Update app.py
Browse files
app.py
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#
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model_list = ["bert-base-uncased", "gpt2", "distilbert-base-uncased"]
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def add_model_to_list(new_model):
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if new_model and new_model not in model_list:
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model_list.append(new_model)
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return model_list
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allow_custom_value=True
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def create_config(model_name, num_labels, use_cache):
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if model_name not in model_list:
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model_list.append(model_name)
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config = AutoConfig.from_pretrained(model_name, num_labels=num_labels, use_cache=use_cache)
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return str(config)
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with gr.Blocks() as demo:
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gr.Markdown("## Config Class - Transformers")
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with gr.Row():
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new_model_input = gr.Textbox(label="Add a New Model", placeholder="Enter model name")
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add_model_button = gr.Button("Add Model")
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num_labels_input = gr.Number(label="Number of Labels", value=2)
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use_cache_input = gr.Checkbox(label="Use Cache", value=True)
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submit_button = gr.Button("Create Config")
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add_model_button.click(fn=add_model_to_list, inputs=new_model_input, outputs=model_dropdown)
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submit_button.click(fn=create_config, inputs=[model_dropdown, num_labels_input, use_cache_input], outputs=output_area)
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demo.launch()
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# Import necessary libraries
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import gradio as gr # Gradio is used to create web interfaces for Python scripts.
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from transformers import AutoConfig # AutoConfig is from the Hugging Face Transformers library, used to create configuration for various models.
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# A list of model names to start with. These are names of popular models from the Hugging Face library.
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model_list = ["bert-base-uncased", "gpt2", "distilbert-base-uncased"]
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# Function to add a new model to the list.
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def add_model_to_list(new_model):
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# Check if the new model is not already in the list and is not an empty string.
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if new_model and new_model not in model_list:
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model_list.append(new_model) # Add the new model to the list.
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return model_list
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# Function to create a configuration for the selected model.
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def create_config(model_name, num_labels, use_cache):
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# If the selected model is not in the list, add it (this is a safety check).
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if model_name not in model_list:
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model_list.append(model_name)
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# Create a configuration for the selected model using AutoConfig.
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config = AutoConfig.from_pretrained(model_name, num_labels=num_labels, use_cache=use_cache)
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return str(config) # Return the configuration as a string.
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# Start building the Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown("## Config Class - Transformers") # Display a title for the web interface.
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with gr.Row(): # Create a row in the interface to organize elements horizontally.
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# Dropdown menu to select a model.
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model_dropdown = gr.Dropdown(label="Select a Model", choices=model_list, value=model_list[0], allow_custom_value=True)
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# Textbox for users to input a new model name.
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new_model_input = gr.Textbox(label="Add a New Model", placeholder="Enter model name")
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# Button to add the new model to the dropdown list.
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add_model_button = gr.Button("Add Model")
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# Numeric input for the number of labels (used in the model configuration).
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num_labels_input = gr.Number(label="Number of Labels", value=2)
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# Checkbox for users to decide whether to use caching.
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use_cache_input = gr.Checkbox(label="Use Cache", value=True)
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# Textbox to display the generated configuration.
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output_area = gr.Textbox(label="Config Output")
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# Button to create the configuration.
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submit_button = gr.Button("Create Config")
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# When the "Add Model" button is clicked, call `add_model_to_list` function.
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add_model_button.click(fn=add_model_to_list, inputs=new_model_input, outputs=model_dropdown)
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# When the "Create Config" button is clicked, call `create_config` function.
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submit_button.click(fn=create_config, inputs=[model_dropdown, num_labels_input, use_cache_input], outputs=output_area)
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# Launch the Gradio interface.
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demo.launch()
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