#!/usr/bin/env python3 """ Generate enhanced HTML website from README.md table """ import pandas as pd import re import os def to_link_if_markdown(cell_text: str) -> str: """Convert markdown links [text](url) to HTML tags""" if not isinstance(cell_text, str): return str(cell_text) cell_text = re.sub(r'\[(.*?)\]\((.*?)\)', r'\1', cell_text) return cell_text.strip() def extract_table_from_readme(readme_path: str) -> pd.DataFrame: """Extract the main projects table from README.md""" with open(readme_path, "r", encoding='utf-8') as f: text = f.readlines() table = [] in_projects_section = False for line in text: # Check if we're in the Projects section if line.strip() == "### Projects": in_projects_section = True continue # Stop if we hit another section if in_projects_section and line.startswith("### ") and "Projects" not in line: break # Extract table rows (has 8 | characters for 7 columns) if in_projects_section and len(re.findall(r"\|", line)) == 8: row = [cell.strip() for cell in line.split("|")[1:-1]] table.append(row) if len(table) < 2: raise ValueError("Could not find valid table in README.md") # First row is header, second row is separator, rest is data header = table[0] data = table[2:] if len(table) > 2 else [] df = pd.DataFrame(data, columns=header) # Apply markdown to HTML conversion df = df.applymap(to_link_if_markdown) return df def get_enhanced_html_template() -> str: """Return the enhanced HTML template""" return ''' Awesome Computational Primatology

🐒 Awesome Computational Primatology

A curated list of machine learning research for non-human primatology

{total_papers} Papers

{years_span} Years

{with_code} With Code

{with_data} With Data

🏷️ Topic Legend

PD Primate Detection

BPE Body Pose Estimation

FD Face Detection

FLE Facial Landmark Estimation

FR Face Recognition

FAC Facial Action Coding

BR Behavior Recognition

AM Avatar/Mesh

SI Species Identification

RL Reinforcement Learning

{table_html}

''' def calculate_stats(df: pd.DataFrame) -> dict: """Calculate statistics from the dataframe""" total_papers = len(df) # Count years years = set() for year in df.iloc[:, 0]: # First column is year if str(year).isdigit(): years.add(int(year)) years_span = len(years) # Count papers with code (Model column) with_code = 0 model_col = df.iloc[:, 4] if len(df.columns) > 4 else pd.Series() # Model column for value in model_col: if isinstance(value, str) and ('Yes' in value or 'Code only' in value): with_code += 1 # Count papers with data (Data column) with_data = 0 data_col = df.iloc[:, 5] if len(df.columns) > 5 else pd.Series() # Data column for value in data_col: if isinstance(value, str) and 'Yes' in value: with_data += 1 return { 'total_papers': total_papers, 'years_span': years_span, 'with_code': with_code, 'with_data': with_data } def main(): """Main function to generate the website""" # Determine if running in GitHub Actions or locally if os.path.exists("/home/runner/work"): # GitHub Actions base_path = "/home/runner/work/awesome-computational-primatology/awesome-computational-primatology" else: # Local development - go up 2 levels from .github/workflows/ script_dir = os.path.dirname(os.path.abspath(__file__)) # .github/workflows/ base_path = os.path.dirname(os.path.dirname(script_dir)) # repository root readme_path = os.path.join(base_path, "README.md") output_path = os.path.join(base_path, "index.html") print(f"Script location: {os.path.abspath(__file__)}") print(f"Base path: {base_path}") print(f"README path: {readme_path}") print(f"Output path: {output_path}") try: # Extract table from README df = extract_table_from_readme(readme_path) print(f"Extracted table with {len(df)} rows and {len(df.columns)} columns") # Calculate statistics stats = calculate_stats(df) print(f"Statistics: {stats}") # Generate table HTML table_html = df.to_html( table_id="table", escape=False, index=False, classes="display" ) # Get template and format with data template = get_enhanced_html_template() html_content = template.replace('{table_html}', table_html) html_content = html_content.replace('{total_papers}', str(stats['total_papers'])) html_content = html_content.replace('{years_span}', str(stats['years_span'])) html_content = html_content.replace('{with_code}', str(stats['with_code'])) html_content = html_content.replace('{with_data}', str(stats['with_data'])) # Write output with open(output_path, "w", encoding='utf-8') as f: f.write(html_content) print(f"✅ Generated enhanced website: {output_path}") except Exception as e: print(f"❌ Error generating website: {e}") raise if __name__ == "__main__": main()