📄 AI Document Analyzer

AI-powered Document Analysis Web Application
Leverage the power of Large Language Models (LLMs) to instantly extract insights and answer questions from PDF and TXT documents.

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🚀 Overview

The AI Document Analyzer is a Streamlit-based web app that allows users to upload documents and interact with their content using cutting-edge AI models.
Whether you want to analyze reports, research papers, or any text-based content, this tool provides real-time, context-aware answers.

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✨ Features

• 📂 Universal Document Upload – Supports PDF and TXT formats
• 🤖 Dual AI Backend –
  • Ollama (Offline): Run locally without internet
  • Gemini (Free Cloud): Google cloud-based AI
• ⚡ Real-time AI Analysis – Ask questions and get instant answers
• 🖥 User-Friendly Interface – Clean, intuitive Streamlit UI
• 🔒 Safe and Lightweight – Minimal setup, optional cloud use

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📄 Supported File Types

File Type	Extraction Method
PDF	PyPDF2 library for structured text extraction
TXT	Direct UTF-8 file reading

⚠️ Note: Scanned or image-based PDFs may not extract text correctly.

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🤖 AI Models

1️⃣ Ollama (Offline)

• Runs locally on your machine
• Uses the tinyllama model
• Works without internet once installed
• Requires Ollama service running

2️⃣ Gemini (Free Cloud)

• Uses Google Gemini Flash model (gemini-flash-latest)
• Requires internet connection
• Requires GEMINI_API_KEY environment variable
• Free-tier friendly and stable for small/medium documents

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🛠 Installation

Prerequisites

• Python 3.7+
• pip package manager

Install Dependencies

pip install streamlit PyPDF2 google-genai

Optional: Ollama Setup

Install Ollama: https://ollama.ai/

Pull the required model:

ollama pull tinyllama

Start Ollama service:

ollama serve

Optional: Gemini Setup

Get a free API key: https://makersuite.google.com/app/apikey

Set environment variable:

# Windows
set GEMINI_API_KEY=your_api_key_here

# Linux/Mac
export GEMINI_API_KEY=your_api_key_here

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💡 Usage

Start the app:

streamlit run app.py

Open browser at http://localhost:8501

1. Upload PDF or TXT document
2. Select AI backend:
   • Ollama (Offline) → Local processing
   • Gemini (Free Cloud) → Cloud-based processing
3. Enter your question about the document
4. Click Analyze Document to get AI-powered answers

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🏗 Project Structure

doc-analysier/
├── app.py              # Main Streamlit app
├── README.md           # Documentation
└── .git/               # Git repository files

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🔑 Core Functions

Function	Description
extract_text_from_file()	Extract text from PDF/TXT
ask_ollama()	Query local Ollama LLM
ask_gemini()	Query Google Gemini API
main()	Handles Streamlit UI and workflow

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⚠️ Limitations

• PDF extraction may fail on scanned or image-based documents
• Ollama tinyllama model has ~3000 character context limit
• Gemini API has free-tier rate limits
• Very large documents may need truncation

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🛠 Troubleshooting

Issue	Solution
"PyPDF2 not installed"	Run pip install PyPDF2
"Ollama not found"	Install Ollama and start the service
"GEMINI_API_KEY not found"	Set environment variable correctly
"Unsupported file type"	Upload PDF or TXT only
"PDF text extraction fails"	Check if PDF is image-based or complex

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🏗 Development Stack

• Python & Streamlit → UI framework
• PyPDF2 → PDF text extraction
• google-genai → Gemini cloud AI integration
• Subprocess → Ollama CLI interaction
• Pathlib & Tempfile → File management

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📝 Future Enhancements

• Retrieval-Augmented Generation (RAG) with vector embeddings
• Multi-document support
• Summarization and highlighting
• Chat-like interface for document Q&A

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📜 License

MIT License – Open source and free to use, modify, and redistribute.