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import streamlit as st |
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from huggingface_hub import HfApi |
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import asyncio |
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import os |
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import plotly.express as px |
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api = HfApi() |
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HTML_DIR = "generated_html_pages" |
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if not os.path.exists(HTML_DIR): |
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os.makedirs(HTML_DIR) |
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default_users = { |
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"users": [ |
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"awacke1", "rogerxavier", "jonatasgrosman", "kenshinn", "Csplk", "DavidVivancos", |
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"cdminix", "Jaward", "TuringsSolutions", "Severian", "Wauplin", |
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"phosseini", "Malikeh1375", "gokaygokay", "MoritzLaurer", "mrm8488", |
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"TheBloke", "lhoestq", "xw-eric", "Paul", "Muennighoff", |
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"ccdv", "haonan-li", "chansung", "lukaemon", "hails", |
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"pharmapsychotic", "KingNish", "merve", "ameerazam08", "ashleykleynhans" |
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] |
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} |
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async def fetch_user_content(username): |
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try: |
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models = list(await asyncio.to_thread(api.list_models, author=username)) |
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datasets = list(await asyncio.to_thread(api.list_datasets, author=username)) |
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return { |
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"username": username, |
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"models": models, |
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"datasets": datasets |
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} |
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except Exception as e: |
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return {"username": username, "error": str(e)} |
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async def fetch_all_users(usernames): |
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tasks = [fetch_user_content(username) for username in usernames] |
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return await asyncio.gather(*tasks) |
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def generate_html_page(username, models, datasets): |
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html_content = f""" |
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<html> |
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<head> |
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<title>{username}'s Hugging Face Content</title> |
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</head> |
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<body> |
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<h1>{username}'s Hugging Face Profile</h1> |
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<p><a href="https://huggingface.co/{username}">π Profile Link</a></p> |
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<h2>π§ Models</h2> |
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<ul> |
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""" |
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for model in models: |
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model_name = model.modelId.split("/")[-1] |
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html_content += f'<li><a href="https://huggingface.co/{model.modelId}">{model_name}</a></li>' |
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html_content += """ |
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</ul> |
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<h2>π Datasets</h2> |
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<ul> |
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""" |
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for dataset in datasets: |
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dataset_name = dataset.id.split("/")[-1] |
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html_content += f'<li><a href="https://huggingface.co/datasets/{dataset.id}">{dataset_name}</a></li>' |
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html_content += """ |
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</ul> |
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</body> |
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</html> |
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""" |
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html_file_path = os.path.join(HTML_DIR, f"{username}.html") |
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with open(html_file_path, "w") as html_file: |
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html_file.write(html_content) |
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return html_file_path |
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@st.cache_data(show_spinner=False) |
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def get_cached_html_file(username): |
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return generate_html_page(username, *get_user_content(username)) |
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def get_user_content(username): |
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user_data = asyncio.run(fetch_user_content(username)) |
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if "error" in user_data: |
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return None, user_data["error"] |
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return user_data["models"], user_data["datasets"] |
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st.title("Hugging Face User Content Display - Let's Automate Some Fun! π") |
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default_users_str = "\n".join(default_users["users"]) |
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usernames = st.text_area("Enter Hugging Face usernames (one per line):", value=default_users_str, height=300) |
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if st.button("Show User Content"): |
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if usernames: |
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username_list = [username.strip() for username in usernames.split('\n') if username.strip()] |
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stats = {"username": [], "models_count": [], "datasets_count": []} |
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st.markdown("### User Content Overview") |
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for username in username_list: |
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with st.container(): |
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st.markdown(f"**{username}** [π Profile](https://huggingface.co/{username})") |
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models, datasets = get_user_content(username) |
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if models is None: |
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st.warning(f"{username}: {datasets} - Looks like the AI needs a coffee break β") |
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else: |
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html_file_path = get_cached_html_file(username) |
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st.markdown(f"[π Download {username}'s HTML Page]({html_file_path})") |
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stats["username"].append(username) |
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stats["models_count"].append(len(models)) |
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stats["datasets_count"].append(len(datasets)) |
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with st.expander(f"π§ Models ({len(models)})", expanded=False): |
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if models: |
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for model in models: |
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model_name = model.modelId.split("/")[-1] |
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st.markdown(f"- [{model_name}](https://huggingface.co/{model.modelId})") |
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else: |
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st.markdown("No models found. Did you check under the rug? π΅οΈββοΈ") |
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with st.expander(f"π Datasets ({len(datasets)})", expanded=False): |
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if datasets: |
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for dataset in datasets: |
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dataset_name = dataset.id.split("/")[-1] |
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st.markdown(f"- [{dataset_name}](https://huggingface.co/datasets/{dataset.id})") |
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else: |
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st.markdown("No datasets found. Maybe theyβre still baking in the oven? πͺ") |
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st.markdown("---") |
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if stats["username"]: |
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st.markdown("### User Content Statistics") |
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fig_models = px.bar(x=stats["username"], y=stats["models_count"], labels={'x':'Username', 'y':'Number of Models'}, title="Number of Models per User") |
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st.plotly_chart(fig_models) |
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fig_datasets = px.bar(x=stats["username"], y=stats["datasets_count"], labels={'x':'Username', 'y':'Number of Datasets'}, title="Number of Datasets per User") |
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st.plotly_chart(fig_datasets) |
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else: |
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st.warning("Please enter at least one username. Don't be shy! π
") |
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st.sidebar.markdown(""" |
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## How to use: |
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1. The text area is pre-filled with a list of Hugging Face usernames. You can edit this list or add more usernames. |
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2. Click 'Show User Content'. |
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3. View the user's models and datasets along with a link to their Hugging Face profile. |
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4. Download an HTML page for each user to use the absolute links offline! |
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5. Check out the statistics visualizations at the end! |
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""") |
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