STEP 1:
Invisora.AI is an AI-powered application that allows users to upload PDF reports, extract and understand key insights, generate interactive visualizations, and export summarized reports — all within a few clicks.
It combines the power of Retrieval-Augmented Generation (RAG), LLMs (Gemini 4.0), Streamlit UI, and Plotly for an intelligent and user-friendly data reporting solution.
✅ Streamlit Web Interface
Upload PDFs and interact with results using an intuitive UI.
✅ Smart PDF Parsing
PDF content is extracted and parsed using PyMuPDF, optimized for structured data extraction.
✅ Retrieval-Augmented Generation (RAG)
Relevant chunks of data are retrieved and sent to Gemini 4.0 using RetrievalQA for precise summaries.
✅ Interactive Charts
Data is converted into clear, interactive charts using Plotly.
✅ AI-Generated Summary
LLM-generated summaries of the uploaded content — concise and insightful.
✅ PDF Report Export
Summarized content and visuals are exported into a downloadable PDF using PDFKit.
Invisora.AI/ │ ├── app.py # Main Streamlit app ├── pdf_parser.py # PDF extraction using PyMuPDF ├── rag_engine.py # RAG pipeline with FAISS & RetrievalQA ├── visualizer.py # Chart generation with Plotly ├── report_generator.py # Generate and export PDF with PDFKit ├── requirements.txt # All Python dependencies └── README.md # Project documentation
| Tech / Tool | Purpose |
|---|---|
| Streamlit | UI and user interaction |
| PyMuPDF | PDF text and data extraction |
| LangChain | For RAG pipeline and LLM orchestration |
| Gemini 4.0 API | Large Language Model for summarization |
| Plotly | Generate interactive visualizations |
| PDFKit | Convert HTML output to downloadable PDF |
| FAISS | Vector store for chunk similarity search |
- Clone the repository
git clone https://github.com/yourusername/Invisora.AI.git
cd Invisora.AI
STEP 2 : Create a virtual environment & activate it
python -m venv .venv
source .venv/bin/activate # For Windows: .venv\Scripts\activate
STEP 3 :
Install dependencies
pip install -r requirements.txt
STEP 4:
Run the Streamlit app
streamlit run app.py
🧪 How It Works
Upload a PDF via the web UI
The PDF is parsed and broken into relevant text chunks
Chunks are embedded and passed through a FAISS vector store
Gemini 4.0 is queried using RetrievalQA to generate summaries
Plotly creates graphs from structured data
Summary + charts are converted into a downloadable PDF report
Sample Output
AI-generated charts and summaries based on a real uploaded PDF
Use Cases
Business report summarization
Market research insights
Automated data storytelling
Academic paper analysis
PDF analytics for non-technical users
outputs:
[
](assets/
)
[
](assets/
)
[
](assets/
)
[
](assets/
)
[
](assets/
)
[
](assets/
)
Author Suranjay Kumar B.Tech Computer Engineering | Marwadi University ✉️ suranjaykumar.119084@marwadiuniversity.ac.in
Contributions Welcome If you’d like to suggest improvements or report issues, feel free to open a pull request or issue.