File size: 3,922 Bytes
9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa c2bed9a 0bedbaa 9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa 9774217 0bedbaa c2bed9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import streamlit as st
from huggingface_hub import HfApi
import pandas as pd
# Default list of Hugging Face usernames
default_users = {
"users": [
"awacke1", "rogerxavier", "jonatasgrosman", "kenshinn", "Csplk", "DavidVivancos",
"cdminix", "Jaward", "TuringsSolutions", "Severian", "Wauplin",
"phosseini", "Malikeh1375", "gokaygokay", "MoritzLaurer", "mrm8488",
"TheBloke", "lhoestq", "xw-eric", "Paul", "Muennighoff",
"ccdv", "haonan-li", "chansung", "lukaemon", "hails",
"pharmapsychotic", "KingNish", "merve", "ameerazam08", "ashleykleynhans"
]
}
api = HfApi()
def get_user_content(username):
try:
# Fetch models, datasets, and spaces associated with the user
models = api.list_models(author=username)
datasets = api.list_datasets(author=username)
spaces = api.list_spaces(author=username)
return {
"models": models,
"datasets": datasets,
"spaces": spaces
}
except Exception as e:
st.error(f"Error fetching content for {username}: {str(e)}")
return None
st.title("Hugging Face User Content Display")
# Convert the default users list to a string
default_users_str = "\n".join(default_users["users"])
# Text area with default list of usernames
usernames = st.text_area("Enter Hugging Face usernames (one per line):", value=default_users_str, height=300)
if st.button("Show User Content"):
if usernames:
username_list = [username.strip() for username in usernames.split('\n') if username.strip()]
results = []
progress_bar = st.progress(0)
for i, username in enumerate(username_list):
content = get_user_content(username)
if content:
profile_link = f"https://huggingface.co/{username}"
profile_emoji = "🔗"
models = [f"[{model.modelId}](https://huggingface.co/{model.modelId})" for model in content['models']]
datasets = [f"[{dataset.id}](https://huggingface.co/datasets/{dataset.id})" for dataset in content['datasets']]
spaces = [f"[{space.id}](https://huggingface.co/spaces/{space.id})" for space in content['spaces']]
results.append({
"Hugging Face": username,
"Profile Link": f"[{profile_emoji} Profile]({profile_link})",
"Models": models,
"Datasets": datasets,
"Spaces": spaces
})
else:
results.append({"Hugging Face": username, "Error": "User content not found"})
progress_bar.progress((i + 1) / len(username_list))
st.markdown("### User Content Overview")
for result in results:
if "Error" not in result:
st.markdown(f"**{result['Hugging Face']}** {result['Profile Link']}")
st.markdown("**Models:**")
st.markdown("\n".join(result["Models"]) if result["Models"] else "No models found")
st.markdown("**Datasets:**")
st.markdown("\n".join(result["Datasets"]) if result["Datasets"] else "No datasets found")
st.markdown("**Spaces:**")
st.markdown("\n".join(result["Spaces"]) if result["Spaces"] else "No spaces found")
st.markdown("---")
else:
st.warning(f"{result['Hugging Face']}: {result['Error']}")
else:
st.warning("Please enter at least one username.")
st.sidebar.markdown("""
## How to use:
1. The text area is pre-filled with a list of Hugging Face usernames. You can edit this list or add more usernames.
2. Click 'Show User Content'.
3. View the user's models, datasets, and spaces along with a link to their Hugging Face profile.
4. The progress bar shows the status of content retrieval.
""")
|