Create app.py

app.py

@@ -0,0 +1,42 @@
+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")
+        
+        # Count trainable parameters
+        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        
+        # Count total parameters
+        total_params = sum(p.numel() for p in model.parameters())
+        
+        # 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()