# PageParse User Manual

Welcome to PageParse, a secure, local, offline-first application designed to parse and extract structured task logs, notes, and details from images, text documents, voice memos, spreadsheets, and video files.

## Features

- **Multimodal Extraction**: Supports images, voice files, video recordings, spreadsheets, and scanned documents.
- **Local AI Explanations**: Leverages Ollama vision models (`moondream`, `llava`) and SLMs (`llama3.2`) to generate detailed explanations and structure raw layout fields locally.
- **Structured SQLite Logging**: Extracted items are classified and parsed into a lightweight SQLite database.
- **Air-gap Fallback**: A visual mode switch allowing zero-network mock storage capability.

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## Operating Instructions

### 1. Prerequisites
Ensure Ollama is running locally with the necessary models:
```cmd
ollama run llama3.2:1b
ollama run moondream
```

### 2. Start the Backend
Activate your virtual environment and start the FastAPI web server:
```cmd
.venv\Scripts\activate
python -m pageparse.cli serve
```

### 3. Accessing the Web App
Open your browser and navigate to: **[http://localhost:8000/](http://localhost:8000/)**

### 4. Uploading Media
1. Ensure the **Air-gap mode** switch is toggled **OFF** in the top-right settings.
2. Select your media file (image, audio, or video) inside the dropzone.
3. Wait for progress to complete (100%).
4. Click on the item in **Ingestion History** to see the detailed local AI summary in the **Raw Extract** panel.