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59e348f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | # 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**:
```bash
pip install -e .
```
2. **Run the App**:
```bash
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**:
```bash
docker build -t qualivec .
```
2. **Run the Docker Container**:
```bash
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:
```csv
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:
```csv
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 |