Medical-research-skills academic-poster-generator

Source: https://github.com/aipoch/medical-research-skills

When to Use

• You have one or more research paper PDFs and need a conference-style poster draft with standard sections (Intro/Methods/Results/Conclusions).
• You want to automatically extract title/authors/abstract/sections from a PDF and restructure them into concise poster bullet points.
• You need a LaTeX poster scaffold using beamerposter, tikzposter, or baposter, with standard poster sizes (A0/A1/36×48").
• You must ensure posters are visual-first (at least 2–3 generated figures) and pass an automated quality gate before delivery.
• You want a browser-viewable final output (

  poster.rendered.html

  ) rather than a compiled PDF (PDF compilation is optional).

Key Features

• End-to-end pipeline: PDF → metadata extraction → content structuring → figure generation → LaTeX poster assembly → HTML base conversion → agent rendering → final HTML.
• Scripted PDF processing:
  • Convert PDF pages to images for reference.
  • Extract metadata (title, authors, abstract, section structure).
  • Structure content into poster-ready bullet points with word limits.
• Mandatory figure workflow:
  • Generate at least 2–3 figures (schematics/flowcharts/mechanisms/comparison charts).
  • Insert figures into LaTeX templates automatically or via config.
• Template support:

  • assets/templates/beamerposter-template.tex

  • assets/templates/tikzposter-template.tex

  • assets/templates/baposter-template.tex

• HTML-first delivery policy:
  • Final deliverables:

    poster.rendered.html

    +

    figures/

  • Intermediate artifacts are temporary and must not be returned.
• Quality control:
  • Automated checks for HTML/LaTeX outputs.
  • Manual checklist reference:

    references/poster_quality_checklist.md

• Design guidance:
  • Poster design principles:

    references/design_principles.md

Dependencies

Python (recommended)

• Python

  >=3.8

• pypdf

  (version not pinned)

• pdfplumber

  (version not pinned)

• pdf2image

  (version not pinned)

• pytesseract

  (version not pinned; required for OCR on scanned PDFs)

• pandas

  (version not pinned; optional for table handling)

• Pillow

  (version not pinned)

• matplotlib

  (version not pinned)

Install (example):

pip install pypdf pdfplumber pdf2image pytesseract pandas pillow matplotlib

System tools

• tesseract-ocr

  (for OCR; required only for scanned PDFs)
• Poppler utilities (commonly required by

  pdf2image

  , e.g.,

  pdftoppm

  )

LaTeX (optional, only if compiling PDF)

• TeX Live / MiKTeX / MacTeX
• TeX packages (via

  tlmgr

  , names may vary by distribution):

  • beamerposter

  • tikzposter

  • baposter

  • qrcode

  • xcolor

  • tcolorbox

  • subcaption

  • graphics

Example:

tlmgr install beamerposter tikzposter baposter qrcode xcolor tcolorbox subcaption graphics

HTML conversion

• pandoc

  (required for

  --html-only

  base HTML generation)

• pdf2htmlEX

  (optional; only if doing PDF → HTML rendering)

Example Usage

1) Run the end-to-end pipeline (recommended)

Creates a timestamped directory under

output/

and supports HTML-only mode.

python scripts/run_poster_pipeline.py paper.pdf --html-only

Expected final structure:

output/YYYYMMDD_HHMMSS/
├── poster.rendered.html
└── figures/
    ├── mechanism.png
    ├── process_flowchart.png
    └── comparison_chart.png

2) Run stages manually (fully reproducible)

Stage A — Extract metadata and structure content (temporary artifacts)

python scripts/extract_metadata.py paper.pdf metadata.json
python scripts/structure_content.py paper.pdf --json poster_content.json --latex content.tex --max-words 800
python scripts/convert_pdf_to_images.py paper.pdf paper_images/ --dpi 600

Stage B — Generate figures (mandatory)

At least 2–3 figures are required.

python scripts/generate_figures.py schematic "Cell signaling pathway" mechanism.png
python scripts/generate_figures.py flowchart "Step 1;Step 2;Step 3" process_flowchart.png
python scripts/generate_figures.py comparison '{"Control":[65],"Our Method":[87]}' comparison_chart.png

Or generate from a config:

python scripts/generate_figures.py config figures_config.json

Stage C — Create poster LaTeX from a template (temporary artifact)

cp assets/templates/beamerposter-template.tex poster.tex
# (Edit poster.tex: title/authors/institute + paste structured content)

Stage D — Insert figures into LaTeX (temporary artifact)

python scripts/insert_figures.py poster.tex --all
# or:
python scripts/insert_figures.py poster.tex --config figures_config.json

Stage E — Convert to base HTML and render final HTML (final artifact)

python scripts/convert_poster.py poster.tex --html-only
# Agent step: read poster.html, validate <img> paths, inject CSS/layout, output poster.rendered.html

Stage F — Quality control (mandatory)

python scripts/check_poster_quality.py poster.rendered.html

Implementation Details

Pipeline and file policy

All generated files must be placed under

output/

(preferably a timestamped subdirectory). Only the following are considered final deliverables:

• poster.rendered.html

• figures/

  directory (PNG figures)

All other files are intermediate/temporary and must not be returned to the user, including (non-exhaustive):

• poster.tex

  ,

  poster.html

• metadata.json

  ,

  poster_content.json

  ,

  figures_config.json

  ,

  figures.json

• LaTeX auxiliary files (

  .aux

  ,

  .log

  ,

  .out

  , etc.)

Conceptual flow:

PDF → metadata extraction (temp) → content structuring (temp) → figure generation (final figures/)
→ LaTeX poster (temp) → base HTML (temp) → agent rendering → poster.rendered.html (final)

PDF extraction approach

• Text and tables are extracted via

  pdfplumber

  .
• For scanned PDFs, OCR is performed by converting pages to images (

  pdf2image

  ) and running

  pytesseract

  .

Typical extraction targets:

• Title/authors (first-page patterns)
• Abstract (from “Abstract” header to next section)
• Section blocks (Introduction/Methods/Results/Conclusion)
• Tables (converted to summaries for poster bullets)

Content structuring rules

• Convert paragraphs to bullet points suitable for poster blocks.
• Enforce a poster-friendly word budget (commonly 600–800 words total).
• Prefer 3–6 key visuals; summarize tables into a small set of metrics.

Figure requirements (mandatory)

• Every poster must include at least 2–3 generated figures.
• Target 40–50% of poster area as visual content.
• Figures should be ≥300 DPI, with clear labels and concise captions.

Supported figure categories:

• Schematics (systems, pathways, conceptual diagrams)
• Flowcharts (procedures, pipelines, algorithms)
• Mechanism diagrams (biological/chemical processes)
• Comparison charts (bar charts, benchmarks)

LaTeX package selection

Use one of the supported poster packages depending on style needs:

• beamerposter (traditional academic)
• tikzposter (modern, colorful)
• baposter (multi-column layouts)

Templates are provided in

assets/templates/

.

HTML rendering requirements (agent step)

After

pandoc

produces

poster.html

, the renderer must:

1. Verify all

   <img>

   references exist and paths resolve to

   figures/

   .
2. Apply poster-grade CSS (grid columns, typography, spacing, captions).
3. Ensure accessibility: WCAG AA contrast ≥ 4.5:1.
4. Output

   poster.rendered.html

   as the only final HTML artifact.

Minimum layout expectations:

• 2–3 column grid on desktop; single column on narrow screens.
• Clear hierarchy: title/authors/institution, section headers, bullet lists, figure blocks with captions.

Quality control requirements (mandatory)

Run

scripts/check_poster_quality.py

on the final output:

• Validate that images exist and render correctly.
• Confirm layout integrity (columns/blocks).
• Ensure readability (font sizes, spacing).
• Confirm contrast compliance (≥ 4.5:1).
• Confirm figure count (≥ 2–3) and that no placeholder text remains.

References:

• Design principles:

  references/design_principles.md

• Manual checklist:

  references/poster_quality_checklist.md