GhostTrace AI: A Local AI That Investigates Digital Ghosts

说明与用途

GhostTrace AI 是一个本地工具，读取虚构的活动日志，使用透明规则评分异常，并利用 Ollama 上的 Qwen2.5 3B 模型生成恐怖侦探故事，支持离线回退引擎。

问题

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.

结果

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.

工作流

• 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.

证据

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.

来源

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

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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.

来源

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.

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