create-llms-txt / app.py
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import gradio as gr
import requests
from bs4 import BeautifulSoup
import re
from urllib.parse import urljoin, urlparse
import asyncio
from collections import defaultdict
import unicodedata
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class WebsiteCrawler:
def __init__(self, max_depth=3, max_pages=50):
self.max_depth = max_depth
self.max_pages = max_pages
self.visited_urls = set()
self.url_metadata = defaultdict(dict)
self.headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}
def clean_text(self, text, is_title=False):
"""Clean and normalize text"""
if not text:
return ""
# Normalize unicode characters
text = unicodedata.normalize('NFKD', text)
text = re.sub(r'[^\x00-\x7F]+', '', text)
if is_title:
# Remove common suffixes and fragments for titles
text = re.sub(r'\s*[\|\-#:•].*', '', text)
text = re.sub(r'^\s*Welcome to\s+', '', text)
text = text.replace('docusaurus_skipToContent_fallback', '')
return ' '.join(text.split()).strip()
async def crawl_page(self, url, depth, base_domain):
"""Crawl a single page and extract information"""
if depth > self.max_depth or url in self.visited_urls or len(self.visited_urls) >= self.max_pages:
return []
try:
response = requests.get(url, headers=self.headers, timeout=10)
response.encoding = 'utf-8'
self.visited_urls.add(url)
soup = BeautifulSoup(response.text, 'html.parser')
# Extract title with fallbacks
title = None
meta_title = soup.find('meta', property='og:title')
if meta_title and meta_title.get('content'):
title = meta_title['content']
if not title:
title_tag = soup.find('title')
if title_tag:
title = title_tag.text
if not title:
h1_tag = soup.find('h1')
if h1_tag:
title = h1_tag.text
if not title:
title = url.split('/')[-1]
title = self.clean_text(title, is_title=True)
# Extract description with fallbacks
desc = None
meta_desc = soup.find('meta', {'name': 'description'})
if meta_desc and meta_desc.get('content'):
desc = meta_desc['content']
if not desc:
og_desc = soup.find('meta', property='og:description')
if og_desc and og_desc.get('content'):
desc = og_desc['content']
if not desc:
first_p = soup.find('p')
if first_p:
desc = first_p.text
desc = self.clean_text(desc) if desc else ""
# Determine category and importance
url_lower = url.lower()
category = 'Optional'
importance = 0
if 'docs' in url_lower or 'documentation' in url_lower:
category = 'Docs'
importance = 5
elif 'api' in url_lower:
category = 'API'
importance = 4
elif 'guide' in url_lower or 'tutorial' in url_lower:
category = 'Guides'
importance = 3
elif 'example' in url_lower:
category = 'Examples'
importance = 2
elif 'blog' in url_lower:
category = 'Blog'
importance = 1
# Store metadata
clean_url = re.sub(r'#.*', '', url).rstrip('/')
if title and len(title.strip()) > 0: # Only store if we have a valid title
self.url_metadata[clean_url] = {
'title': title,
'description': desc,
'category': category,
'importance': importance
}
# Find links
links = []
for a in soup.find_all('a', href=True):
href = a['href']
if not any(x in href.lower() for x in ['javascript:', 'mailto:', '.pdf', '.jpg', '.png', '.gif']):
next_url = urljoin(url, href)
if urlparse(next_url).netloc == base_domain:
links.append(next_url)
return links
except Exception as e:
logger.error(f"Error crawling {url}: {str(e)}")
return []
async def crawl_website(self, start_url):
"""Crawl website starting from the given URL"""
base_domain = urlparse(start_url).netloc
queue = [(start_url, 0)]
seen = {start_url}
while queue and len(self.visited_urls) < self.max_pages:
current_url, depth = queue.pop(0)
if depth > self.max_depth:
continue
links = await self.crawl_page(current_url, depth, base_domain)
for link in links:
if link not in seen and urlparse(link).netloc == base_domain:
seen.add(link)
queue.append((link, depth + 1))
def generate_llms_txt(self):
"""Generate llms.txt content"""
if not self.url_metadata:
return "No content was found to generate llms.txt"
# Sort and filter URLs
sorted_urls = sorted(
self.url_metadata.items(),
key=lambda x: (x[1]['importance'], x[0]),
reverse=True
)
# Generate content
content = []
main_metadata = sorted_urls[0][1]
content.append(f"# {main_metadata['title']}")
if main_metadata['description']:
content.append(f"\n> {main_metadata['description']}")
# Group by category
categories = defaultdict(list)
seen_titles = set()
for url, metadata in sorted_urls:
title = metadata['title']
if title not in seen_titles:
categories[metadata['category']].append((url, metadata))
seen_titles.add(title)
# Add sections
for category in ['Docs', 'API', 'Guides', 'Examples', 'Blog', 'Optional']:
if category in categories:
content.append(f"\n## {category}")
for url, metadata in categories[category]:
if metadata['description']:
content.append(f"\n- [{metadata['title']}]({url}): {metadata['description']}")
else:
content.append(f"\n- [{metadata['title']}]({url})")
return "\n".join(content)
async def process_url(url, max_depth, max_pages):
"""Process URL and generate llms.txt"""
try:
# Add https:// if not present
if not url.startswith(('http://', 'https://')):
url = 'https://' + url
# Validate URL
result = urlparse(url)
if not all([result.scheme, result.netloc]):
return "", "Invalid URL format. Please enter a valid URL."
# Process website
crawler = WebsiteCrawler(max_depth=int(max_depth), max_pages=int(max_pages))
await crawler.crawl_website(url)
content = crawler.generate_llms_txt()
return content, f"Successfully crawled {len(crawler.visited_urls)} pages."
except Exception as e:
return "", f"Error: {str(e)}"
# Create Gradio interface
theme = gr.themes.Soft(primary_hue="blue", font="Open Sans")
with gr.Blocks(theme=theme, css="""
@import url('https://fonts.googleapis.com/css2?family=Open+Sans:wght@400;600&display=swap');
.gradio-container {
font-family: 'Open Sans', sans-serif !important;
}
.gr-button {
font-family: 'Open Sans', sans-serif !important;
font-weight: 600 !important;
}
.primary-btn {
background-color: #2436d4 !important;
color: white !important;
}
.primary-btn:hover {
background-color: #1c2aa8 !important;
}
[data-testid="textbox"] {
font-family: 'Open Sans', sans-serif !important;
}
.gr-padded {
font-family: 'Open Sans', sans-serif !important;
}
.gr-input {
font-family: 'Open Sans', sans-serif !important;
}
.gr-label {
font-family: 'Open Sans', sans-serif !important;
}
""") as iface:
gr.Markdown("# llms.txt Generator")
gr.Markdown("Generate an llms.txt file from a website following the specification.")
with gr.Row():
url_input = gr.Textbox(
label="Website URL",
placeholder="Enter the website URL (e.g., example.com)",
info="The URL will be automatically prefixed with https:// if not provided"
)
with gr.Row():
with gr.Column():
depth_input = gr.Slider(minimum=1, maximum=5, value=3, step=1, label="Maximum Crawl Depth")
with gr.Column():
pages_input = gr.Slider(minimum=10, maximum=100, value=50, step=10, label="Maximum Pages")
generate_btn = gr.Button("Generate llms.txt", variant="primary")
output = gr.Textbox(
label="Generated llms.txt Content",
lines=20,
show_copy_button=True,
container=True
)
status = gr.Textbox(label="Status")
generate_btn.click(
fn=lambda url, depth, pages: asyncio.run(process_url(url, depth, pages)),
inputs=[url_input, depth_input, pages_input],
outputs=[output, status]
)
if __name__ == "__main__":
iface.launch()