Offline Document Intelligence Studio

A FastAPI app with a small HTML dashboard and six routers: OCR, chat, summarise, extract, predict and retrieval Q&A. LLM work is delegated to a local Ollama server, and the whole thing ships as a Docker image and a Windows executable.

Sensitive documents shouldn't have to leave the machine. The goal was OCR, summaries, field extraction and Q&A over uploaded files using only local models.

1. DashboardJinja2 + plain HTML/JS front end served by FastAPI.
2. Routers/ocr /chat /summarize /extract /predict /rag, each a thin layer over a service.
3. ServicesOCR, LLM, summary, extraction, prediction and retrieval logic.
4. OllamaExternal local server called over HTTP, configurable through environment variables.
5. PackagingDockerfile, compose file and a PyInstaller-aware path setup for the Windows executable.

1. 01

   Input

   Image or PDF upload.

2. 02

   Preprocessing

   PDF pages at 300 dpi, then grayscale, resize, denoise, deskew and threshold with OpenCV.

3. 03

   OCR

   Tesseract text extraction.

4. 04

   Retrieval

   500-character chunks indexed with TF-IDF; top matches by cosine similarity.

5. 05

   LLM

   Ollama answers only from the retrieved context, or summarises and extracts fields as JSON.

6. 06

   Output

   Text, summary, extracted fields or a grounded answer in the dashboard.

• OCR with OpenCV preprocessing (denoise, deskew, threshold)
• Local LLM (Ollama, llama3.2) for chat, summaries and JSON extraction
• TF-IDF retrieval for document Q&A
• RandomForest classifier (Iris, as an assignment component)

Router and service separation

Endpoints stay thin; OCR, LLM, retrieval and prediction each live in their own service module.

Environment-driven config

The Ollama URL, Poppler path and Tesseract command are environment variables, and a cloud-demo mode flag switches off what can't run in the hosted version.

Honest hosting write-up

The repo documents why Ollama can't run on a free Hugging Face Space, and what the hosted demo therefore omits.

Frozen-app support

Path handling accounts for PyInstaller's runtime layout so the same code runs as a script or a packaged exe.

• A local LLM doesn't fit the free Hugging Face Space limits, so the hosted demo runs without the model-backed features.
• Packaging for Windows meant handling frozen-app resource paths.

• FastAPI
• Python
• Tesseract
• OpenCV
• scikit-learn
• Ollama
• Docker
• Jinja2