macadeliccc commited on
Commit
cd6b836
·
1 Parent(s): ccaeb2a

add app.py

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Files changed (3) hide show
  1. README.md +5 -7
  2. app.py +249 -0
  3. requirements.txt +3 -0
README.md CHANGED
@@ -1,14 +1,12 @@
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  ---
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- title: Hf Downloads Dashboard
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- emoji: 👀
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- colorFrom: blue
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- colorTo: blue
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  sdk: gradio
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  sdk_version: 5.37.0
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  app_file: app.py
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  pinned: false
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  license: apache-2.0
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  short_description: Track your lifetime/monthly downloads on all your models
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  ---
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+ title: HF Downloads Dashboard
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+ emoji: 🏆
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+ colorFrom: green
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+ colorTo: purple
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  sdk: gradio
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  sdk_version: 5.37.0
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  app_file: app.py
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  pinned: false
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  license: apache-2.0
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  short_description: Track your lifetime/monthly downloads on all your models
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+ ---
 
 
app.py ADDED
@@ -0,0 +1,249 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
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+
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+
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+ class HFDownloadsCalculator:
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+
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+ BASE_URL = "https://huggingface.co/api"
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+
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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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+
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+ def get_user_models_with_all_time_downloads(self, username: str) -> List[Dict]:
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+ response = requests.get(
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+ f"{self.BASE_URL}/models",
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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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+
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+ def calculate_total_downloads(self, username: str) -> Dict:
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+ models = self.get_user_models_with_all_time_downloads(username)
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+
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+ total_all_time = 0
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+ total_monthly = 0
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+ model_stats = []
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+
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+ for model in models:
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+ model_id = model.get("modelId") or model.get("id") or model.get("_id", "unknown")
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+ all_time = model.get("downloadsAllTime", 0)
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+ monthly = model.get("downloads", 0)
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+
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+ total_all_time += all_time
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+ total_monthly += monthly
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+
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+ if all_time > 0:
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+ model_stats.append({
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+ "name": model_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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+
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+ model_stats.sort(key=lambda x: x["downloads_all_time"], reverse=True)
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+
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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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+ "model_count": len(models),
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+ "models_with_downloads": len(model_stats),
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+ "top_models": model_stats
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+ }
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+
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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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+
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+ def get_model_timeseries(self, model_id: str, days: int = 30) -> pd.DataFrame:
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+ response = requests.get(f"https://huggingface.co/api/models/{model_id}")
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+ data = response.json()
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+
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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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+
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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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+
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+ def create_dashboard(self, username: str):
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+ if not username:
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+ return None, None, None, "Please enter a username"
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+
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+ try:
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+ stats = self.calculator.calculate_total_downloads(username)
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+
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+ # Metrics HTML
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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['model_count']}</h2>
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+ <p style="margin: 5px 0; color: #a8a8b8;">Total Models</p>
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+ </div>
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+ </div>
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+ """
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+
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+ # Line chart for time series
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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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+
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+ for i, model in enumerate(stats['top_models'][:5]):
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+ ts_data = self.get_model_timeseries(model['name'])
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+ color_idx = i % len(colors)
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+
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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=model['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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+
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+ fig_line.update_layout(
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+ height=400,
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+ title=dict(text="Top 5 Models - 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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+
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+ # Bar chart for download distribution
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+ fig_bar = go.Figure()
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+ top_10 = stats['top_models'][:10]
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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_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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+
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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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+
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+ fig_bar.update_layout(
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+ height=400,
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+ title=dict(text="Top 10 Models - Download Distribution", font=dict(size=18), x=0.5, xanchor='center'),
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+ xaxis_title="Model",
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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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+
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+ # Create table
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+ df = pd.DataFrame([
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+ [
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+ model['name'],
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+ f"{model['downloads_all_time']:,}",
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+ f"{model['downloads_monthly']:,}",
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+ f"{(model['downloads_monthly'] / model['downloads_all_time'] * 100):.1f}%" if model['downloads_all_time'] > 0 else "0%"
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+ ]
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+ for model in stats['top_models']
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+ ], columns=["Model", "All-Time Downloads", "Monthly Downloads", "Monthly %"])
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+
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+ return metrics_html, fig_line, fig_bar, df
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+
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+ except Exception as e:
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+ return None, None, None, f"Error: {str(e)}"
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+
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+
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+ def main():
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+ dashboard = HFDashboard()
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+
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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 downloads and visualize trends over time")
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+
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+ with gr.Row():
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+ with gr.Column():
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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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+
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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=["Model", "All-Time Downloads", "Monthly Downloads", "Monthly %"],
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+ label="All Models with Downloads"
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+ )
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+
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+ def update_dashboard(username):
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+ return dashboard.create_dashboard(username)
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+
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+ refresh_btn.click(
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+ fn=update_dashboard,
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+ inputs=[username_input],
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+ outputs=[metrics_display, line_plot, bar_plot, table_output]
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+ )
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+
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+ app.load(
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+ fn=update_dashboard,
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+ inputs=[username_input],
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+ outputs=[metrics_display, line_plot, bar_plot, table_output]
242
+ )
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+
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+ return app
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+
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+
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+ if __name__ == "__main__":
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+ app = main()
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+ app.launch()
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ gradio
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+ plotly
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+ datetime