app.py

import gradio as gr
from transformers import AutoModel
import torch

def count_parameters(model_path):
    try:
        # Load model on CPU
        model = AutoModel.from_pretrained(model_path, device_map="cpu", trust_remote_code=True)
        
        # Count trainable parameters (accounting for weight tying)
        unique_params = {}
        for name, p in model.named_parameters():
            if p.requires_grad:
                unique_params[p.data_ptr()] = (name, p.numel())
        
        trainable_params = sum(numel for _, numel in unique_params.values())
        
        # Count total parameters (accounting for weight tying)
        unique_params = {}
        for name, p in model.named_parameters():
            unique_params[p.data_ptr()] = (name, p.numel())
        
        total_params = sum(numel for _, numel in unique_params.values())
        
        # Format numbers with commas for readability
        return f"""
Total Parameters: {total_params:,}
Trainable Parameters: {trainable_params:,}
        """
    except Exception as e:
        return f"Error loading model: {str(e)}"

# Create Gradio interface
demo = gr.Interface(
    fn=count_parameters,
    inputs=gr.Textbox(
        label="Enter Hugging Face Model Path",
        placeholder="e.g., bert-base-uncased"
    ),
    outputs=gr.Textbox(label="Parameter Count"),
    title="Hugging Face Model Parameter Counter",
    description="Enter a Hugging Face model path to see its parameter count.",
    examples=[
        ["bert-base-uncased"],
        ["gpt2"],
        ["roberta-base"]
    ]
)

if __name__ == "__main__":
    demo.launch()