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