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import streamlit as st
from huggingface_hub import HfApi
import asyncio
import os
import plotly.express as px
# Initialize the Hugging Face API
api = HfApi()
# Directory to save the generated HTML files
HTML_DIR = "generated_html_pages"
if not os.path.exists(HTML_DIR):
os.makedirs(HTML_DIR)
# Default list of Hugging Face usernames - where all the magic begins! 🪄
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"
]
}
# Asynchronous function to fetch user content - because why wait when you can multitask? 🚀
async def fetch_user_content(username):
try:
# Fetch models and datasets - the stars of our show! 🌟
models = list(await asyncio.to_thread(api.list_models, author=username)) # Convert generator to list
datasets = list(await asyncio.to_thread(api.list_datasets, author=username)) # Convert generator to list
return {
"username": username,
"models": models,
"datasets": datasets
}
except Exception as e:
# Oops! Something went wrong - blame it on the gremlins! 😈
return {"username": username, "error": str(e)}
# Fetch all users concurrently - more hands (or threads) make light work! 💪
async def fetch_all_users(usernames):
tasks = [fetch_user_content(username) for username in usernames]
return await asyncio.gather(*tasks)
# Generate HTML content for a user and save it to a file - because who doesn't love a good download link? 💾
def generate_html_page(username, models, datasets):
html_content = f"""
<html>
<head>
<title>{username}'s Hugging Face Content</title>
</head>
<body>
<h1>{username}'s Hugging Face Profile</h1>
<p><a href="https://huggingface.co/{username}">🔗 Profile Link</a></p>
<h2>🧠 Models</h2>
<ul>
"""
for model in models:
model_name = model.modelId.split("/")[-1]
html_content += f'<li><a href="https://huggingface.co/{model.modelId}">{model_name}</a></li>'
html_content += """
</ul>
<h2>📚 Datasets</h2>
<ul>
"""
for dataset in datasets:
dataset_name = dataset.id.split("/")[-1]
html_content += f'<li><a href="https://huggingface.co/datasets/{dataset.id}">{dataset_name}</a></li>'
html_content += """
</ul>
</body>
</html>
"""
# Save the HTML content to a file
html_file_path = os.path.join(HTML_DIR, f"{username}.html")
with open(html_file_path, "w") as html_file:
html_file.write(html_content)
return html_file_path
# Cache the HTML file path using Streamlit's caching decorator
@st.cache_data(show_spinner=False)
def get_cached_html_file(username):
return generate_html_page(username, *get_user_content(username))
# Fetch user content from the API (without caching)
def get_user_content(username):
user_data = asyncio.run(fetch_user_content(username))
if "error" in user_data:
return None, user_data["error"]
return user_data["models"], user_data["datasets"]
# Streamlit app setup - the nerve center of our operation! 🎛️
st.title("Hugging Face User Content Display - Let's Automate Some Fun! 🎉")
# Convert the default users list to a string - because nobody likes typing out long lists! 📝
default_users_str = "\n".join(default_users["users"])
# Text area with default list of usernames - feel free to add your friends! 👥
usernames = st.text_area("Enter Hugging Face usernames (one per line):", value=default_users_str, height=300)
# Show User Content button - the big red button! (But actually it's blue) 🖱️
if st.button("Show User Content"):
if usernames:
username_list = [username.strip() for username in usernames.split('\n') if username.strip()]
# Collect statistics for Plotly graphs
stats = {"username": [], "models_count": [], "datasets_count": []}
st.markdown("### User Content Overview")
for username in username_list:
with st.container():
# Profile link - because everyone deserves their 15 seconds of fame! 🎤
st.markdown(f"**{username}** [🔗 Profile](https://huggingface.co/{username})")
# Generate HTML page and provide download link - because who wouldn't want a custom webpage? 🌐
models, datasets = get_user_content(username)
if models is None:
st.warning(f"{username}: {datasets} - Looks like the AI needs a coffee break ☕")
else:
html_file_path = get_cached_html_file(username)
st.markdown(f"[📄 Download {username}'s HTML Page]({html_file_path})")
# Add to statistics for Plotly graphs
stats["username"].append(username)
stats["models_count"].append(len(models))
stats["datasets_count"].append(len(datasets))
# Models section with expander - 🧠 because AI models are brainy! 🧠
with st.expander(f"🧠 Models ({len(models)})", expanded=False):
if models:
for model in models:
model_name = model.modelId.split("/")[-1]
st.markdown(f"- [{model_name}](https://huggingface.co/{model.modelId})")
else:
st.markdown("No models found. Did you check under the rug? 🕵️♂️")
# Datasets section with expander - 📚 because data is the foundation of AI! 📚
with st.expander(f"📚 Datasets ({len(datasets)})", expanded=False):
if datasets:
for dataset in datasets:
dataset_name = dataset.id.split("/")[-1]
st.markdown(f"- [{dataset_name}](https://huggingface.co/datasets/{dataset.id})")
else:
st.markdown("No datasets found. Maybe they’re still baking in the oven? 🍪")
st.markdown("---")
# Plotly graphs to visualize the number of models and datasets each user has
if stats["username"]:
st.markdown("### User Content Statistics")
# Plotting the number of models per user
fig_models = px.bar(x=stats["username"], y=stats["models_count"], labels={'x':'Username', 'y':'Number of Models'}, title="Number of Models per User")
st.plotly_chart(fig_models)
# Plotting the number of datasets per user
fig_datasets = px.bar(x=stats["username"], y=stats["datasets_count"], labels={'x':'Username', 'y':'Number of Datasets'}, title="Number of Datasets per User")
st.plotly_chart(fig_datasets)
else:
st.warning("Please enter at least one username. Don't be shy! 😅")
# Sidebar instructions - just in case you get lost! 🗺️
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 and datasets along with a link to their Hugging Face profile.
4. Download an HTML page for each user to use the absolute links offline!
5. Check out the statistics visualizations at the end!
""")
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