📄 AI PDF Analyst
Upload PDF → AI Summary → Q&A → Insights (Fully Offline Gen-AI Application)
An intelligent PDF analysis system built using Streamlit, FAISS, and local transformer models. It summarizes long PDFs, answers questions using RAG, and extracts insights — all without any paid APIs, making it perfect for students, AI practitioners, and interview portfolios.
🔗 https://frrmb4l1-8501.inc1.devtunnels.ms/
📄 PDF Upload & Text Extraction
Extracts full text from any PDF using pdfplumber.
🧠 AI Summary (Local LLM)
Generates high-quality summaries using BART/T5.
🔍 Ask Questions — RAG Powered
Embedding using SentenceTransformers Vector search using FAISS Local model generates answers
📊 Document Insights
Word count, text length, page count, and readability metrics.
🎨 Beautiful & Simple Streamlit UI
Custom CSS, Responsive UI, clean layout, dark theme look.
🖥️ No API Keys Required
Zero cost, zero rate limits, perfect for beginners & demos.
- Streamlit
- Custom CSS
- Transformers (T5/BART local models)
- SentenceTransformers
- FAISS (Vector Database)
- Retrieval-Augmented Generation (RAG)
- pdfplumber
- NumPy
- PyTorch
AI-PDF-Analyst/
│── app.py
│── requirements.txt
│── README.md
│── assets/
│ └── styles.css
│── modules/
│ ├── pdf_extractor.py
│ ├── summarizer.py
│ ├── rag_engine.py
│ └── insights.py
│── venv/
1️⃣ Clone the Repository git clone https://github.com//AI-PDF-Analyst.git cd AI-PDF-Analyst
2️⃣ Create Virtual Environment python -m venv venv venv\Scripts\activate # Windows
3️⃣ Install Dependencies pip install -r requirements.txt
4️⃣ Run the App streamlit run app.py
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PDF Processing
Extract text Split into chunks of 500–700 characters
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Embedding + FAISS Index
Convert chunks into embeddings using SentenceTransformers Store in FAISS for fast similarity search
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AI Summary
Local transformer (T5/BART) generates summary No API needed
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RAG Q&A
Convert question → embedding Retrieve best chunk Local model generates answer