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| title: Interactive Topic Modeler | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| python_version: "3.10" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Interactive Topic Modeler | |
| This is a premium, lightweight, interactive topic modeling tool designed specifically for computational social science and humanities courses. | |
| ## Features | |
| - **Flexible Data Input**: Upload `.csv`, `.xlsx`, or `.txt` datasets directly. For tabular data, select any target text column for analysis. | |
| - **Multiple Algorithms**: | |
| - **LDA (Latent Dirichlet Allocation)**: Fast, traditional topic modeling running locally on CPU. | |
| - **NMF (Non-Negative Matrix Factorization)**: Classic linear algebraic topic modeling. | |
| - **BERTopic (API Mode)**: Utilizes advanced neural embeddings via Hugging Face Serverless API with your personal access token. | |
| - **Interactive Visualizations**: View plotly bar charts displaying keywords and importance weights per topic. | |
| - **Download Labeled Dataset**: Automatically outputs a downloadable version of the original dataset annotated with the assigned topics and document probabilities. | |