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PeopleModelsDatasets2X / app.py

awacke1

Update app.py

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	import streamlit as st
	from huggingface_hub import HfApi
	import pandas as pd
	import asyncio
	import base64
	from io import BytesIO

	# Initialize the Hugging Face API
	api = HfApi()

	# 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 = await asyncio.to_thread(api.list_models, author=username)
	datasets = await asyncio.to_thread(api.list_datasets, author=username)

	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 encode it as Base64 for download - 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>
	"""
	# Encode the HTML content as Base64 - like wrapping it in a fancy gift box! 🎁
	return base64.b64encode(html_content.encode()).decode()

	# 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()]

	# Run the asyncio loop to fetch all users - time to unleash the hounds! 🐕
	results = asyncio.run(fetch_all_users(username_list))

	st.markdown("### User Content Overview")
	for result in results:
	username = result["username"]
	if "error" not in result:
	with st.container():
	# Profile link - because everyone deserves their 15 seconds of fame! 🎤
	st.markdown(f"{username} [🔗 Profile](https://huggingface.co/{username})")

	# Create columns for models and datasets - divide and conquer! 🏛️
	col1, col2 = st.columns(2)

	# Models section with emoji - 🧠 because AI models are brainy! 🧠
	with col1:
	st.markdown("Models: 🧠")
	if result['models']:
	for model in result['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 emoji - 📚 because data is the foundation of AI! 📚
	with col2:
	st.markdown("Datasets: 📚")
	if result['datasets']:
	for dataset in result['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? 🍪")

	# Generate HTML page and provide download link - because who wouldn't want a custom webpage? 🌐
	html_page = generate_html_page(username, result['models'], result['datasets'])
	st.markdown(f"[📄 Download {username}'s HTML Page](data:text/html;base64,{html_page})")

	st.markdown("---")
	else:
	st.warning(f"{username}: {result['error']} - Looks like the AI needs a coffee break ☕")

	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!
	""")