📄 Smart Document Tagger & Classifier

A full-stack intelligent document management system that automatically:

• Extract text from uploaded documents (.pdf / .txt)
• Classify documents into predefined enterprise categories
• Move files to category-specific folders
• Display classification results and extracted text in a web dashboard

🚀 Built using Django + Gemini API + PyMuPDF. Designed for real-world AI/ML applications in Enterprise Information Management.

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✨ Features

✅ Upload .pdf or .txt documents
✅ Automatically extract text using PyMuPDF
✅ Classify document using Gemini (Google AI) with zero training
✅ Dynamically store predicted label (e.g., Invoice, HR, Legal, Resume, etc.)
✅ Auto-organize files into folders based on their classification
✅ Minimal, functional web UI using Django templates
✅ Scalable structure: each phase builds toward a production-grade AI pipeline

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🧠 Document Categories

Currently supported categories:

• Invoice
• Legal
• HR
• Technical
• Resume
• Report
• Marketing
• Financial Statement
• Email
• Policy Document
• Meeting Minutes
• Presentation
• Contract
• Product Manual
• Other (fallback)

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⚙️ Tech Stack

Layer	Tech Used
Backend	Django 5.2, Python 3.10+
AI/NLP	Google Gemini Pro (via google-genai SDK), PyMuPDF
Storage	Local filesystem (Django MEDIA_ROOT)
Frontend	Django Templates (HTML/CSS), Bootstrap (optional)
Deployment	Dev: runserver; Prod: WSGI/ASGI

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🧪 How It Works

1. User uploads a .pdf or .txt file through the web form
2. System extracts raw text using PyMuPDF
3. The extracted text is passed to Gemini Pro (via API)
4. Gemini returns a category label based on the content
5. The file is moved to a subdirectory for that category (e.g., documents/HR/)
6. All document data is saved and displayed in the dashboard

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🛠️ Local Setup Instructions

1. Clone the repository

git clone https://github.com/yourusername/smart-doc-ai.git
cd smart-doc-ai