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import streamlit as st
import requests
from bs4 import BeautifulSoup
import re
from llama_index.core.node_parser import MarkdownNodeParser
from llama_index.core.schema import Document, MetadataMode
import textstat
from markdownify import markdownify as md
# --- Core Logic Classes ---
class WebpageContentProcessor:
"""
Handles fetching, converting, and parsing webpage content into structured chunks.
This class is responsible for the entire content processing pipeline.
"""
def __init__(self):
pass
def fetch_and_convert_to_markdown(self, url: str) -> str:
"""
Fetches HTML content, removes common boilerplate tags from the entire page,
and then converts the remaining body content to Markdown using markdownify.
"""
try:
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
response = requests.get(url, headers=headers, timeout=15)
response.raise_for_status()
html_content = response.text
soup = BeautifulSoup(html_content, 'html.parser')
# Remove common boilerplate and non-content tags from the entire document
tags_to_remove = ['nav', 'header', 'footer', 'aside', 'script', 'style', 'noscript', 'form']
for tag_name in tags_to_remove:
for element in soup.find_all(tag_name):
element.decompose()
# Process the entire remaining body
content_container = soup.find('body')
if not content_container:
return "Error: Could not find the <body> of the webpage."
markdown_output = md(str(content_container))
# Post-processing to clean up the resulting Markdown
markdown_output = re.sub(r'\n{3,}', '\n\n', markdown_output)
markdown_output = re.sub(r'(\n\s*[\*\-]\s*\n)|(^\s*[\*\-]\s*$)', '\n', markdown_output, flags=re.MULTILINE)
return markdown_output.strip()
except requests.exceptions.Timeout:
return "Error: The request timed out. The server is taking too long to respond."
except requests.exceptions.RequestException as e:
return f"Error fetching the URL: {e}. Please check the URL and your connection."
except Exception as e:
return f"An unexpected error occurred during content processing: {e}"
def parse_markdown_into_chunks(self, markdown_content: str) -> list:
"""
Parses Markdown content into logically separated chunks based on its structure.
Uses MarkdownNodeParser to respect headers and sections.
"""
if not markdown_content or "Error" in markdown_content:
return []
parser = MarkdownNodeParser(include_metadata=True)
doc = Document(text=markdown_content)
nodes = parser.get_nodes_from_documents([doc])
structured_chunks = []
for i, node in enumerate(nodes):
content = node.get_content(metadata_mode=MetadataMode.NONE).strip()
if not content:
continue
title_match = re.match(r"^(#+)\s*(.*)", content)
if title_match:
title = title_match.group(2).strip()
content_text = content[len(title_match.group(0)):].strip()
else:
first_line = content.split('\n')[0].strip()
title = (first_line[:75] + '...') if len(first_line) > 75 else first_line
content_text = content
if not title:
title = f"[Chunk {i+1}]"
structured_chunks.append({
"id": i,
"title": title,
"content": content_text
})
return structured_chunks
class ChunkManager:
"""
Manages the state of chunks, including their content, statistics, and targets.
"""
def __init__(self):
self._chunks = []
self.target_flesch_min = 60
self.target_grade_max = 9
self.target_min_chunk_words = 40
self.target_max_chunk_words = 600
def set_chunks(self, chunks: list):
self._chunks = [self._add_stats_to_chunk(chunk) for chunk in chunks]
def get_chunks(self) -> list:
return self._chunks
def _add_stats_to_chunk(self, chunk: dict) -> dict:
chunk['stats'] = self._calculate_chunk_stats(chunk['content'])
return chunk
def _calculate_chunk_stats(self, text: str) -> dict:
"""Calculates readability and other metrics for a text chunk."""
stats = {}
try:
stats['word_count'] = textstat.lexicon_count(text, removepunct=True)
stats['flesch_reading_ease'] = textstat.flesch_reading_ease(text)
stats['flesch_kincaid_grade'] = textstat.flesch_kincaid_grade(text)
except (Exception, TypeError):
stats.update({'word_count': 0, 'flesch_reading_ease': 0, 'flesch_kincaid_grade': 0})
return stats
def format_chunk_stats(self, stats: dict) -> str:
"""Creates a formatted string of stats with color-coding based on targets."""
flesch_color = "green" if stats.get('flesch_reading_ease', 0) >= self.target_flesch_min else "red"
grade_color = "green" if stats.get('flesch_kincaid_grade', 0) <= self.target_grade_max else "red"
word_color = "green" if self.target_min_chunk_words <= stats.get('word_count', 0) <= self.target_max_chunk_words else "red"
return (
f"**Word Count:** <span style='color:{word_color};'>{stats.get('word_count', 0)}</span> | "
f"**Reading Ease:** <span style='color:{flesch_color};'>{stats.get('flesch_reading_ease', 0):.2f}</span> | "
f"**Grade Level:** <span style='color:{grade_color};'>{stats.get('flesch_kincaid_grade', 0):.2f}</span>"
)
def get_document_summary_stats(self) -> str:
"""Calculates and formats stats for the entire document."""
if not self._chunks:
return "No document loaded."
total_words = sum(c['stats']['word_count'] for c in self._chunks)
if len(self._chunks) > 0:
avg_ease = sum(c['stats']['flesch_reading_ease'] for c in self._chunks) / len(self._chunks)
avg_grade = sum(c['stats']['flesch_kincaid_grade'] for c in self._chunks) / len(self._chunks)
else:
avg_ease = avg_grade = 0
return (
f"- **Total Chunks:** {len(self._chunks)}\n"
f"- **Total Words:** {total_words}\n"
f"- **Avg. Reading Ease:** {avg_ease:.2f}\n"
f"- **Avg. Grade Level:** {avg_grade:.2f}"
)
def get_chunk_by_id(self, chunk_id: int) -> dict | None:
return next((c for c in self._chunks if c["id"] == chunk_id), None)
def update_chunk_content(self, chunk_id: int, new_content: str):
chunk = self.get_chunk_by_id(chunk_id)
if chunk:
chunk["content"] = new_content
self._add_stats_to_chunk(chunk)
if chunk["title"].startswith("["):
first_line = new_content.split('\n')[0].strip()
new_title = (first_line[:75] + '...') if len(first_line) > 75 else first_line
if new_title:
chunk["title"] = new_title
def delete_chunk(self, chunk_id: int):
self._chunks = [c for c in self._chunks if c["id"] != chunk_id]
for i, chunk in enumerate(self._chunks):
chunk['id'] = i
def get_final_markdown(self) -> str:
if not self._chunks:
return "No content to display."
final_doc_parts = []
for c in self._chunks:
is_header = re.match(r"^(#+)\s*(.*)", c['title'])
if not c['title'].startswith("[") and not is_header:
final_doc_parts.append(f"## {c['title']}\n\n{c['content']}")
else:
final_doc_parts.append(c['content'])
return "\n\n---\n\n".join(final_doc_parts)
def set_targets(self, flesch_min: float, grade_max: float, min_words: int, max_words: int):
self.target_flesch_min = flesch_min
self.target_grade_max = grade_max
self.target_min_chunk_words = min_words
self.target_max_chunk_words = max_words
self.set_chunks(self.get_chunks())
# --- Streamlit UI Application ---
st.set_page_config(layout="wide", page_title="Webpage Content Editor")
# --- MODIFIED: Custom CSS to increase sidebar width ---
st.markdown(
"""
<style>
[data-testid="stSidebar"] {
width: 450px !important;
}
</style>
""",
unsafe_allow_html=True
)
def init_session_state():
if 'processor' not in st.session_state:
st.session_state.processor = WebpageContentProcessor()
if 'manager' not in st.session_state:
st.session_state.manager = ChunkManager()
if 'selected_chunk_id' not in st.session_state:
st.session_state.selected_chunk_id = None
if 'status_message' not in st.session_state:
st.session_state.status_message = ""
init_session_state()
processor = st.session_state.processor
manager = st.session_state.manager
with st.sidebar:
# --- MODIFIED: Removed the st.image line for the logo ---
st.title("Settings & Overview")
with st.expander("About this App & AI Writing Guidelines", expanded=True):
st.info(
"""
This app helps you refine web content for AI synthesis by chunking it into logical, verifiable blocks.
**Writing for AI Verifiability:**
* **Structure with Headers:** Use H1, H2, H3 tags logically.
* **Write for Clarity:** Use short, direct sentences. State facts explicitly.
* **Create Verifiable Blocks:** Format content as definitions, Q&As, or step-by-step guides.
* **Use the Editor's Metrics:** Aim for a **Reading Ease > 60** and a **Word Count** between 40-600 per chunk. The colors will guide you.
""", icon="💡"
)
st.subheader("📊 Document Overview")
st.markdown(manager.get_document_summary_stats(), unsafe_allow_html=True)
st.subheader("🎯 Content Targets")
with st.form("targets_form"):
st.write("Set readability targets to guide your editing. Colors in the editor will reflect these targets.")
c1, c2 = st.columns(2)
f_min = c1.number_input("Min Flesch Reading Ease", value=float(manager.target_flesch_min), help="Measures readability. Higher scores mean the text is easier to read. Scores of 60-70 are considered plain English.")
g_max = c2.number_input("Max Flesch-Kincaid Grade", value=float(manager.target_grade_max), help="Estimates the U.S. school grade level needed to understand the text. A score of 8.0 means an eighth grader can read it. Lower scores are easier to read.")
w_min = c1.number_input("Min Chunk Words", value=int(manager.target_min_chunk_words))
w_max = c2.number_input("Max Chunk Words", value=int(manager.target_max_chunk_words))
if st.form_submit_button("Set New Targets", use_container_width=True):
manager.set_targets(f_min, g_max, w_min, w_max)
st.session_state.status_message = "Content targets have been updated."
st.rerun()
st.subheader("📋 Final Compiled Document")
st.text_area("Final Markdown Output", manager.get_final_markdown(), height=300, key="final_markdown")
# --- Main Page Layout ---
st.title("📝 Content Chunk Editor")
st.caption("Developed by [Emilija Gjorgjevska](https://www.linkedin.com/in/emilijagjorgjevska/) | Inspired by Andrea Volpini's [work on content chunking](https://wordlift.io/blog/en/googles-ai-mode-product-pages/).<br>A tool to fetch, chunk, and refine web content for AI synthesis. Best experienced on desktop.", unsafe_allow_html=True)
url_input = st.text_input("Enter a webpage URL to start", key="url_input")
with st.expander("⚠️ Important Information", expanded=False):
st.warning(
"""
**Early Draft:** This is an early version of the application. You may encounter bugs or incomplete features.
""",
icon="🛠️"
)
st.warning(
"""
**Restrictive Bot Policy:** This tool fetches content using automated requests. If a target website blocks bots, the app may time out or fail to retrieve content.
""",
icon="🤖"
)
if st.button("Process URL", use_container_width=True, type="primary"):
if url_input:
with st.spinner("Fetching and chunking content..."):
markdown = processor.fetch_and_convert_to_markdown(url_input)
if "Error" in markdown:
st.session_state.status_message = markdown
manager.set_chunks([])
st.session_state.selected_chunk_id = None
else:
chunks = processor.parse_markdown_into_chunks(markdown)
manager.set_chunks(chunks)
if chunks:
st.session_state.status_message = f"Successfully processed {len(chunks)} chunks."
st.session_state.selected_chunk_id = chunks[0]['id']
else:
st.session_state.status_message = "Could not extract any content chunks."
st.session_state.selected_chunk_id = None
st.rerun()
if st.session_state.status_message:
st.toast(st.session_state.status_message)
st.session_state.status_message = ""
chunks = manager.get_chunks()
if not chunks:
st.write("Process a URL to begin editing content chunks, or adjust settings in the sidebar.")
with st.expander("Chunking Strategy Examples"):
st.write("See how different websites structure their content, affecting chunking quality.")
st.error("**Bad Chunking Example (Few Structural Headers)**")
st.markdown("""
* [Wikipedia: Markdown](https://en.wikipedia.org/wiki/Markdown)
""")
st.success("**Good Chunking Examples (Clear, Hierarchical Headers)**")
st.markdown("""
* [The Blog Starter](https://www.theblogstarter.com/)
* [Google Safety Blog](https://blog.google/technology/safety-security/google-survey-digital-security-2025/)
* [HubSpot: What is a Blog?](https://blog.hubspot.com/marketing/what-is-a-blog)
""")
else:
chunk_ids = [c['id'] for c in chunks]
if st.session_state.selected_chunk_id not in chunk_ids:
st.session_state.selected_chunk_id = chunk_ids[0] if chunk_ids else None
if st.session_state.selected_chunk_id is not None:
chunk_options = {c['id']: c['title'] for c in chunks}
selected_id = st.selectbox(
"Select a chunk to edit",
options=chunk_ids,
format_func=lambda x: f"Chunk {x}: {chunk_options.get(x, 'N/A')}",
index=chunk_ids.index(st.session_state.selected_chunk_id)
)
if selected_id != st.session_state.selected_chunk_id:
st.session_state.selected_chunk_id = selected_id
st.rerun()
selected_chunk = manager.get_chunk_by_id(st.session_state.selected_chunk_id)
if selected_chunk:
editor_col, preview_col = st.columns(2)
with editor_col:
st.markdown(f"**Editing: {selected_chunk['title']}**")
st.markdown(manager.format_chunk_stats(selected_chunk['stats']), unsafe_allow_html=True)
edited_content = st.text_area(
"Chunk Content (Markdown)",
value=selected_chunk['content'],
height=400,
key=f"editor_{selected_chunk['id']}"
)
b_col1, b_col2, _ = st.columns([1, 1, 3])
if b_col1.button("Update Chunk", use_container_width=True, key=f"update_{selected_chunk['id']}"):
manager.update_chunk_content(selected_chunk['id'], edited_content)
st.session_state.status_message = "Chunk updated successfully!"
st.rerun()
if b_col2.button("Delete Chunk", use_container_width=True, key=f"delete_{selected_chunk['id']}"):
manager.delete_chunk(selected_chunk['id'])
st.session_state.status_message = "Chunk deleted."
remaining_chunks = manager.get_chunks()
st.session_state.selected_chunk_id = remaining_chunks[0]['id'] if remaining_chunks else None
st.rerun()
with preview_col:
st.markdown("**Live Preview**")
with st.container(height=525, border=True):
st.markdown(edited_content, unsafe_allow_html=True) |