lloyd-drift-demo 0.1.2

LLOYD symbolic drift detection engine

L.L.O.Y.D. — Language Layers Over Your Data

A symbolic drift detection and tone deviation engine that listens like a human would — tracking not just sentiment, but meaning breaks, symbolic conflict, and emotional escalation.

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🔗 Try It Now

• 📦 Install via PyPI:
  pip install lloyd-drift-demo==0.1.0
  → View on PyPI

• 🌐 Live Demo (Streamlit):
  https://tinyurl.com/Lloyd-demo

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🔍 What Is LLOYD?

LLOYD isn’t another sentiment classifier.
It’s a drift-aware analyzer that tells you when a conversation turns — emotionally, symbolically, or relationally.

From sarcastic reversals to performative breakdowns, LLOYD is designed to detect subtle shifts in tone that traditional NLP often misses.

✅ Calibration is limited, but customizable.
LLOYD is lightly tuned, but designed for adaptation to domain-specific tone models.

✅ Work-in-progress with feedback welcome.
This is an active project — contributions, questions, and use-case tests are encouraged.
Contact: [e-mail]

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✨ What Makes It Different?

🧱 How LLOYD Compares: Above the Stack

LLOYD doesn’t just label tone — it listens like a person, tracking symbolic shifts, emotional slope, and layered meaning.

Here’s how it stacks up:

Tier	Model Type	Capabilities	Notes
🟩 LLOYD	Symbolic Drift Engine	Emotional drift scoring, override logic, symbolic pattern detection, sarcasm flags, badges	✅ Built for human-level nuance and meaning tracking
🟨 Mid-Level	Sentiment Classifier	Polarity scoring, intensity detection	⚠️ Misses sarcasm, symbolic shifts, escalation cues
🟥 Legacy	Keyword Matcher	Token triggers, emotion word lists	❌ Fails on nuance, symbolic inversion, or context

🟢 LLOYD hears the difference between “Great job” and “Great job…”
🔴 Others just check for “positive” or “negative.”

Please note: LLOYD is already scaffolded for Drift Memory and short-term tone weighting —
this table excludes those in-progress features until the official demo drops.

✨ It’s better — and it’s not even done yet.

• Symbolic override detection ("Great job…", "You helped?")
• Emphasis escalation tracking (ALL CAPS, !!!, emoji floods)
• Drift memory modeling to detect emotional pressure buildup
• Mirror match and mocked echo detection
• Output includes rationale, badge label, override label

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🧪 Quick Start

pip install lloyd-drift-demo==0.1.0
python devtools/run.py

Sample output:

Badge    : 🛳 override: emphasis_override
🔹 [sarcasm_hint]
Baseline : Great job.
Incoming : Great job...
Drift    : True
Label    : sarcasm_hint
Δ        : 80
Rationale: Trailing or embedded sarcasm marker detected.

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🌐 Streamlit GUI

Launch the visual interface:

streamlit run devtools/sandbox_demo/app/app.py

Try this real example:

Baseline : Why wasn’t this done earlier?
Incoming : You are garbage.
Drift    : True
Label    : hostile_emphasis
Δ        : 92
Badge    : 🛳 override: hostile_emphasis
Rationale: Intensified hostile language detected — override triggered.

Baseline : Why wasn’t this done earlier?
Incoming : I had to take out the garbage.
Drift    : False
Label    : neutral
Δ        : 5
Badge    : none
Rationale: No drift detected — response remains within expected symbolic frame.

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📊 Drift Graph — Tone Shift Over Time

This plot captures real drift data across a conversation, showing:

• Δ tone changes turn by turn
• Sudden spikes in emotional pressure
• Contextual difference between neutral and hostile replies
• Future use of short-term memory to weight recent drift and override impact

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🧠 Coming Soon — Drift Memory + Field Responsiveness Demo

Scaffolding is already in place for a future interactive demo that showcases:

• Short-term memory tracking across turns
• Escalation detection (e.g., passive → sarcastic → hostile)
• Override arbitration with memory decay
• Field responsiveness (proactive vs. reactive tone shifts)

Prototype logic lives in:

src/lloyd_drift_demo/engine/drift_memory.py

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🛠 Drift Thresholds (Tunable)

Users can modify:

• DRIFT_THRESHOLD (default = 0.15)
• Emphasis override sensitivity
• Symbolic override rules

Feedback is welcome for future tuning.

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💡 Tip: Use ChatGPT as a Temporary Code Lab Assistant

You can copy and paste full Python files into ChatGPT to get live analysis, refactors, and debugging help — just like a pair programmer.

✅ Totally legal — as long as it’s your code (or permissively licensed)
✅ Session-aware — ChatGPT can remember your pasted files for the whole conversation
✅ No training risk — Your code stays private; nothing is used to train the model

⚠️ Session memory resets when you refresh, log out, or start a new chat
⚠️ Don’t paste private or proprietary code unless you’re sure it’s safe

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🗂 Project Structure

📁 LLOYD_Language_Engine/
├── README.md
├── media/
│   └── graph.png
├── src/
│   └── lloyd_drift_demo/
│       └── engine/
│           └── drift_utils_v2.py
├── demos/
│   └── sandbox_demo/
│       └── app/
│           └── app.py

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📦 Requirements

• Python 3.11+
• pip install -r requirements.txt

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🤝 Contribute or Collaborate

This is an active research project.
Feedback, testing, and conceptual contributions welcome.

📬 Contact: [e-mail]
🧵 Twitter/Reddit: @putmanmodel

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📚 Credits

• Built on top of the excellent GoEmotions dataset from Google Research
• Special thanks to the community at r/datasets for sharing valuable resources and inspiration
• And to Lloyd, my brother — whom I "accidentally" named this project after

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📜 License

Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Use, modify, and remix freely — just don’t sell it.

“Most sentiment systems end with polarity.
LLOYD starts with meaning.”