Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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from transformers import AutoConfig
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#
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model_list = ["bert-base-uncased", "gpt2", "distilbert-base-uncased"]
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def
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if 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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def create_config(model_name, num_labels, use_cache):
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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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model_dropdown = gr.Dropdown(label="Select a Model", choices=model_list)
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add_model_button = gr.Button("Add Model")
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submit_button = gr.Button("Create Config")
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# Logic to create a config
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submit_button.click(fn=create_config, inputs=[model_dropdown, num_labels, use_cache], outputs=output)
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demo.launch()
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import gradio as gr
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from transformers import AutoConfig
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# Initialize with some common models
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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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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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model_dropdown = gr.Dropdown(label="Select a Model", choices=model_list, value=model_list[0])
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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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output_area = gr.Textbox(label="Config Output", readonly=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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