GhostTrace AI: A Local AI That Investigates Digital Ghosts

Описание и применение

GhostTrace AI – локальное приложение, которое анализирует фиктивные журналы активности, выявляет аномалии и генерирует атмосферные истории о призраках, используя модель Qwen2.5 3B через Ollama.

Задача

Develop a local AI tool that can analyze fictional computer activity logs, detect anomalies, and generate spooky detective stories without relying on cloud AI services.

Результат

All 25 automated tests passed, and the demo video shows the tool detecting anomalies, explaining flags, and generating a spooky narrative. The local inference model eliminates cloud costs and enhances privacy.

Рабочий процесс

• 1. Read supported JSON, LOG, and TXT files from a selected sample data directory.
• 2. Apply transparent rule-based scoring to each event to detect anomalies and generate explanations.
• 3. Use the local Qwen2.5 3B model through Ollama to generate a spooky story based on flagged events.
• 4. Display a filterable investigation timeline of events and scores.
• 5. If the local model is unavailable, invoke the fallback engine to produce a procedural story.

Доказательства

GhostTrace AI analyzes sample activity logs, identifies unusual-looking events using transparent rule-based scoring, and uses a locally running language model to turn those events into a story.

Источник

I built GhostTrace AI using Python, Ollama, Qwen2.5 3B, Streamlit, and Pytest.

Источник

The project includes automated tests for log parsing, anomaly scoring, sample cases, timeline filtering, and story-generation fallback. During development, the reported test suite passed all 25 tests.

Источник

By running the open-weight Qwen2.5 3B model locally through Ollama, the application can process its investigation data on the user's machine. There are no per-request cloud AI charges, and developers can inspect the source code, modify prompts, and experiment with other supported models.

Источник