app.py

import streamlit as st
from huggingface_hub import HfApi
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
from functools import lru_cache
import time

st.set_page_config(page_title="HF Contributions", layout="wide")
api = HfApi()


# Cache for API responses
@lru_cache(maxsize=1000)
def cached_repo_info(repo_id, repo_type):
    return api.repo_info(repo_id=repo_id, repo_type=repo_type)


@lru_cache(maxsize=1000)
def cached_list_commits(repo_id, repo_type):
    return list(api.list_repo_commits(repo_id=repo_id, repo_type=repo_type))


@lru_cache(maxsize=100)
def cached_list_items(username, kind):
    if kind == "model":
        return list(api.list_models(author=username))
    elif kind == "dataset":
        return list(api.list_datasets(author=username))
    elif kind == "space":
        return list(api.list_spaces(author=username))
    return []


# Rate limiting
class RateLimiter:
    def __init__(self, calls_per_second=10):
        self.calls_per_second = calls_per_second
        self.last_call = 0

    def wait(self):
        current_time = time.time()
        time_since_last_call = current_time - self.last_call
        if time_since_last_call < (1.0 / self.calls_per_second):
            time.sleep((1.0 / self.calls_per_second) - time_since_last_call)
        self.last_call = time.time()


rate_limiter = RateLimiter()


# Function to fetch commits for a repository (optimized)
def fetch_commits_for_repo(repo_id, repo_type, username, selected_year):
    try:
        rate_limiter.wait()
        # Skip private/gated repos upfront
        repo_info = cached_repo_info(repo_id, repo_type)
        if repo_info.private or (hasattr(repo_info, 'gated') and repo_info.gated):
            return [], []

        # Get initial commit date
        initial_commit_date = pd.to_datetime(repo_info.created_at).tz_localize(None).date()
        commit_dates = []
        commit_count = 0

        # Add initial commit if it's from the selected year
        if initial_commit_date.year == selected_year:
            commit_dates.append(initial_commit_date)
            commit_count += 1

        # Get all commits
        commits = cached_list_commits(repo_id, repo_type)
        for commit in commits:
            commit_date = pd.to_datetime(commit.created_at).tz_localize(None).date()
            if commit_date.year == selected_year:
                commit_dates.append(commit_date)
                commit_count += 1

        return commit_dates, commit_count
    except Exception:
        return [], 0


# Function to get commit events for a user (optimized)
def get_commit_events(username, kind=None, selected_year=None):
    commit_dates = []
    items_with_type = []
    kinds = [kind] if kind else ["model", "dataset", "space"]

    for k in kinds:
        try:
            items = cached_list_items(username, k)
            items_with_type.extend((item, k) for item in items)
            repo_ids = [item.id for item in items]

            # Optimized parallel fetch with chunking
            chunk_size = 5  # Process 5 repos at a time
            for i in range(0, len(repo_ids), chunk_size):
                chunk = repo_ids[i:i + chunk_size]
                with ThreadPoolExecutor(max_workers=min(5, len(chunk))) as executor:
                    future_to_repo = {
                        executor.submit(fetch_commits_for_repo, repo_id, k, username, selected_year): repo_id
                        for repo_id in chunk
                    }
                    for future in as_completed(future_to_repo):
                        repo_commits, repo_count = future.result()
                        if repo_commits:  # Only extend if we got commits
                            commit_dates.extend(repo_commits)
        except Exception as e:
            st.warning(f"Error fetching {k}s for {username}: {str(e)}")

    # Create DataFrame with all commits
    df = pd.DataFrame(commit_dates, columns=["date"])
    if not df.empty:
        df = df.drop_duplicates()  # Remove any duplicate dates
    return df, items_with_type


# Calendar heatmap function (optimized)
def make_calendar_heatmap(df, title, year):
    if df.empty:
        st.info(f"No {title.lower()} found for {year}.")
        return

    # Optimize DataFrame operations
    df["count"] = 1
    df = df.groupby("date", as_index=False).sum()
    df["date"] = pd.to_datetime(df["date"])

    # Create date range more efficiently
    start = pd.Timestamp(f"{year}-01-01")
    end = pd.Timestamp(f"{year}-12-31")
    all_days = pd.date_range(start=start, end=end)

    # Optimize DataFrame creation and merging
    heatmap_data = pd.DataFrame({"date": all_days, "count": 0})
    heatmap_data = heatmap_data.merge(df, on="date", how="left", suffixes=("", "_y"))
    heatmap_data["count"] = heatmap_data["count_y"].fillna(0)
    heatmap_data = heatmap_data.drop("count_y", axis=1)

    # Calculate week and day of week more efficiently
    heatmap_data["dow"] = heatmap_data["date"].dt.dayofweek
    heatmap_data["week"] = (heatmap_data["date"] - start).dt.days // 7

    # Create pivot table more efficiently
    pivot = heatmap_data.pivot(index="dow", columns="week", values="count").fillna(0)

    # Optimize month labels calculation
    month_labels = pd.date_range(start, end, freq="MS").strftime("%b")
    month_positions = pd.date_range(start, end, freq="MS").map(lambda x: (x - start).days // 7)

    # Create custom colormap with specific boundaries
    from matplotlib.colors import ListedColormap, BoundaryNorm
    colors = ['#ebedf0', '#9be9a8', '#40c463', '#30a14e', '#216e39']  # GitHub-style green colors
    bounds = [0, 1, 3, 11, 31, float('inf')]  # Boundaries for color transitions
    cmap = ListedColormap(colors)
    norm = BoundaryNorm(bounds, cmap.N)

    # Create plot more efficiently
    fig, ax = plt.subplots(figsize=(12, 1.2))

    # Convert pivot values to integers to ensure proper color mapping
    pivot_int = pivot.astype(int)

    # Create heatmap with explicit vmin and vmax
    sns.heatmap(pivot_int, ax=ax, cmap=cmap, norm=norm, linewidths=0.5, linecolor="white",
                square=True, cbar=False, yticklabels=["M", "T", "W", "T", "F", "S", "S"])

    ax.set_title(f"{title}", fontsize=12, pad=10)
    ax.set_xlabel("")
    ax.set_ylabel("")
    ax.set_xticks(month_positions)
    ax.set_xticklabels(month_labels, fontsize=8)
    ax.set_yticklabels(ax.get_yticklabels(), rotation=0, fontsize=8)
    st.pyplot(fig)


# Sidebar
with st.sidebar:
    st.title("👤 Contributor")
    username = st.selectbox(
        "Select or type a username",
        options=["Gyaneshere", "facebook", "google", "stabilityai", "Salesforce", "tiiuae", "bigscience"],
        index=0
    )
    st.markdown("<div style='text-align: center; margin: 10px 0;'>OR</div>", unsafe_allow_html=True)
    custom = st.text_input("", placeholder="Enter custom username/org")
    if custom.strip():
        username = custom.strip()
    year_options = list(range(datetime.now().year, 2017, -1))
    selected_year = st.selectbox("🗓️ Year", options=year_options)

# Main Content
st.title("🤗 Hugging Face Contributions")
if username:
    with st.spinner("Fetching commit data..."):
        # Create a dictionary to store commits by type
        commits_by_type = {}
        commit_counts_by_type = {}

        # Fetch commits for each type separately
        for kind in ["model", "dataset", "space"]:
            try:
                items = cached_list_items(username, kind)
                repo_ids = [item.id for item in items]

                # Process repos in chunks
                chunk_size = 5
                total_commits = 0
                all_commit_dates = []

                for i in range(0, len(repo_ids), chunk_size):
                    chunk = repo_ids[i:i + chunk_size]
                    with ThreadPoolExecutor(max_workers=min(5, len(chunk))) as executor:
                        future_to_repo = {
                            executor.submit(fetch_commits_for_repo, repo_id, kind, username, selected_year): repo_id
                            for repo_id in chunk
                        }
                        for future in as_completed(future_to_repo):
                            repo_commits, repo_count = future.result()
                            if repo_commits:
                                all_commit_dates.extend(repo_commits)
                                total_commits += repo_count

                commits_by_type[kind] = all_commit_dates
                commit_counts_by_type[kind] = total_commits

            except Exception as e:
                st.warning(f"Error fetching {kind}s for {username}: {str(e)}")
                commits_by_type[kind] = []
                commit_counts_by_type[kind] = 0

        # Calculate total commits across all types
        total_commits = sum(commit_counts_by_type.values())

        st.subheader(f"{username}'s Activity in {selected_year}")
        st.metric("Total Commits", total_commits)

        # Create DataFrame for all commits
        all_commits = []
        for commits in commits_by_type.values():
            all_commits.extend(commits)
        all_df = pd.DataFrame(all_commits, columns=["date"])
        if not all_df.empty:
            all_df = all_df.drop_duplicates()  # Remove any duplicate dates

        make_calendar_heatmap(all_df, "All Commits", selected_year)

        # Metrics and heatmaps for each type
        col1, col2, col3 = st.columns(3)
        for col, kind, emoji, label in [
            (col1, "model", "🧠", "Models"),
            (col2, "dataset", "📦", "Datasets"),
            (col3, "space", "🚀", "Spaces")
        ]:
            with col:
                try:
                    total = len(cached_list_items(username, kind))
                    commits = commits_by_type.get(kind, [])
                    commit_count = commit_counts_by_type.get(kind, 0)
                    df_kind = pd.DataFrame(commits, columns=["date"])
                    if not df_kind.empty:
                        df_kind = df_kind.drop_duplicates()  # Remove any duplicate dates
                    st.metric(f"{emoji} {label}", total)
                    st.metric(f"Commits in {selected_year}", commit_count)
                    make_calendar_heatmap(df_kind, f"{label} Commits", selected_year)
                except Exception as e:
                    st.warning(f"Error processing {label}: {str(e)}")
                    st.metric(f"{emoji} {label}", 0)
                    st.metric(f"Commits in {selected_year}", 0)
                    make_calendar_heatmap(pd.DataFrame(), f"{label} Commits", selected_year)