# `Slither.Examples.TextAnalysis.TextPipe`
[🔗](https://github.com/nshkrdotcom/slither/blob/v0.1.0/lib/slither/examples/text_analysis/text_pipe.ex#L1)

Text analysis pipeline demonstrating why process isolation beats free-threaded Python.

The pipeline has two phases:

**Phase 1 -- Pipe run:** prepare -> analyze -> classify by sentiment.
  1. Prepares raw text by attaching stopwords from an ETS-backed store (beam stage)
  2. Sends batches to Python for word frequency, readability, and sentiment analysis
  3. Routes results into positive, negative, or neutral buckets based on sentiment score

**Phase 2 -- Worker stats collection:** After the pipe run, uses Dispatch directly to
call `get_worker_stats` on each Python worker. Each worker reports its accumulated
state (`_global_index`, `_doc_count`), proving that:
  - Different workers accumulated different subsets of documents
  - Each worker's `_doc_count` is consistent (no torn counters)
  - The Elixir side can merge per-worker results safely in a single process

Run with `Slither.Examples.TextAnalysis.TextPipe.run_demo/0`.

# `prepare`

Prepare a raw text item for Python analysis.

Reads the full stopword list from the ETS store and packages it
alongside the text into the map expected by `text_analyzer.analyze_batch`.

# `run_demo`

Run the text analysis demo, printing formatted results to stdout.

Executes two phases:
1. The text analysis pipe (prepare -> analyze -> classify)
2. Worker stats collection via Dispatch (proves process isolation)

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*Consult [api-reference.md](api-reference.md) for complete listing*