https://neuralgap-nlp-task-domo.streamlit.app/
This project implements a web application using Streamlit for document retrieval based on semantic similarity. It leverages the pre-trained sentence-transformer model paraphrase-MiniLM-L6-v2 for effective text embedding and retrieval.
- Python 3.11 (highly recommended)
- Streamlit (
pip install streamlit) - Sentence Transformers (
pip install sentence-transformers) - Torch (
pip install torch) (consider using a GPU-compatible version for faster processing) - NLTK (
pip install nltk)
1. Create a virtual environment (recommended for dependency isolation):
Creating a virtual environment helps isolate project dependencies and avoid conflicts with other Python installations on your system. Here's how to do it on Windows:
a. Open Command Prompt:
Press the Windows key, type "cmd", and press Enter.
b. Create the virtual environment:
python -m venv venv # Replace 'venv' with your desired virtual environment name
This command creates a directory named venv in your current working directory. This directory contains the isolated Python environment and its packages.
c. Activate the virtual environment:
venv\Scripts\activate.bat # Activate the virtual environment
Now, your command prompt will indicate that the virtual environment is active (usually denoted by (venv) before the prompt). This means you're working within the isolated environment and any packages installed here won't affect your system-wide Python installation.
2. Install dependencies:
Once your virtual environment is activated, proceed with installing the required packages:
pip install -r requirements.txt
3. Running the App:
a. Navigate to the project directory:
cd Neuralgap-NLP-Task
b. Run the application:
streamlit run nuralgap_task_demo_app.py