about/" class="md-nav__link">
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‎site/about/index.html‎

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<link rel="canonical" href="https://DeepSoftwareAnalytics.github.io/Awesome-Issue-Resolution/about/">
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<link rel="prev" href="../tables/">
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<link rel="prev" href="../admin/">
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<link rel="next" href="../cite/">
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<li class="md-tabs__item">
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<a href="../admin/" class="md-tabs__link">
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Admin
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<li class="md-nav__item">
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<span class="md-ellipsis">
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Admin
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</a>
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