🔬 Scientific Named Entity Explorer

Interactive Streamlit app for mining scientific literature using pretrained Named-Entity-Recognition (NER) models.

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📌 Overview

The Scientific Named Entity Explorer is an interactive tool for automated extraction of scientific entities from research abstracts.
It supports upload from Excel sheets and provides:

• 🧠 Pretrained scientific NER models
• 🎛️ Rich filters & configurable thresholds
• 🔍 Entity-level visualization as colored chips
• 📊 Summaries and downloadable reports
• ⚡ Fast local execution via Streamlit

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📚 Table of Contents

• Prerequisites
• Quick Start
• Step-by-Step Installation
• Using the App
• Troubleshooting
• Screenshots
• Contributing

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🧩 Prerequisites

• Python 3.10+
• git (optional)
• Internet connection (initial model download)

⚠️ Note: Pretrained models and PyTorch wheels are large. Ensure sufficient disk space.

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🚀 Quick Start

git clone https://github.com/Rkl2023/Named_entity_algorithm_project.git
cd Named_entity_algorithm_project
python3 install.py
source .venv/bin/activate  # Windows: .venv\Scripts\activate
streamlit run app.py

🛠️ Step-by-Step Installation

1. Clone the project

git clone https://github.com/k25063738/Named_entity_algorithm_project
cd Named_entity_algorithm_project

2. Create + activate virtual environment

python3 -m venv .venv
source .venv/bin/activate      # macOS/Linux
# .venv\Scripts\activate       # Windows

3. Install dependencies

pip install --upgrade pip
pip install -r requirements.txt

4. Launch the app

streamlit run app.py

Prefer automation?
python3 install.py handles everything for you.

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🕹️ Using the App

1. Activate your virtual environment
2. Run:

   streamlit run app.py

3. Open Streamlit tab (default: http://localhost:8501)
4. Select NER model + confidence threshold
5. Upload Excel file (Title + Abstract required)
6. Click Run NER Extraction to:
   • Process abstracts with model
   • Display entities as colored chips
   • Filter by keyword / entity type
7. Download results as CSV / JSON / Excel

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🐞 Troubleshooting

🔧 pip cannot find a matching torch wheel

pip install --upgrade pip

Apple Silicon users may need wheels from:
👉 https://pytorch.org/

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🐢 Model download slow

First run downloads large models.
Subsequent runs use:

~/.cache/huggingface/

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❌ streamlit not found

Activate virtual environment:

source .venv/bin/activate

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❗ Excel upload fails

Ensure required columns:

• Title
• Abstract

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🧮 Performance issues

• Split very large Excel files
• Increase confidence threshold

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📸 Example Screenshots

🖥️ Main Interface

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🤝 Contributing

Feel free to open issues or submit PRs.

⭐ If you find this project useful, consider giving it a star!