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| title: CHB BERTopic V3 | |
| emoji: 🔬 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: "5.31.0" | |
| app_file: app.py | |
| pinned: false | |
| python_version: "3.11" | |
| short_description: BERTopic analysis app for Computers in Human Behavior | |
| # CHB BERTopic V3 | |
| This Space runs the final CHB BERTopic app for MDM Assignment-4. | |
| ## What is included | |
| - Gradio app entrypoint: `app.py` | |
| - Pipeline code: `tools.py` | |
| - Bundled corpus: `data/ComputersinHumanBehavior_TopicModelling_Export_7553_for_app.csv` | |
| - Local Space output folder: `outputs` | |
| ## Runtime | |
| The pipeline uses SPECTER2 embeddings, UMAP, HDBSCAN, BERTopic, and PAJAIS taxonomy mapping. | |
| LLM-assisted labeling, theme grouping, taxonomy reasoning, and narrative generation use Hugging Face inference. Add `HF_TOKEN` as a Space secret before running those phases. If the token is missing or inference fails, the app uses deterministic fallback logic where the pipeline supports it. | |
| ## Run Order | |
| Use the default input path shown in the app, then run the phases in order: | |
| 1. Load corpus | |
| 2. Discover abstract and title topics | |
| 3. Label topics | |
| 4. Consolidate themes | |
| 5. Map themes to PAJAIS | |
| 6. Generate comparison | |
| 7. Generate narrative | |
| ## Author | |
| Prasad Vijay Gade (2024301006) | MDM Assignment-4 | |