README.md

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Local AI — Offline Multi-Modal Document Intelligence System

Turn images, PDFs, plain text, audio, and video into structured JSON — 100% offline, CPU-only. No cloud APIs, no API keys, no telemetry, no internet dependency after setup.

Built with Streamlit (not React/FastAPI — swapped per project requirements for a single-process, easy-to-run app), Tesseract OCR, and a lightweight rule-based local extraction engine, with an optional upgrade path to a real local LLM via llama-cpp-python.

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✅ What's fully working out of the box (zero extra downloads)

• Images → OCR → JSON (Tesseract, native)
• PDFs → text/OCR → JSON (pdfplumber, with automatic OCR fallback for scanned pages)
• Plain text → JSON
• Automatic document type classification (invoice, resume, receipt, meeting notes, contract, medical report, letter, research paper, certificate, general)
• Rule-based entity extraction: people, emails, phones, dates, amounts, organizations, invoice numbers, keywords, tags, summary, confidence score
• SQLite storage, full History view, Search, Dashboard with charts
• Export to JSON / CSV / Excel
• Duplicate detection via SHA-256 file hashing
• Demo Mode (one click, no upload needed)

This mode (rules in Settings) is what the app uses by default and what was tested end-to-end while building this — see "What was tested" below.

🔌 Optional upgrades (require one-time internet access on your machine)

Feature	Install	Notes
Real local LLM extraction	pip install llama-cpp-python + a .gguf model in models/	Switch to "llm" mode in Settings. See backend/llm_local.py for model recommendations (TinyLlama, Phi-3-mini, Qwen2.5-3B).
Audio transcription	pip install openai-whisper + ffmpeg on PATH	Powers .mp3/.wav/.m4a uploads
Video transcription	same as above	Extracts audio via ffmpeg, then transcribes

Once installed, these still run entirely locally / CPU-only — the "offline" install step is a one-time download of the model file, not a runtime API call.

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Quickstart

git clone <this-repo>
cd local-ai
python -m venv venv && source venv/bin/activate   # optional but recommended
pip install -r requirements.txt

# Tesseract binary (required for OCR):
#   Ubuntu/Debian:  sudo apt install tesseract-ocr
#   macOS:          brew install tesseract
#   Windows:        https://github.com/UB-Mannheim/tesseract/wiki

streamlit run app.py

Open the URL Streamlit prints (usually http://localhost:8501). Try Home → Run Demo Mode, or upload a file from sample_docs/.

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Architecture

Input (image/pdf/text/audio/video)
        │
        ▼
   OCR / Transcription      (Tesseract / pdfplumber / Whisper)
        │
        ▼
   Text Cleaning
        │
        ▼
   Local AI Extraction      (rule-based engine, or local LLM via llama.cpp)
        │
        ▼
   Structured JSON
        │
        ▼
   SQLite Storage  ──►  Dashboard / History / Search / Export

Folder structure

local-ai/
├── app.py                # Streamlit UI (Home, Upload, Dashboard, History, Search, Settings)
├── backend/
│   ├── ocr.py             # Tesseract image OCR
│   ├── pdf_extract.py     # PDF text extraction + OCR fallback for scans
│   ├── extractor.py       # Document classification + rule-based JSON extraction
│   ├── llm_local.py       # Optional llama-cpp-python local LLM extraction
│   ├── audio.py           # Optional Whisper transcription
│   ├── video.py           # Optional ffmpeg audio extraction + Whisper
│   ├── pipeline.py         # Orchestrates the full flow + status updates
│   ├── db.py               # SQLite storage (stdlib sqlite3, no ORM dependency)
│   └── exporters.py        # JSON / CSV / Excel export
├── prompts/
│   └── templates.py        # Prompt templates per document type (used in LLM mode)
├── sample_docs/            # Sample files for quick testing / Demo Mode
├── models/                 # Drop a .gguf model here to enable LLM mode
├── data/                   # SQLite database lives here (created at runtime)
├── requirements.txt
├── LICENSE                 # GPLv3
└── README.md

API surface (internal, called by the Streamlit UI)

These are plain Python functions, not HTTP endpoints (no separate backend server is needed since Streamlit handles both UI and processing in one process):

• pipeline.process_file(filename, bytes, mode, model_path, ocr_lang, status_callback)
• db.list_documents(), db.search_documents(query), db.get_document(id), db.delete_document(id), db.stats()
• exporters.to_json_bytes(rows), to_csv_bytes(rows), to_excel_bytes(rows)

If you specifically need a separate REST API (e.g. to call from another app), the same backend/ modules can be wrapped in FastAPI with a thin main.py — they have no Streamlit dependency themselves.

What was tested while building this (in a sandboxed, no-internet environment)

• Image OCR on a generated test image ✅
• PDF native-text extraction on a generated test PDF ✅
• Document classification across invoice/resume/meeting-notes samples ✅
• Full entity extraction (emails, phones, dates, amounts, orgs, names) ✅
• SQLite insert/list/search/stats/delete ✅
• Duplicate detection via hashing ✅
• JSON/CSV/Excel export ✅

Not testable in that environment (no internet to install/download): the Streamlit UI render itself, llama-cpp-python LLM mode, and Whisper audio/video transcription. The code for all three is complete and follows each library's standard, stable API — install the optional dependencies above to light them up.

Security & privacy

No cloud calls, no telemetry, no analytics, no external API keys anywhere in this codebase. All processing and storage stays on the machine running streamlit run app.py.

License

GPLv3 — see LICENSE.

Future improvements

• Folder-watch mode for auto-processing new files
• Custom JSON schema builder in Settings
• Batch processing queue with pause/resume/retry
• Side-by-side original-document + JSON viewer with click-to-highlight

• Multi-user auth if deployed beyond a single local user

offline notes analyser

Getting started

To make it easy for you to get started with GitLab, here's a list of recommended next steps.

Already a pro? Just edit this README.md and make it your own. Want to make it easy? Use the template at the bottom!

Add your files

• Create or upload files
• Add files using the command line or push an existing Git repository with the following command:

cd existing_repo
git remote add origin https://code.swecha.org/sriharshini2901/offline-notes-analyser.git
git branch -M main
git push -uf origin main

Integrate with your tools

• Set up project integrations

Collaborate with your team

• Invite team members and collaborators
• Create a new merge request
• Automatically close issues from merge requests
• Enable merge request approvals
• Set auto-merge

Test and Deploy

Use the built-in continuous integration in GitLab.

• Get started with GitLab CI/CD
• Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)
• Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy
• Use pull-based deployments for improved Kubernetes management
• Set up protected environments

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Editing this README

When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to makeareadme.com for this template.

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