| | --- |
| | title: Council Topics Classifier |
| | emoji: 🏛️ |
| | colorFrom: blue |
| | colorTo: green |
| | sdk: streamlit |
| | sdk_version: 1.36.0 |
| | app_file: src/streamlit_app.py |
| | pinned: false |
| | license: cc-by-4.0 |
| | --- |
| | |
| | # 🏛️ Council Topics Classifier |
| |
|
| | **Council Topics Classifier** is a system for automatically identifying topics in **Portuguese municipal meeting minutes discussion subjects**. |
| |
|
| | --- |
| |
|
| | ## 🎯 About |
| |
|
| | This demo showcases the classifier's ability to: |
| | - Detect topics in Portuguese municipal texts discussion subjects |
| | - Use a hybrid feature set (TF-IDF + BERTimbau embeddings) |
| | - Combine Logistic Regression and Gradient Boosting models in an adaptive weighted ensemble |
| | - Apply dynamic thresholds optimized per topic |
| | - Handle unbalanced topic distributions with active learning |
| |
|
| | --- |
| |
|
| | ## 📊 Model Performance |
| |
|
| | - **Model Architecture**: Logistic Regression + 3x Gradient Boosting models |
| | - **Features**: TF-IDF (1–3 n-grams) + BERTimbau contextual embeddings |
| | - **Adaptive weighting**: Rare topics get higher LogReg weight, common topics get higher GB weight |
| | - **Dynamic thresholds**: Optimized per topic using validation data |
| |
|
| | --- |
| |
|
| | ## 📝 Usage |
| |
|
| | 1. **Try Your Own Text**: Paste Portuguese municipal text in the input area |
| | 2. **Demo Examples**: Select from pre-loaded examples to see topic predictions |
| | 3. **View Results**: Confidence scores for each predicted topic are displayed interactively |
| |
|
| | --- |
| |
|
| | ## 🔧 Running Locally |
| |
|
| | ```bash |
| | pip install -r requirements.txt |
| | streamlit run app.py |
| | |
