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	# Import necessary libraries
	import gradio as gr # Gradio is used to create web interfaces for Python scripts.
	from transformers import AutoConfig # AutoConfig is from the Hugging Face Transformers library, used to create configuration for various models.import gradio as gr
	from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel

	# A list of model names to start with. These are names of popular models from the Hugging Face library.
	model_list = ["EleutherAI/gpt-neo-125m", "bert-base-uncased", "TigerResearch/tigerbot-70b-chat-4bit-v2", "openai/whisper-large-v3", "openai/shap-e","google/switch-c-2048", "gpt2","mosaicml/mpt-30b",
	"google/flan-t5-small","mosaicml/mpt-7b","distilbert-base-uncased,","microsoft/speecht5_tts","suno/bark-small","suno/bark",]

	# Function to add a new model to the list.
	def add_model_to_list(new_model):
	# Check if the new model is not already in the list and is not an empty string.
	if new_model and new_model not in model_list:
	model_list.append(new_model) # Add the new model to the list.
	return model_list

	# Function to create a configuration for the selected model.
	def create_config(model_name, num_labels, use_cache):
	if isinstance(model_name, list):
	model_name = model_name[0] # Take the first model name from the list if it's a list

	# Ensure num_labels is an integer
	num_labels = int(num_labels)

	# If the selected model is not in the list, add it (this is a safety check).
	if model_name not in model_list:
	model_list.append(model_name)

	# Create a configuration for the selected model using AutoConfig.
	config = AutoConfig.from_pretrained(model_name, num_labels=num_labels, use_cache='true')
	return str(config) # Return the configuration as a string.

	# Start building the Gradio interface
	with gr.Group(elem_id="UI-conf"):
	custom_css = """
	.gradio-container {
	background-color: #f0f0f0; /* Light grey background */
	font-family: Arial, sans-serif;
	}
	.gradio-textbox {
	border: 2px solid #4CAF50; /* Green border for textboxes */
	border-radius: 5px;
	}
	.gradio-button {
	background-color: #4CAF50; /* Green background for buttons */
	color: white;
	border: none;
	border-radius: 5px;
	padding: 10px 20px;
	text-align: center;
	text-decoration: none;
	display: inline-block;
	font-size: 16px;
	margin: 4px 2px;
	cursor: pointer;
	}
	.gradio-button:hover {
	background-color: #45a049; /* Darker green on hover */
	}
	"""

	with gr.Blocks(css=custom_css) as demo:
	gr.Markdown("## Config Class - Transformers") # Display a title for the web interface.
	with gr.Row(): # Create a row in the interface to organize elements horizontally.
	# Dropdown menu to select a model.
	model_dropdown = gr.Dropdown(label="Select a Model", choices=model_list, value=model_list[0], allow_custom_value=True)
	# Textbox for users to input a new model name.
	new_model_input = gr.Textbox(label="Add a New Model", placeholder="Enter model name")
	# Button to add the new model to the dropdown list.
	add_model_button = gr.Button("Add Model")
	# Numeric input for the number of labels (used in the model configuration).
	num_labels_input = gr.Number(label="Number of Labels", value=2)
	# Checkbox for users to decide whether to use caching.
	use_cache_input = gr.Checkbox(label="Use Cache", value='true')
	# Textbox to display the generated configuration.
	output_area = gr.Textbox(label="Config Output",)
	# Button to create the configuration.
	submit_button = gr.Button("Create Config")

	# When the "Add Model" button is clicked, call `add_model_to_list` function.
	add_model_button.click(fn=add_model_to_list, inputs=new_model_input, outputs=model_dropdown)
	# When the "Create Config" button is clicked, call `create_config` function.
	submit_button.click(fn=create_config, inputs=[model_dropdown, num_labels_input, use_cache_input], outputs=output_area)

	# Launch the Gradio interface.
	demo.launch()