437 lines
10 KiB
Markdown
437 lines
10 KiB
Markdown
# Usage Examples and Workflows
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## Complete Workflow Examples
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### Example 1: Conference Presentation Package
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**Scenario**: Preparing for a major conference presentation with website, poster, and video.
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**User Request**: "I need to create a complete presentation package for my NeurIPS paper submission. Generate a website, poster, and video presentation."
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**Workflow**:
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```bash
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# Step 1: Organize paper files
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mkdir -p input/neurips2025_paper
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cp main.tex input/neurips2025_paper/
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cp -r figures/ input/neurips2025_paper/
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cp -r tables/ input/neurips2025_paper/
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cp bibliography.bib input/neurips2025_paper/
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# Step 2: Generate all components
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python pipeline_all.py \
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--input-dir input/neurips2025_paper \
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--output-dir output/ \
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--model-choice 1 \
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--generate-website \
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--generate-poster \
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--generate-video \
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--poster-width-inches 48 \
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--poster-height-inches 36 \
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--enable-logo-search
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# Step 3: Review outputs
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ls -R output/neurips2025_paper/
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# - website/index.html
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# - poster/poster_final.pdf
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# - video/final_video.mp4
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```
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**Output**:
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- Interactive website showcasing research
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- 4'×3' conference poster (print-ready)
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- 12-minute presentation video
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- Processing time: ~45 minutes (without talking-head)
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---
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### Example 2: Quick Website for Preprint
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**Scenario**: Creating an explorable homepage for a bioRxiv preprint.
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**User Request**: "Convert my genomics preprint to an interactive website to accompany the bioRxiv submission."
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**Workflow**:
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```bash
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# Using PDF input (LaTeX not available)
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python pipeline_all.py \
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--input-dir papers/genomics_preprint/ \
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--output-dir output/genomics_web/ \
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--model-choice 1 \
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--generate-website
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# Deploy to GitHub Pages or personal server
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cd output/genomics_web/website/
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# Add link to bioRxiv paper, data repositories, code
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# Upload to hosting service
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```
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**Tips**:
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- Include links to bioRxiv DOI
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- Add GitHub repository links
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- Include data availability section
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- Embed interactive visualizations if possible
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---
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### Example 3: Video Abstract for Journal Submission
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**Scenario**: Creating a video abstract for a journal that encourages multimedia submissions.
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**User Request**: "Generate a 5-minute video abstract for my Nature Communications submission."
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**Workflow**:
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```bash
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# Generate concise video focusing on key findings
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python pipeline_light.py \
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--model_name_t gpt-4.1 \
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--model_name_v gpt-4.1 \
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--result_dir output/video_abstract/ \
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--paper_latex_root papers/nature_comms/ \
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--video-duration 300 \
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--slides-per-minute 3
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# Optional: Add custom intro/outro slides
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# Optional: Include talking-head for introduction
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```
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**Output**:
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- 5-minute video abstract
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- Focus on visual results
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- Clear, accessible narration
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- Journal-ready format
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---
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### Example 4: Multi-Paper Website Generation
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**Scenario**: Creating websites for multiple papers from a research group.
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**User Request**: "Generate websites for all 5 papers our lab published this year."
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**Workflow**:
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```bash
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# Organize papers
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mkdir -p batch_input/
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# Create subdirectories: paper1/, paper2/, paper3/, paper4/, paper5/
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# Each with their LaTeX sources
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# Batch process
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python pipeline_all.py \
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--input-dir batch_input/ \
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--output-dir batch_output/ \
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--model-choice 1 \
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--generate-website \
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--enable-logo-search
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# Creates:
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# batch_output/paper1/website/
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# batch_output/paper2/website/
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# batch_output/paper3/website/
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# batch_output/paper4/website/
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# batch_output/paper5/website/
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```
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**Best Practice**:
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- Use consistent naming conventions
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- Process overnight for large batches
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- Review each website for accuracy
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- Deploy to unified lab website
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---
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### Example 5: Poster for Virtual Conference
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**Scenario**: Creating a digital poster for a virtual conference with interactive elements.
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**User Request**: "Create a poster for the virtual ISMB conference with clickable links to code and data."
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**Workflow**:
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```bash
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# Generate poster with QR codes and links
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python pipeline_all.py \
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--input-dir papers/ismb_submission/ \
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--output-dir output/ismb_poster/ \
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--model-choice 1 \
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--generate-poster \
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--poster-width-inches 48 \
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--poster-height-inches 36 \
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--enable-qr-codes
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# Manually add QR codes to:
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# - GitHub repository
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# - Interactive results dashboard
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# - Supplementary data
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# - Video presentation
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```
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**Digital Enhancements**:
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- PDF with embedded hyperlinks
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- High-resolution PNG for virtual platform
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- Separate PDF with video links for download
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---
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### Example 6: Promotional Video Clip
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**Scenario**: Creating a short promotional video for social media.
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**User Request**: "Generate a 2-minute highlight video of our Cell paper for Twitter."
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**Workflow**:
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```bash
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# Generate short, engaging video
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python pipeline_light.py \
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--model_name_t gpt-4.1 \
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--model_name_v gpt-4.1 \
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--result_dir output/promo_video/ \
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--paper_latex_root papers/cell_paper/ \
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--video-duration 120 \
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--presentation-style public
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# Post-process:
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# - Extract key 30-second clip for Twitter
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# - Add captions for sound-off viewing
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# - Optimize file size for social media
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```
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**Social Media Optimization**:
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- Square format (1:1) for Instagram
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- Horizontal format (16:9) for Twitter/LinkedIn
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- Vertical format (9:16) for TikTok/Stories
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- Add text overlays for key findings
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---
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## Common Use Case Patterns
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### Pattern 1: LaTeX Paper → Full Package
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**Input**: LaTeX source with all assets
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**Output**: Website + Poster + Video
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**Time**: 45-90 minutes
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**Best for**: Major publications, conference presentations
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```bash
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python pipeline_all.py \
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--input-dir [latex_dir] \
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--output-dir [output_dir] \
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--model-choice 1 \
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--generate-website \
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--generate-poster \
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--generate-video
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```
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---
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### Pattern 2: PDF → Interactive Website
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**Input**: Published PDF paper
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**Output**: Explorable website
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**Time**: 15-30 minutes
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**Best for**: Post-publication promotion, preprint enhancement
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```bash
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python pipeline_all.py \
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--input-dir [pdf_dir] \
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--output-dir [output_dir] \
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--model-choice 1 \
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--generate-website
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```
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---
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### Pattern 3: LaTeX → Conference Poster
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**Input**: LaTeX paper
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**Output**: Print-ready poster (custom size)
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**Time**: 10-20 minutes
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**Best for**: Conference poster sessions
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```bash
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python pipeline_all.py \
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--input-dir [latex_dir] \
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--output-dir [output_dir] \
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--model-choice 1 \
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--generate-poster \
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--poster-width-inches [width] \
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--poster-height-inches [height]
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```
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---
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### Pattern 4: LaTeX → Presentation Video
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**Input**: LaTeX paper
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**Output**: Narrated presentation video
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**Time**: 20-60 minutes (without talking-head)
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**Best for**: Video abstracts, online presentations, course materials
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```bash
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python pipeline_light.py \
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--model_name_t gpt-4.1 \
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--model_name_v gpt-4.1 \
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--result_dir [output_dir] \
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--paper_latex_root [latex_dir]
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```
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---
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## Platform-Specific Outputs
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### Twitter/X Promotional Content
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The system auto-detects Twitter targeting for numeric folder names:
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```bash
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# Create Twitter-optimized content
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mkdir -p input/001_twitter_post/
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# System generates English promotional content
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```
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**Generated Output**:
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- Short, engaging summary
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- Key figure highlights
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- Hashtag recommendations
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- Thread-ready format
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---
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### Xiaohongshu (小红书) Content
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For Chinese social media, use alphanumeric folder names:
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```bash
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# Create Xiaohongshu-optimized content
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mkdir -p input/xhs_genomics/
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# System generates Chinese promotional content
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```
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**Generated Output**:
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- Chinese language content
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- Platform-appropriate formatting
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- Visual-first presentation
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- Engagement optimizations
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---
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## Troubleshooting Common Scenarios
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### Scenario: Large Paper (>50 pages)
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**Challenge**: Processing time and content selection
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**Solution**:
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```bash
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# Option 1: Focus on key sections
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# Edit LaTeX to comment out less critical sections
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# Option 2: Process in parts
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# Generate website for overview
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# Generate separate detailed videos for methods/results
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# Option 3: Use faster model for initial pass
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# Review and regenerate critical components with better model
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```
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---
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### Scenario: Complex Mathematical Content
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**Challenge**: Equations may not render perfectly
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**Solution**:
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- Use LaTeX input (not PDF) for best equation handling
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- Review generated content for equation accuracy
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- Manually adjust complex equations if needed
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- Consider using figure screenshots for critical equations
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---
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### Scenario: Non-Standard Paper Structure
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**Challenge**: Paper doesn't follow standard IMRAD format
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**Solution**:
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- Provide custom section guidance in paper metadata
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- Review generated structure and adjust
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- Use more powerful model (GPT-4.1) for better adaptation
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- Consider manual section annotation in LaTeX comments
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---
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### Scenario: Limited API Budget
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**Challenge**: Reducing costs while maintaining quality
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**Solution**:
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```bash
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# Use GPT-3.5-turbo for simple papers
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python pipeline_all.py \
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--input-dir [paper_dir] \
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--output-dir [output_dir] \
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--model-choice 3
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# Generate only needed components
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# Website-only (cheapest)
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# Poster-only (moderate)
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# Video without talking-head (moderate)
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```
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---
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### Scenario: Tight Deadline
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**Challenge**: Need outputs quickly
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**Solution**:
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```bash
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# Parallel processing if multiple papers
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# Use faster models (GPT-3.5-turbo)
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# Generate only essential component first
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# Skip optional features (logo search, talking-head)
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python pipeline_light.py \
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--model_name_t gpt-3.5-turbo \
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--model_name_v gpt-3.5-turbo \
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--result_dir [output_dir] \
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--paper_latex_root [latex_dir]
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```
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**Priority Order**:
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1. Website (fastest, most versatile)
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2. Poster (moderate speed, print deadline)
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3. Video (slowest, can be generated later)
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---
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## Quality Optimization Tips
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### For Best Website Results
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1. Use LaTeX input with all assets
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2. Include high-resolution figures
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3. Ensure paper has clear section structure
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4. Enable logo search for professional appearance
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5. Review and test all interactive elements
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### For Best Poster Results
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1. Provide high-resolution figures (300+ DPI)
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2. Specify exact poster dimensions needed
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3. Include institution branding information
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4. Use professional color scheme
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5. Test print small preview before full poster
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### For Best Video Results
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1. Use LaTeX for clearest content extraction
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2. Specify target duration appropriately
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3. Review script before video generation
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4. Choose appropriate presentation style
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5. Test audio quality and pacing
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### For Best Overall Results
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1. Start with clean, well-organized LaTeX source
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2. Use GPT-4 or GPT-4.1 for highest quality
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3. Review all outputs before finalizing
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4. Iterate on any component that needs adjustment
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5. Combine components for cohesive presentation package
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