Counts words from scraped URL in Flask
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Updated
Apr 12, 2023 - Python
Counts words from scraped URL in Flask
Simple text processing, part 2. First steps with regular expression (regex) in Python.
Apify actor: token-aware, structure-preserving text chunker for RAG. Sizes chunks in tiktoken tokens, keeps headings, tables, code blocks and links intact, and emits token counts, heading paths and content hashes. Billed per KB of text processed.
A multilingual translation tool with context-aware text segmentation and Amazon Translate integration.
Screen resumes to identify the best candidates.Build a machine learning model that screens resumes and ranks candidates based on job descriptions.Streamline the hiring process for HR departments by automating candidate screening.
web and youtube scrapping for the given input like a search engine that brings links and text from web and youtube.
Python text scrambler & case randomizer with punctuation-aware shuffling.
Structural and Lexical complexity data analysis of Seanad corpus data.
Multi-script Sanskrit transliteration engine for Dharmic digital humanities corpora.
Record, replay, and diff WebSocket sessions for debugging.
印尼电信项目聊天记录分析工具 - 完整的Python工具链用于分析MOCN项目聊天记录
Applies named regex patterns to any CSV text column and returns structured extraction results. Thirteen built-in patterns cover common document fields. Custom patterns can be added at runtime without editing code. Outputs matched records as CSV.
CLI text encryption and decryption tool supporting Caesar and substitution ciphers
A DSA-based plagiarism detection system using KMP, Rabin-Karp, Shingling, Winnowing, Jaccard Similarity, and TF-IDF with FastAPI backend and premium React dashboard.
Write, read, detect and strip invisible text watermarks in 13 Unicode/ASCII channels. Identical functionality from a non-interactive CLI and a Flask web UI + JSON API.
LexNeedle is a deterministic, Unicode-aware dictionary matcher for Python 3.12+, with zero runtime dependencies. Use it when exact terms, trustworthy original-text offsets, and predictable overlap handling matter.
Unified for All MLX tasks, Train locally. Prove it on Apple Silicon. Scale when needed.
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