import streamlit as st
from huggingface_hub import HfApi
import pandas as pd
from concurrent.futures import ThreadPoolExecutor, as_completed

# 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 {
            "username": username,
            "models": models,
            "datasets": datasets,
            "spaces": spaces
        }
    except Exception as e:
        return {"username": username, "error": str(e)}

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 = []
        status_bars = {}

        # Set up the progress bars for each user
        for username in username_list:
            status_bars[username] = st.progress(0, text=f"Fetching data for {username}...")

        def fetch_and_display(username):
            content = get_user_content(username)
            status_bars[username].progress(100, text=f"Data fetched for {username}")
            return content

        # Use ThreadPoolExecutor for concurrent execution
        with ThreadPoolExecutor(max_workers=len(username_list)) as executor:
            future_to_username = {executor.submit(fetch_and_display, username): username for username in username_list}
            for future in as_completed(future_to_username):
                result = future.result()
                results.append(result)

        st.markdown("### User Content Overview")
        for result in results:
            username = result["username"]
            if "error" not in result:
                profile_link = f"https://huggingface.co/{username}"
                profile_emoji = "🔗"
                
                models = [f"[{model.modelId}](https://huggingface.co/{model.modelId})" for model in result['models']]
                datasets = [f"[{dataset.id}](https://huggingface.co/datasets/{dataset.id})" for dataset in result['datasets']]
                spaces = [f"[{space.id}](https://huggingface.co/spaces/{space.id})" for space in result['spaces']]

                st.markdown(f"**{username}** {profile_emoji} [Profile]({profile_link})")
                st.markdown("**Models:**")
                st.markdown("\n".join(models) if models else "No models found")
                st.markdown("**Datasets:**")
                st.markdown("\n".join(datasets) if datasets else "No datasets found")
                st.markdown("**Spaces:**")
                st.markdown("\n".join(spaces) if spaces else "No spaces found")
                st.markdown("---")
            else:
                st.warning(f"{username}: {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 bars show the status of content retrieval for each user.
""")