README.md

metadata

title: WitGym
emoji: 🎭
colorFrom: green
colorTo: yellow
sdk: gradio
sdk_version: 6.17.3
python_version: '3.12'
app_file: app.py
pinned: false
license: apache-2.0
short_description: Paste awkward. Get one sharp wit line + coach drills.
tags:
  - build-small-hackathon
  - track:wood
  - thousand-token-wood
  - comedy
  - rag
  - case-based-reasoning
  - qwen
  - achievement:offbrand
  - achievement:sharing
  - achievement:fieldnotes
  - sponsor:openai

🎭 WitGym

One sharp line, grounded in human precedent — then drills to sharpen it.

WitGym is a comedy coaching engine for awkward real‑life moments. Paste what happened and it returns one usable line (not a paragraph), grounded in structurally similar precedent from The Office — then lets you iterate with drills: sharpen it, different angle, explain why it works.

Live Space: build-small-hackathon/WitGym

Why I built this (and why it’s not “just prompt it to be funny”)

Comedy has always been a personal interest — not just watching it, but reverse‑engineering why a line lands. Most “be funny” apps are vibes: you get a wall of text and no way to improve it.

WitGym treats wit like a skill you can train:

• Extract the mechanism (status games, tension, violation distance, subtext)
• Retrieve precedent by structure (not by topic keywords)
• Draft a few constrained options
• Pick a winner with an explicit rubric
• Polish to one sharp line

Try it in 10 seconds

• Paste any awkward moment (or tap a starter chip in the sidebar).
• You’ll see the phases stream live: extract → retrieve → draft → rank → polish.
• Then iterate with drills: sharpen it, different angle, explain why it works.

What makes it different

• CBR‑RAG on comedy mechanics: retrieval is driven by archetype, tension, violation distance, and subtext — not by copying jokes or matching keywords.
• Small‑model friendly by design: the intelligence is in the pipeline and the precedent index, not “bigger weights.”
• Tournament ranking (not one-shot generation): the best line is selected by a fixed rubric (domain anchoring + final-clause punchline quality + sharpness).
• Inspectable traces: the UI shows what the system did (progressive disclosure), plus a sanitized public trace export.

System overview (high-level)

flowchart TD
  UserInput["User: paste awkward moment"] --> Router{"Route?"}
  Router -->|banter| Banter["One-sentence banter reply"]
  Router -->|coaching| CoachAsk["Ask one clarifying question"]
  Router -->|quick_wit| Pipeline["CBR-RAG wit pipeline"]

  CoachAsk --> Pipeline

  Pipeline --> Extract["Pass 1: Extract ComedyMetadata via Qwen3.5-27B"]
  Extract --> Retrieve["Retrieve top precedent scenes via bge-small"]
  Retrieve --> Generate["Pass 2: Draft persona candidates"]
  Generate --> Rank["Pass 3: Rank by explicit rubric"]
  Rank --> Compress["Pass 4: Compress to one sharp line"]
  Compress --> Output["Final line + sharpen or explain drills"]

Algorithm sketch (pipeline-level)

sequenceDiagram
  participant UI as Gradio UI
  participant Engine as WitGym Engine
  participant LLM as Qwen 3.5 27B
  participant Embed as BGE Small
  participant Index as Office Index

  UI->>Engine: respond_stream
  Engine->>LLM: extract ComedyMetadata
  LLM-->>Engine: metadata JSON
  Engine->>Embed: encode metadata query
  Embed-->>Engine: query embedding
  Engine->>Index: cosine search and rerank
  Index-->>Engine: precedent scenes
  Engine->>LLM: draft persona candidates
  LLM-->>Engine: candidates
  Engine->>LLM: rank candidates
  LLM-->>Engine: winner
  Engine->>LLM: compress winner
  LLM-->>Engine: final line
  Engine-->>UI: stream all phases

Evidence / badges

• Sharing is Caring (achievement:sharing): public pipeline traces — sanitized JSONL (metadata, scene IDs, candidate stats, execution log; no Office dialogue text). Regenerate with uv run python scripts/export_public_traces.py.
• Field Notes (achievement:fieldnotes): docs/field-notes.md.
• Off‑Brand UI (achievement:offbrand): custom Gradio UI + streaming trace disclosure.

Submission links

• Source code: GitHub — https://github.com/akshay-babbar/witgym
• Demo video: YouTube — https://youtu.be/enb5ua65RZM
• Social post: LinkedIn — https://www.linkedin.com/posts/akshay4b_happy-to-share-a-project-ive-been-building-ugcPost-7472401282822111232-Q_nt/
• Validate README: Build Small validator

Technical details (grounded in the repo)

• Engine entrypoint: witgym/engine.py (respond() + respond_stream()).
• Pass 1 extraction: witgym/extractor.py → ComedyMetadata (JSON).
• Retrieval: witgym/retriever.py (cosine over an indexed embedding matrix; optional cross-encoder rerank).
• Pass 2 generation + ranking: witgym/generator.py (persona candidates + rubric ranker).
• UI: app.py (Gradio; streaming phases + progressive disclosure).

Run locally

uv sync
witgym-index
export LLM_BACKEND=hf_api
export HF_TOKEN=hf_...
uv run python app.py

Built for the Build Small Hackathon 2026 — Thousand Token Wood.