app/README.md

QualiVec Streamlit Demo

This Streamlit application provides an interactive demonstration of the QualiVec library for qualitative content analysis using LLM embeddings.

Features

• Interactive Data Upload: Upload your own CSV files for reference and labeled data
• Model Configuration: Choose from different pre-trained embedding models
• Threshold Optimization: Automatically find the optimal similarity threshold
• Real-time Classification: See classification results as they happen
• Comprehensive Evaluation: View detailed performance metrics and visualizations
• Bootstrap Analysis: Get confidence intervals for robust evaluation

How to Run

Option 1: Local Installation

1. Install Dependencies:

   pip install -e .
   

2. Run the App:

   cd app
   uv run run_demo.py
   

3. Access the App: Open your browser and navigate to http://localhost:8501

Option 2: Docker

1. Build the Docker Image:

   docker build -t qualivec .
   

2. Run the Docker Container:

   docker run --rm -p 8501:8501 qualivec
   

3. Access the App: Open your browser and navigate to http://localhost:8501

Note: The Docker option provides a containerized environment with all dependencies pre-installed, making it easier to run the application without setting up a local Python environment.

Data Format Requirements

Reference Data (CSV)

Your reference data should contain:

• tag: The class/category label
• sentence: The example text for that category

Example:

tag,sentence
Positive,This is absolutely fantastic!
Negative,This is terrible and disappointing
Neutral,This is okay I guess

Labeled Data (CSV)

Your labeled data should contain:

• sentence: The text to be classified
• Label: The true class/category (for evaluation)

Example:

sentence,Label
I love this product so much!,Positive
Not very good quality,Negative
Average product nothing special,Neutral

Navigation

The app is organized into 5 main sections:

1. 🏠 Home: Overview and introduction to QualiVec
2. 📊 Data Upload: Upload your reference and labeled data files
3. 🔧 Configuration: Set up embedding models and parameters
4. 🎯 Classification: Run the classification and optimization process
5. 📈 Results: View detailed results and download outputs