app.py

import streamlit as st
import pandas as pd

# Path to the CSV file
csv_file_path = "formatted_data.csv"

# Reading the CSV file
df = pd.read_csv(csv_file_path)

# Displaying the DataFrame in the Streamlit app with enhanced interactivity
st.title('Olas Predict Benchmark')
st.markdown('## Leaderboard showing the performance of Olas Predict tools on the Autocast dataset.')
st.markdown("<style>.big-font {font-size:20px !important;}</style>", unsafe_allow_html=True)
st.markdown('Use the table below to interact with the data and explore the performance of different tools.', unsafe_allow_html=True)
st.dataframe(df.style.format(precision=2))

st.markdown("""
## Benchmark Overview
- The benchmark evaluates the performance of Olas Predict tools on the Autocast dataset.
- The dataset has been refined to enhance the evaluation of the tools.
- The leaderboard shows the performance of the tools based on the refined dataset.
- The script to run the benchmark is available in the repo [here](https://github.com/valory-xyz/olas-predict-benchmark).

## How to run your tools on the benchmark
- Fork the repo [here](https://github.com/valory-xyz/olas-predict-benchmark).
- Git init the submodules and update the submodule to get the latest dataset `mech` tool.
    - `git submodule init`
    - `git submodule update --remote --recursive`
- Include your tool in the `mech/packages` directory accordingly.
    - Guidelines on how to include your tool can be found [here](xxx).
- Run the benchmark script.

## Dataset Overview
This project leverages the Autocast dataset from the research paper titled ["Forecasting Future World Events with Neural Networks"](https://arxiv.org/abs/2206.15474). 
The dataset has undergone further refinement to enhance the performance evaluation of Olas mech prediction tools. 
Both the original and refined datasets are hosted on HuggingFace.

### Refined Dataset Files
- You can find the refined dataset on HuggingFace [here](https://huggingface.co./datasets/valory/autocast).
- `autocast_questions_filtered.json`: A JSON subset of the initial autocast dataset.
- `autocast_questions_filtered.pkl`: A pickle file mapping URLs to their respective scraped documents within the filtered dataset.
- `retrieved_docs.pkl`: Contains all the scraped texts.

### Filtering Criteria
To refine the dataset, we applied the following criteria to ensure the reliability of the URLs:
- URLs not returning HTTP 200 status codes are excluded.
- Difficult-to-scrape sites, such as Twitter and Bloomberg, are omitted.
- Links with less than 1000 words are removed.
- Only samples with a minimum of 5 and a maximum of 20 working URLs are retained.

### Scraping Approach
The content of the filtered URLs has been scraped using various libraries, depending on the source:
- `pypdf2` for PDF URLs.
- `wikipediaapi` for Wikipedia pages.
- `requests`, `readability-lxml`, and `html2text` for most other sources.
- `requests`, `beautifulsoup`, and `html2text` for BBC links.
""", unsafe_allow_html=True)