| import streamlit as st
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| import re
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|
|
|
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| def recursive_splitter(data):
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| paragraphs = data.split('\n\n')
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| sentences = [sentence for para in paragraphs for sentence in para.split('.')]
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| return [sentence.strip() + '.' for sentence in sentences if sentence.strip()]
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|
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| def html_splitter(data):
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| parts = re.split(r'(<[^>]+>)', data)
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| return [part for part in parts if part.strip()]
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|
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| def markdown_splitter(data):
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| parts = re.split(r'(^#{1,6} .*$)', data, flags=re.MULTILINE)
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| return [part.strip() for part in parts if part.strip()]
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|
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| def code_splitter(data):
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| parts = re.split(r'(?m)^def ', data)
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| return [f'def {part.strip()}' if idx > 0 else part.strip() for idx, part in enumerate(parts) if part.strip()]
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|
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| def token_splitter(data):
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| tokens = re.findall(r'\b\w+\b', data)
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| return tokens
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|
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| def character_splitter(data):
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| return list(data)
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|
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| def semantic_chunker(data):
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| sentences = re.split(r'(?<=\.)\s+', data)
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| return [sentence.strip() for sentence in sentences if sentence.strip()]
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|
|
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| splitter_details = {
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| "Recursive Splitter": {
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| "function": recursive_splitter,
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| "description": "Recursively splits the data into smaller chunks, like paragraphs into sentences. Useful for processing text at different levels of granularity."
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| },
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| "HTML Splitter": {
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| "function": html_splitter,
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| "description": "Splits data based on HTML tags, making it easier to work with structured web content, such as isolating specific sections of HTML code."
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| },
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| "Markdown Splitter": {
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| "function": markdown_splitter,
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| "description": "Splits markdown content based on headings (e.g., '# ', '## '). Useful for processing documents written in Markdown format."
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| },
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| "Code Splitter": {
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| "function": code_splitter,
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| "description": "Splits programming code into logical blocks like functions or classes. Useful for code analysis and documentation."
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| },
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| "Token Splitter": {
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| "function": token_splitter,
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| "description": "Splits data into individual tokens/words, which is often the first step in natural language processing (NLP) tasks."
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| },
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| "Character Splitter": {
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| "function": character_splitter,
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| "description": "Splits text into individual characters. Useful for character-level analysis or encoding tasks."
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| },
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| "Semantic Chunker": {
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| "function": semantic_chunker,
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| "description": "Splits data based on semantic meaning, typically by sentences. Ensures that related information stays together."
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| },
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| }
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| st.sidebar.title("Splitter Settings")
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| st.sidebar.subheader("Data Input")
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| user_data = st.sidebar.text_area("Enter the data you want to split:", "This is a sample text. Enter your data here...")
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|
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| st.sidebar.subheader("Splitter Type")
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| splitter_type = st.sidebar.selectbox(
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| "Choose a splitter type:",
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| list(splitter_details.keys())
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| )
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|
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| st.sidebar.subheader("Options")
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| show_info = st.sidebar.checkbox("Show information about all splitter types")
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|
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| st.title("RAG Splitter System")
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| st.markdown('<p class="title">Developed By: Irfan Ullah Khan</p>', unsafe_allow_html=True)
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|
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| st.subheader(f"Selected Splitter: {splitter_type}")
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| st.write(splitter_details[splitter_type]["description"])
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|
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|
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| if st.button("Split Data"):
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| with st.spinner('Processing data...'):
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| splitter_function = splitter_details[splitter_type]["function"]
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| split_output = splitter_function(user_data)
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|
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| if split_output:
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| st.subheader(f"Output using {splitter_type}")
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| for idx, part in enumerate(split_output):
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| st.write(f"**Part {idx + 1}:**")
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| st.write(part)
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|
|
| if show_info:
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| for name, details in splitter_details.items():
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| st.subheader(name)
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| st.write(details["description"])
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|
|
