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--- |
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title: Leaderboard Gradio |
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emoji: 🦀 |
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colorFrom: purple |
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colorTo: gray |
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sdk: gradio |
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python_version: 3.11.0 |
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sdk_version: 4.29.0 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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--- |
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# About this space |
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This HF space is a 'Gradio' based space with the configuration above. |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# Cloning the space repo |
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`git clone https://huggingface.co/spaces/valory/olas-prediction-leaderboard` |
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# Updating the space repo |
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Update the space like any github repo |
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Make sure you have git-lfs (since the CSVs are big and need LFS to push) |
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Use similar `git` functions to push |
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# Re-starting the space repo |
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There are two ways: |
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1. Push a small commit |
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2. Use the `Restart this space` from the [settings](https://huggingface.co/spaces/valory/olas-prediction-leaderboard/settings) page |
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3. Use the `Factory rebuild` |
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# Running the benchmark to contribute with new data |
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Run the benchmark locally using this [repo](https://github.com/valory-xyz/olas-predict-benchmark) |
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Please see the readme on the repo on how to run |
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Copy the relevant row/columns from `summary.csv` in the results folder |
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Paste the CSV in the root of the `olas-prediction-leaderboard` HF space repo as `formatted_data.csv` |
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Add the changes and push using `git add, commit, and push` commands |
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Note: you just need to add the new data as a new row in the csv file. One row per model/tool. |
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# Scripts of the repository |
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## app.py |
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Starts the gradio app |
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Also, kickstart the start.py |
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There are 4 tabs: |
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1. Benchmark Leaderboard: Shows the benchmark data |
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2. About: Some FAQs |
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3. Contribute: Some details on how to contribute |
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4. Run the benchmark: Run the benchmark on any tools. You will have to provide your api keys |
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## start.py |
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Setups the necessary things including - Olas-predict-benchmark repo, mech repo, and the required datasets for running the benchmark |
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