| # 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. | |
| # A list of model names to start with. These are names of popular models from the Hugging Face library. | |
| model_list = ["bert-base-uncased", "gpt2", "distilbert-base-uncased"] | |
| # 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 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=use_cache) | |
| return str(config) # Return the configuration as a string. | |
| # Start building the Gradio interface | |
| with gr.Blocks() 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() |