🚀 SummarIQ – AI-Powered Scientific Paper Summarizer

Extract • Understand • Summarize • Visualize (PDF + LaTeX) SummarIQ is an AI-powered system designed to summarize scientific research papers written in PDF or LaTeX. It extracts sections, equations, metadata, and produces structured summaries using transformer-based models like T5-small and BART-large.

This project includes:

• AI summarization models (T5, BART)
• LaTeX equation extraction & rendering
• FastAPI backend
• Gradio UI
• Docker deployment
• Prometheus + Grafana monitoring
• iOS UI prototypes
• Remote GPU inference (Kaggle)

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🎥 System Demo Video

Watch the system in action:

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Project Structure

├── App design
│   ├── Purple Pink Gradient Login Page Mobile Prototype (3)
│   │   ├── 1.jpg
│   │   ├── 2.jpg
│   │   └── 3.jpg
│   ├── Screen_1.png
│   ├── Screen_2.png
│   └── Screen_3.png
├── deployment
│   ├── docker-compose.yml
│   ├── Dockerfile
│   └── gradio_app.py
├── documentation
│   ├── plot_images
│   │   ├── Architechture_diagram.png
│   │   ├── fairness_explainability.png
│   │   ├── githubrepo.png
│   │   ├── sample_equation_output.png
│   │   ├── sample_summary_output.png
│   │   └── shap_summary.png
│   ├── Poster.pdf
│   ├── Project_report.pdf
│   └── SummarIQ_project_template.pdf
├── monitoring
│   ├── grafana
│   │   └── summariq_graphana_dashboard.json
│   ├── metrices_report
│   │   ├── metrics_report_valset.csv
│   │   ├── monitoring_images
│   │   │   ├── cpu_usage.png
│   │   │   ├── Fairlear_matrices.png
│   │   │   ├── feedback_latest_rating.png
│   │   │   ├── feedback_no_comments_total.png
│   │   │   ├── feedback_rating.png
│   │   │   ├── feedback_total.png
│   │   │   ├── feedback_with_comments_total.png
│   │   │   ├── localhost_metrices_log.png
│   │   │   ├── request_count.png
│   │   │   ├── request_latency_seconds.png
│   │   │   └── shap_values.png
│   │   └── structured_summary.csv
│   └── prometheus
│       ├── alert_rules.yml
│       └── prometheus.yml
├── Notebooks
│   ├── bart_train.ipynb
│   ├── kaggle_server_run.ipynb
│   ├── risk_management.ipynb
│   ├── t5_model_test.ipynb
│   ├── test_summarize.ipynb
│   ├── test_t5_small.ipynb
│   ├── training.ipynb
│   └── valset_test.ipynb
├── README.md
├── requirements_projects.txt
├── requirements.txt
├── src
    ├── __pycache__
    │   └── app.cpython-310.pyc
    ├── app.py
    ├── data
    │   ├── latex_extracted.json
    │   └── Test_pdf
    │       ├── 2404.08534v2.pdf
    │       ├── sm.pdf
    │       ├── sm.tex
    │       ├── Tower_V3.tex
    │       └── tower.pdf
    ├── equation_renderer
    │   ├── __pycache__
    │   │   ├── app.cpython-310.pyc
    │   │   ├── eqapp.cpython-310.pyc
    │   │   ├── quicklatex_app.cpython-310.pyc
    │   │   ├── quicklatex_renderer.cpython-310.pyc
    │   │   └── renderer.cpython-310.pyc
    │   ├── eqapp.py
    │   └── renderer.py
    ├── extractor
    │   ├── __pycache__
    │   │   └── latex_extractor.cpython-310.pyc
    │   ├── extract_usingcv.py
    │   └── latex_extractor.py
    ├── models
    │   ├── __pycache__
    │   │   └── summarizer_remote.cpython-310.pyc
    │   └── summarizer_remote.py
    ├── summarizer_kaggle.py
    ├── summarizer_local.py
    └── utils
        ├── load_data.py
        └── response_server
            ├── api_response.json
            └── response_without_T5server.json

Features

AI Summarization

• T5-small (fast, lightweight)
• BART-large (high accuracy)
• Section-wise summarization
• Handles long scientific text

LaTeX Equation Handling

• Detects inline & block equations
• Renders equations as images
• Ranks important equations

FastAPI Backend

• /summarize-latex endpoint
• PDF/LaTeX processing
• JSON structured output

Gradio UI

• Upload interface
• Real-time response
• Feedback collection

Deployment

• Dockerfile + docker-compose
• Separate services for API, Gradio, Prometheus
• Works on local + cloud environments

Monitoring

• Prometheus for metric scraping
• Grafana dashboards
• Custom metric: summariq_feedback_total

iOS Mobile App Prototype

• Screens included in App design/
• Designed for future live deployment

Installation

1. Clone the repository

git clone https://github.com/yourusername/SummarIQ.git
cd SummarIQ

2.Create a virtual environment

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

This project uses two separate requirements files:

• requirements_projects.txt(local development)

  pip install -r requirements_projects.txt
  

• requirements.txt(Docker/production)

  pip install -r requirements.txt
  

Run with Docker

• Build containers

  docker-compose build
  

• Start services

  docker-compose up
  

This starts:

• FastAPI → http://localhost:8000
• Gradio UI → http://localhost:7860
• Prometheus → http://localhost:9090
• Grafana → http://localhost:3000

Run Locally (without Docker)

Start FastAPI server

uvicorn app:app --reload --port 8000

Start Gradio UI

python gradio_app.py

API Usage

Endpoint

POST /summarize-latex

Example (cURL)

curl -X POST -F "file=@sample.tex" http://localhost:8000/summarize-latex

Output Example

utils/response_server/api_response.json

Model Pipeline

• Receive LaTeX/PDF
• Extract sections & equations
• Preprocess scientific text
• Summarize using T5/BART
• Produce structured JSON output
• Render LaTeX equations
• Display summary in Gradio / iOS UI

Monitoring (Prometheus + Grafana)

Metrics collected:

• API latency (p50, p90, p95)
• Total requests
• Feedback count summariq_feedback_total
• CPU/RAM usage
• Docker container performance
• Error rates Dashboards:
• Grafana → http://localhost:3000
• Prometheus → http://localhost:9090

iOS App Prototype

Located in App design/: Includes:

• Login screen
• Upload screen
• Processing screen
• Summary UI design Powered by SwiftUI (planned for Phase 2 deployment).

Roadmap

Phase 2

• Fine-tuned summarization (domain-specific)
• Full iOS app integration
• Deploy backend to AWS/GCP
• Real-time feedback storage in DB (MongoDB/PostgreSQL)
• Advanced Grafana dashboards
• Semantic equation understanding
• User accounts + authentication