Intelligent document classifier
Web application that classifies documents using a multimodal LLM and generates outputs in Excel or Word.
01 Problem and context
Digitalizar documentos heterogéneos (facturas escaneadas, actas, planillas) exige clasificarlos y transcribirlos a mano hacia el formato correcto — un trabajo repetitivo y propenso a errores.
02 Solution
Un orquestador en cuatro etapas (clasificación, extracción, generación, resumen) usa visión LLM para imágenes escaneadas y clasificación textual para documentos ofimáticos, y enruta el resultado automáticamente: datos tabulares a Excel y narrativos a Word, con resumen ejecutivo incluido.
03 How it's built
A Flask-based web system that accepts documents (images, PDF, Word, Excel, PowerPoint, TXT, CSV), classifies them by type using the Groq API with the Llama 4 Scout multimodal model, extracts structured data, and generates an output file in Excel or Word based on the content. A central orchestrator coordinates the classification, extraction, generation, and executive summary pipeline with intelligent routing: tabular documents to .xlsx and narrative ones to .docx. It features a REST API and a drag-and-drop web interface, complete with automatic cleanup of temporary files.
04 Highlights
- Four-stage orchestrated pipeline (classification, extraction, generation, summarization) implemented in architecture/orchestrator.py.
- Multimodal classification: LLM vision for scanned images and text classification for PDF, Word, Excel, and PowerPoint.
- Automated output format routing: tabular data to Excel (.xlsx) and narrative documents to Word (.docx).
- REST API with processing, download, and health check endpoints, plus a drag-and-drop web interface built in vanilla JS.
- Temporary files with deferred automatic cleanup and configuration ready for serverless deployment on Vercel.