(results_list)
| 106 | return [r for r in results if r['stars'] >= 1000] |
| 107 | |
| 108 | def build_table(results_list): |
| 109 | |
| 110 | def build_html_fields(d): |
| 111 | return ['<a href="%s">%s</a>' % (d['url'], d['title'].split('/')[-1]), d['stars_unparsed'], d['desc']] |
| 112 | |
| 113 | def build_md_fields(d): |
| 114 | return ['[%s](%s)' % (d['title'].split('/')[-1], d['url']), d['stars_unparsed'], d['desc']] |
| 115 | |
| 116 | html = '<table><thead><tr><td>Project Name</td><td>Stars</td><td>Description</td></tr></thead>' |
| 117 | md = '| Project Name | Stars | Description |\n| ------- | ------ | ------ |\n' |
| 118 | for r in results_list: |
| 119 | html += '<tr><td>' + '</td><td>'.join(build_html_fields(r)) + '</td></tr>' |
| 120 | md += '|' + '|'.join(build_md_fields(r)) + '|\n' |
| 121 | html += '</table>' |
| 122 | return html, md |
| 123 | |
| 124 | topics = get_topic(['tensorflow', 'deep-learning', 'pytorch', 'machine-learning'], n_pages=15) |
| 125 | searches = search(['tensorflow', 'deep learning', 'pytorch', 'cntk', 'machine learning'], n_pages=15) |
no test coverage detected