app

app.py

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+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)}")
+    
+    # Fetch derivative models - using the tag format that works
+    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:
+                # Get models for this type
+                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()
+        
+        # Format model types to be more readable
+        type_dist['model_type'] = type_dist['model_type'].str.capitalize()
+        
+        # Create bar chart with better formatting
+        fig_types = px.bar(
+            type_dist, 
+            x='model_type', 
+            y='downloads_30d',
+            title='Downloads by Model Type',
+            labels={
+                'downloads_30d': 'Downloads (last 30 days)', 
+                'model_type': 'Model Type'
+            },
+            text=type_dist['downloads_30d'].apply(format_number)  # Add value labels
+        )
+        
+        # Update layout for better readability
+        fig_types.update_traces(textposition='outside')
+        fig_types.update_layout(
+            uniformtext_minsize=8,
+            uniformtext_mode='hide',
+            xaxis_tickangle=0,
+            yaxis_title="Downloads",
+            plot_bgcolor='white',
+            bargap=0.3
+        )
+        
+    else:
+        # Create empty figure if no data
+        fig_types = px.bar(title='No data available')
+    
+    # Create top models table
+    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)
+    else:
+        top_models = pd.DataFrame(columns=['model_id', 'model_type', 'downloads_30d', 'likes'])
+    
+    
+    # Format the summary statistics
+    summary_html = f"""
+    <div style='padding: 20px; background-color: #f5f5f5; border-radius: 10px; margin-bottom: 20px;'>
+        <h3>Summary Statistics</h3>
+        <p>Derivative Models Downloads: {format_number(total_downloads_deriv)} ({total_derivatives} models)</p>
+        <p>Original Models Downloads: {format_number(total_downloads_orig)} ({total_originals} models)</p>
+        <p>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"
+                )
+        
+        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()

requirements.txt

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+gradio
+pandas
+plotly
+huggingface_hub