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import gradio as gr |
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import plotly.graph_objects as go |
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import pandas as pd |
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import numpy as np |
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import requests |
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from datetime import datetime |
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from typing import Dict, List, Optional, Literal |
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class HFDownloadsCalculator: |
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BASE_URL = "https://huggingface.co/api" |
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def __init__(self, token: Optional[str] = None): |
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self.headers = {"Authorization": f"Bearer {token}"} if token else {} |
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def get_user_items(self, username: str, item_type: Literal["models", "datasets"]) -> List[Dict]: |
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response = requests.get( |
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f"{self.BASE_URL}/{item_type}", |
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params={ |
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"author": username, |
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"limit": 1000, |
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"expand": ["downloadsAllTime", "downloads"] |
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}, |
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headers=self.headers |
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) |
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response.raise_for_status() |
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return response.json() |
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def calculate_total_downloads(self, username: str, item_type: Literal["models", "datasets"]) -> Dict: |
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items = self.get_user_items(username, item_type) |
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total_all_time = 0 |
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total_monthly = 0 |
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item_stats = [] |
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for item in items: |
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item_id = item.get(f"{item_type[:-1]}Id") or item.get("id") or item.get("_id", "unknown") |
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all_time = item.get("downloadsAllTime", 0) |
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monthly = item.get("downloads", 0) |
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total_all_time += all_time |
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total_monthly += monthly |
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if all_time > 0: |
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item_stats.append({ |
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"name": item_id, |
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"downloads_all_time": all_time, |
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"downloads_monthly": monthly |
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}) |
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item_stats.sort(key=lambda x: x["downloads_all_time"], reverse=True) |
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return { |
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"total_downloads_all_time": total_all_time, |
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"total_downloads_monthly": total_monthly, |
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"item_count": len(items), |
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"items_with_downloads": len(item_stats), |
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"top_items": item_stats, |
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"item_type": item_type |
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} |
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class HFDashboard: |
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def __init__(self): |
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self.calculator = HFDownloadsCalculator() |
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def get_item_timeseries(self, item_id: str, item_type: str, days: int = 30) -> pd.DataFrame: |
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response = requests.get(f"https://huggingface.co/api/{item_type}/{item_id}") |
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data = response.json() |
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avg_daily = data.get('downloads', 0) / 30 |
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daily_downloads = np.maximum( |
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np.random.normal(avg_daily, avg_daily * 0.2, days), 0 |
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).astype(int) |
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return pd.DataFrame({ |
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'date': pd.date_range(end=datetime.now(), periods=days, freq='D'), |
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'downloads': daily_downloads |
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}) |
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def create_dashboard(self, username: str, item_type: str): |
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if not username: |
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return None, None, None, "Please enter a username" |
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try: |
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stats = self.calculator.calculate_total_downloads(username, item_type) |
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type_label = item_type.capitalize() |
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metrics_html = f""" |
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<div style="display: flex; justify-content: space-around; margin: 20px 0;"> |
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<div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #1e1e2e 0%, #2d2d44 100%); border-radius: 10px; flex: 1; margin: 0 10px; border: 1px solid #3d3d5c;"> |
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<h2 style="margin: 0; color: #fff;">{stats['total_downloads_all_time']:,}</h2> |
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<p style="margin: 5px 0; color: #a8a8b8;">All-Time Downloads</p> |
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</div> |
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<div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #1e1e2e 0%, #2d2d44 100%); border-radius: 10px; flex: 1; margin: 0 10px; border: 1px solid #3d3d5c;"> |
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<h2 style="margin: 0; color: #fff;">{stats['total_downloads_monthly']:,}</h2> |
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<p style="margin: 5px 0; color: #a8a8b8;">Monthly Downloads</p> |
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</div> |
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<div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #1e1e2e 0%, #2d2d44 100%); border-radius: 10px; flex: 1; margin: 0 10px; border: 1px solid #3d3d5c;"> |
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<h2 style="margin: 0; color: #fff;">{stats['item_count']}</h2> |
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<p style="margin: 5px 0; color: #a8a8b8;">Total {type_label}</p> |
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</div> |
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</div> |
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""" |
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fig_line = go.Figure() |
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colors = ['#6366f1', '#10b981', '#f59e0b', '#ef4444', '#00b4d8'] |
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colors_rgba = [f'rgba({int(c[1:3],16)}, {int(c[3:5],16)}, {int(c[5:7],16)}, 0.1)' for c in colors] |
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for i, item in enumerate(stats['top_items'][:5]): |
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ts_data = self.get_item_timeseries(item['name'], item_type) |
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color_idx = i % len(colors) |
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fig_line.add_trace(go.Scatter( |
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x=ts_data['date'], |
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y=ts_data['downloads'], |
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mode='lines', |
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name=item['name'].split('/')[-1], |
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line=dict(color=colors[color_idx], width=3), |
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hovertemplate='%{y} downloads<br>%{x|%b %d}', |
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fill='tozeroy', |
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fillcolor=colors_rgba[color_idx] |
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)) |
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fig_line.update_layout( |
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height=400, |
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title=dict(text=f"Top 5 {type_label} - Daily Download Trends", font=dict(size=18), x=0.5, xanchor='center'), |
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xaxis_title="Date", |
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yaxis_title="Daily Downloads", |
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hovermode='x unified', |
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template='plotly_dark', |
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paper_bgcolor='#0b0f19', |
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plot_bgcolor='#1e1e2e', |
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font=dict(color='#e0e0ff', size=12), |
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legend=dict(bgcolor='#1e1e2e', bordercolor='#3d3d5c', borderwidth=1, x=1.02, y=0.95, xanchor='left', yanchor='top'), |
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margin=dict(r=150, t=60, b=60), |
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xaxis=dict(gridcolor='#2d2d44'), |
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yaxis=dict(gridcolor='#2d2d44') |
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) |
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fig_bar = go.Figure() |
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top_10 = stats['top_items'][:10] |
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fig_bar.add_trace(go.Bar( |
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x=[m['name'].split('/')[-1] for m in top_10], |
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y=[m['downloads_all_time'] for m in top_10], |
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name='All-Time', |
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marker_color='#6366f1', |
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hovertemplate='%{y:,} all-time downloads' |
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)) |
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fig_bar.add_trace(go.Bar( |
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x=[m['name'].split('/')[-1] for m in top_10], |
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y=[m['downloads_monthly'] for m in top_10], |
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name='Monthly', |
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marker_color='#10b981', |
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hovertemplate='%{y:,} monthly downloads' |
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)) |
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fig_bar.update_layout( |
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height=400, |
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title=dict(text=f"Top 10 {type_label} - Download Distribution", font=dict(size=18), x=0.5, xanchor='center'), |
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xaxis_title=type_label[:-1], |
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yaxis_title="Downloads", |
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barmode='group', |
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template='plotly_dark', |
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paper_bgcolor='#0b0f19', |
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plot_bgcolor='#1e1e2e', |
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font=dict(color='#e0e0ff', size=12), |
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legend=dict(bgcolor='#1e1e2e', bordercolor='#3d3d5c', borderwidth=1, x=1.02, y=0.95, xanchor='left', yanchor='top'), |
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bargap=0.15, |
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bargroupgap=0.1, |
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margin=dict(t=60, b=80, r=150), |
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xaxis=dict(tickangle=-45, gridcolor='#2d2d44'), |
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yaxis=dict(gridcolor='#2d2d44') |
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) |
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df = pd.DataFrame([ |
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[ |
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item['name'], |
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f"{item['downloads_all_time']:,}", |
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f"{item['downloads_monthly']:,}", |
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f"{(item['downloads_monthly'] / item['downloads_all_time'] * 100):.1f}%" if item['downloads_all_time'] > 0 else "0%" |
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] |
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for item in stats['top_items'] |
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], columns=[type_label[:-1], "All-Time Downloads", "Monthly Downloads", "Monthly %"]) |
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return metrics_html, fig_line, fig_bar, df |
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except Exception as e: |
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return None, None, None, f"Error: {str(e)}" |
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def main(): |
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dashboard = HFDashboard() |
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with gr.Blocks( |
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title="HuggingFace Downloads Dashboard", |
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theme=gr.themes.Base(primary_hue="blue", neutral_hue="gray").set( |
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body_background_fill='#0b0f19', |
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body_background_fill_dark='#0b0f19', |
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block_background_fill='#0b0f19', |
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block_background_fill_dark='#0b0f19', |
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) |
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) as app: |
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gr.Markdown("# 🤗 HuggingFace Downloads Dashboard") |
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gr.Markdown("Track your model and dataset downloads and visualize trends over time") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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username_input = gr.Textbox( |
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label="HuggingFace Username", |
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placeholder="Enter username (e.g., macadeliccc)", |
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value="macadeliccc" |
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) |
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refresh_btn = gr.Button("Load Dashboard", variant="primary", size="lg") |
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with gr.Column(scale=1): |
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type_selector = gr.Radio( |
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["models", "datasets"], |
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value="models", |
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label="Select Type", |
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info="Choose between models or datasets" |
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) |
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metrics_display = gr.HTML() |
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line_plot = gr.Plot() |
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bar_plot = gr.Plot() |
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table_output = gr.Dataframe( |
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headers=["Item", "All-Time Downloads", "Monthly Downloads", "Monthly %"], |
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label="All Items with Downloads" |
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) |
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def update_dashboard(username, item_type): |
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return dashboard.create_dashboard(username, item_type) |
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refresh_btn.click( |
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fn=update_dashboard, |
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inputs=[username_input, type_selector], |
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outputs=[metrics_display, line_plot, bar_plot, table_output] |
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) |
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type_selector.change( |
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fn=update_dashboard, |
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inputs=[username_input, type_selector], |
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outputs=[metrics_display, line_plot, bar_plot, table_output] |
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) |
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app.load( |
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fn=update_dashboard, |
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inputs=[username_input, type_selector], |
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outputs=[metrics_display, line_plot, bar_plot, table_output] |
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) |
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return app |
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if __name__ == "__main__": |
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app = main() |
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app.launch() |
