Spaces:
Runtime error
Runtime error
| import requests | |
| import json | |
| import pandas as pd | |
| from tqdm.auto import tqdm | |
| import streamlit as st | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| import streamlit.components.v1 as components | |
| def make_clickable(model_name): | |
| link = "https://huggingface.co/" + model_name | |
| return f'<a target="_blank" href="{link}">{model_name}</a>' | |
| def get_model_ids(): | |
| api = HfApi() | |
| models = api.list_models(filter="llama-leaderboard") | |
| model_ids = [x.modelId for x in models] | |
| return model_ids | |
| def get_metadata(model_id): | |
| try: | |
| readme_path = hf_hub_download(model_id, filename="README.md") | |
| return metadata_load(readme_path) | |
| except requests.exceptions.HTTPError: | |
| # 404 README.md not found | |
| return None | |
| def parse_metrics_accuracy(meta): | |
| if "model-index" not in meta: | |
| return None | |
| result = meta["model-index"][0]["results"] | |
| metrics = result[0]["metrics"] | |
| accuracy = metrics[0]["value"] | |
| return accuracy | |
| def get_data(): | |
| data = [] | |
| model_ids = get_model_ids() | |
| for model_id in tqdm(model_ids): | |
| meta = get_metadata(model_id) | |
| if meta is None: | |
| continue | |
| row = {} | |
| row["Model"] = model_id | |
| row["Accuracy"] = parse_metrics_accuracy(meta) | |
| data.append(row) | |
| return pd.DataFrame.from_records(data) | |
| dataframe = get_data() | |
| dataframe = dataframe.fillna("") | |
| st.markdown("# The 🦙 Leaderboard") | |
| st.markdown( | |
| f"This is a leaderboard of **{len(dataframe)}** llama classification models.\n\n" | |
| ) | |
| st.markdown( | |
| "This is the most comprehensive leaderboard of llama image classifier models published. You can try out the different models below" | |
| ) | |
| st.markdown( | |
| "Please click on the model's name to be redirected to its model card which includes documentation." | |
| ) | |
| # turn the model ids into clickable links | |
| dataframe["Model"] = dataframe["Model"].apply(make_clickable) | |
| dataframe = dataframe.sort_values(by=['Accuracy'], ascending=False) | |
| table_html = dataframe.to_html(escape=False) | |
| table_html = table_html.replace("<th>", '<th align="left">') # left-align the headers | |
| st.write(table_html, unsafe_allow_html=True) | |
| embed_gradio = components.html( | |
| """ | |
| <iframe src="https://hf.space/embed/osanseviero/llama-classifiers/+" frameBorder="0" height="1200" width="700" title="Gradio app" class="container p-0 flex-grow space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads"></iframe> | |
| """, | |
| height=1200, | |
| width=700 | |
| ) |