| title: "NOVIC: Unconstrained Open Vocabulary Image Classification" | |
| short_description: "Prompt-free open vocabulary classification of any image" | |
| license: gpl-3.0 | |
| emoji: 🖼️ | |
| colorFrom: green | |
| colorTo: yellow | |
| sdk: gradio | |
| python_version: "3.10" | |
| sdk_version: "5.35.0" | |
| app_file: app.py | |
| fullWidth: true | |
| header: default | |
| tags: | |
| - prompt-free | |
| - open vocabulary | |
| - image classification | |
| - computer vision | |
| - identification | |
| - generative | |
| pinned: true | |
| disable_embedding: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| To run the Gradio space locally, you first create a sandboxed Python 3.10 environment: | |
| ```bash | |
| python -m venv .venv # <-- Python 3.10 | |
| source .venv/bin/activate | |
| # OR... | |
| conda create -y -n novic_spaces python=3.10 | |
| conda activate novic_spaces | |
| ``` | |
| Then install `gradio` and any further requirements (**Note:** A pip-based install of `torch` is actually numerically different than a conda-based install of `pytorch`, which matters for the conda-based pretrained models, but for Hugging Face spaces we do not really have another choice): | |
| ```bash | |
| pip install gradio==5.35.0 -r requirements.txt | |
| ``` | |
| Now run the Gradio application: | |
| ```bash | |
| ./app.py | |
| # OR... | |
| gradio app.py | |
| ``` | |
| Open in a browser the URL that the application is running on (as shown in the CLI output), e.g. [http://127.0.0.1:7860](http://127.0.0.1:7860). | |