import streamlit as st import requests import base64 import os import asyncio from huggingface_hub import HfApi import plotly.express as px import zipfile # Importing zipfile to handle ZIP operations # Initialize the Hugging Face API api = HfApi() # Directory to save the downloaded and generated files HTML_DIR = "generated_html_pages" if not os.path.exists(HTML_DIR): os.makedirs(HTML_DIR) # Directory to save the ZIP files ZIP_DIR = "generated_zips" if not os.path.exists(ZIP_DIR): os.makedirs(ZIP_DIR) # 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" ] } # Asynchronous function to fetch user content using Hugging Face API async def fetch_user_content(username): try: # Fetch models and datasets models = list(await asyncio.to_thread(api.list_models, author=username)) datasets = list(await asyncio.to_thread(api.list_datasets, author=username)) return { "username": username, "models": models, "datasets": datasets } except Exception as e: return {"username": username, "error": str(e)} # Function to download the user page using requests def download_user_page(username): url = f"https://huggingface.co/{username}" try: response = requests.get(url) response.raise_for_status() html_content = response.text html_file_path = os.path.join(HTML_DIR, f"{username}.html") with open(html_file_path, "w", encoding='utf-8') as html_file: html_file.write(html_content) return html_file_path, None except Exception as e: return None, str(e) # Function to create a ZIP archive of the HTML files @st.cache_resource def create_zip_of_files(files): zip_name = "HuggingFace_User_Pages.zip" # Renamed for clarity zip_file_path = os.path.join(ZIP_DIR, zip_name) with zipfile.ZipFile(zip_file_path, 'w') as zipf: for file in files: # Add each HTML file to the ZIP archive with its basename zipf.write(file, arcname=os.path.basename(file)) return zip_file_path # Function to generate a download link for the ZIP file @st.cache_resource def get_zip_download_link(zip_file): with open(zip_file, 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() href = f'📥 Download All HTML Pages as ZIP' return href # Function to fetch all users concurrently async def fetch_all_users(usernames): tasks = [fetch_user_content(username) for username in usernames] return await asyncio.gather(*tasks) # Function to get all HTML files for the selected users def get_all_html_files(usernames): html_files = [] errors = {} for username in usernames: html_file, error = download_user_page(username) if html_file: html_files.append(html_file) else: errors[username] = error return html_files, errors # Streamlit app setup st.title("Hugging Face User Page Downloader & Zipper 📄➕📦") # Text area with default list of usernames user_input = st.text_area( "Enter Hugging Face usernames (one per line):", value="\n".join(default_users["users"]), height=300 ) # Show User Content button if st.button("Show User Content"): if user_input: username_list = [username.strip() for username in user_input.split('\n') if username.strip()] # Fetch user content asynchronously user_data_list = asyncio.run(fetch_all_users(username_list)) # Collect statistics for Plotly graphs stats = {"username": [], "models_count": [], "datasets_count": []} # List to store paths of successfully downloaded HTML files successful_html_files = [] st.markdown("### User Content Overview") for user_data in user_data_list: username = user_data["username"] with st.container(): # Profile link st.markdown(f"**{username}** [🔗 Profile](https://huggingface.co/{username})") if "error" in user_data: st.warning(f"{username}: {user_data['error']} - Something went wrong! ⚠️") else: models = user_data["models"] datasets = user_data["datasets"] # Download the user's HTML page html_file_path, download_error = download_user_page(username) if html_file_path: successful_html_files.append(html_file_path) st.success(f"✅ Successfully downloaded {username}'s page.") else: st.error(f"❌ Failed to download {username}'s page: {download_error}") # Add to statistics stats["username"].append(username) stats["models_count"].append(len(models)) stats["datasets_count"].append(len(datasets)) # Display models 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. 🤷‍♂️") # Display datasets 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. 🤷‍♀️") st.markdown("---") # Check if there are any successfully downloaded HTML files to zip if successful_html_files: # Create a ZIP archive of the HTML files zip_file_path = create_zip_of_files(successful_html_files) # Generate a download link for the ZIP file zip_download_link = get_zip_download_link(zip_file_path) st.markdown(zip_download_link, unsafe_allow_html=True) else: st.warning("No HTML files were successfully downloaded to create a ZIP archive.") # Plotly graphs to visualize the number of models and datasets each user has if stats["username"]: st.markdown("### User Content Statistics") # 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) # 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 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 each user's models and datasets along with a link to their Hugging Face profile. 4. **Download a ZIP archive** containing all the HTML pages by clicking the download link. 5. Check out the statistics visualizations below! """)