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olas-prediction-leaderboard
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olas-prediction-leaderboard / README.md
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Trying a new gradio client library
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metadata
title: Leaderboard Gradio
emoji: 🦀
colorFrom: purple
colorTo: gray
sdk: gradio
python_version: 3.11.0
sdk_version: 4.29.0
app_file: app.py
pinned: false
license: apache-2.0

About this space

This HF space is a 'Gradio' based space with the configuration above.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Cloning the space repo

git clone https://huggingface.co/spaces/valory/olas-prediction-leaderboard

Updating the space repo

Update the space like any github repo Make sure you have git-lfs (since the CSVs are big and need LFS to push) Use similar git functions to push

Re-starting the space repo

There are two ways:

  1. Push a small commit
  2. Use the Restart this space from the settings page
  3. Use the Factory rebuild

Running the benchmark to contribute with new data

Run the benchmark locally using this repo Please see the readme on the repo on how to run Copy the relevant row/columns from summary.csv in the results folder Paste the CSV in the root of the olas-prediction-leaderboard HF space repo as formatted_data.csv Add the changes and push using git add, commit, and push commands Note: you just need to add the new data as a new row in the csv file. One row per model/tool.

Scripts of the repository

app.py

Starts the gradio app Also, kickstart the start.py There are 4 tabs:

  1. Benchmark Leaderboard: Shows the benchmark data
  2. About: Some FAQs
  3. Contribute: Some details on how to contribute
  4. Run the benchmark: Run the benchmark on any tools. You will have to provide your api keys

start.py

Setups the necessary things including - Olas-predict-benchmark repo, mech repo, and the required datasets for running the benchmark