fdaudens's picture
fdaudens HF staff
Update app.py
ffed801 verified
import gradio as gr
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from huggingface_hub import HfApi
from datetime import datetime
import numpy as np
def format_number(num):
"""Format large numbers with K, M suffix"""
if num >= 1e6:
return f"{num/1e6:.1f}M"
elif num >= 1e3:
return f"{num/1e3:.1f}K"
return str(num)
def fetch_stats():
"""Fetch all DeepSeek model statistics"""
api = HfApi()
# Fetch original models
original_models = [
"deepseek-ai/deepseek-r1",
"deepseek-ai/deepseek-r1-zero",
"deepseek-ai/deepseek-r1-distill-llama-70b",
"deepseek-ai/deepseek-r1-distill-qwen-32b",
"deepseek-ai/deepseek-r1-distill-qwen-14b",
"deepseek-ai/deepseek-r1-distill-llama-8b",
"deepseek-ai/deepseek-r1-distill-qwen-7b",
"deepseek-ai/deepseek-r1-distill-qwen-1.5b"
]
original_stats = []
for model_id in original_models:
try:
info = api.model_info(model_id)
original_stats.append({
'model_id': model_id,
'downloads_30d': info.downloads if hasattr(info, 'downloads') else 0,
'likes': info.likes if hasattr(info, 'likes') else 0
})
except Exception as e:
print(f"Error fetching {model_id}: {str(e)}")
model_types = ["adapter", "finetune", "merge", "quantized"]
base_models = [
"DeepSeek-R1",
"DeepSeek-R1-Zero",
"DeepSeek-R1-Distill-Llama-70B",
"DeepSeek-R1-Distill-Qwen-32B",
"DeepSeek-R1-Distill-Qwen-14B",
"DeepSeek-R1-Distill-Llama-8B",
"DeepSeek-R1-Distill-Qwen-7B",
"DeepSeek-R1-Distill-Qwen-1.5B"
]
derivative_stats = []
for base_model in base_models:
for model_type in model_types:
try:
models = list(api.list_models(
filter=f"base_model:{model_type}:deepseek-ai/{base_model}",
full=True
))
# Add each model to our stats
for model in models:
derivative_stats.append({
'base_model': f"deepseek-ai/{base_model}",
'model_type': model_type,
'model_id': model.id,
'downloads_30d': model.downloads if hasattr(model, 'downloads') else 0,
'likes': model.likes if hasattr(model, 'likes') else 0
})
except Exception as e:
print(f"Error fetching {model_type} models for {base_model}: {str(e)}")
# Create DataFrames
original_df = pd.DataFrame(original_stats, columns=['model_id', 'downloads_30d', 'likes'])
derivative_df = pd.DataFrame(derivative_stats, columns=['base_model', 'model_type', 'model_id', 'downloads_30d', 'likes'])
return original_df, derivative_df
def create_stats_html():
"""Create HTML for displaying statistics"""
original_df, derivative_df = fetch_stats()
# Create summary statistics
total_originals = len(original_df)
total_derivatives = len(derivative_df)
total_downloads_orig = original_df['downloads_30d'].sum()
total_downloads_deriv = derivative_df['downloads_30d'].sum()
# Create derivative type distribution chart
if len(derivative_df) > 0:
# Create distribution by model type
type_dist = derivative_df.groupby('model_type').agg({
'model_id': 'count',
'downloads_30d': 'sum'
}).reset_index()
type_dist = derivative_df.groupby('model_type').agg({
'model_id': 'count',
'downloads_30d': 'sum'
}).reset_index()
type_dist['model_type'] = type_dist['model_type'].str.capitalize()
type_dist = type_dist.sort_values('downloads_30d', ascending=True)
fig_types = go.Figure(data=[
go.Bar(
x=list(type_dist['model_type']), # Convert to list
y=list(type_dist['downloads_30d'].values), # Convert series to list of values
marker_color='rgb(55, 83, 109)'
)
])
fig_types.update_layout(
title='Downloads by Model Type',
#xaxis_title='Model Type',
yaxis_title='Downloads',
plot_bgcolor='white',
showlegend=False,
bargap=0.3
)
fig_types.update_traces(
text=type_dist['downloads_30d'].apply(format_number),
textposition='outside'
)
else:
fig_types = px.bar(title='No data available')
if len(derivative_df) > 0:
top_models = derivative_df.nlargest(10, 'downloads_30d')[
['model_id', 'model_type', 'downloads_30d', 'likes']
].copy() # Create a copy to avoid SettingWithCopyWarning
# Capitalize model types in the table
top_models['model_type'] = top_models['model_type'].str.capitalize()
# Format download numbers
top_models['downloads_30d'] = top_models['downloads_30d'].apply(format_number)
# Create clickable links for model_id
top_models['model_id'] = top_models['model_id'].apply(
lambda x: f'<a href="https://huggingface.co./{x}" target="_blank" onclick="window.open(\'https://huggingface.co./{x}\', \'_blank\')">{x}</a>'
)
else:
top_models = pd.DataFrame(columns=['model_id', 'model_type', 'downloads_30d', 'likes'])
summary_html = f"""
<div style='padding: 20px; background-color: #f5f5f5; border-radius: 10px; margin-bottom: 20px;'>
<h3 style='color: #333333;'>Summary Statistics</h3>
<p style='color: #333333;'>Derivative Models Downloads: {format_number(total_downloads_deriv)} ({total_derivatives} models)</p>
<p style='color: #333333;'>Original Models Downloads: {format_number(total_downloads_orig)} ({total_originals} models)</p>
<p style='color: #333333;'>Last Updated: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}</p>
</div>
"""
return summary_html, fig_types, top_models
def create_interface():
"""Create Gradio interface"""
with gr.Blocks(theme=gr.themes.Soft()) as interface:
gr.HTML("<h1 style='text-align: center;'>DeepSeek Models Stats</h1>")
with gr.Row():
with gr.Column():
summary_html = gr.HTML()
with gr.Column():
plot = gr.Plot()
with gr.Row():
table = gr.DataFrame(
headers=["Model ID", "Type", "Downloads (30d)", "Likes"],
label="Top 10 Most Downloaded Models",
wrap=True,
datatype=["html", "str", "str", "number"]
)
def update_stats():
summary, fig, top_models = create_stats_html()
return summary, fig, top_models
interface.load(update_stats,
outputs=[summary_html, plot, table])
return interface
# Create and launch the interface
demo = create_interface()
demo.launch()