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import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForTextToWaveform
def install_model(namemodel,tokenn,namemodelonxx):
model = AutoModelForTextToWaveform.from_pretrained(namemodel,token=tokenn)
namemodelonxxx=convert_to_onnx(model,namemodelonxx)
return namemodelonxxx
def convert_to_onnx(model,namemodelonxx):
vocab_size = model.text_encoder.embed_tokens.weight.size(0)
example_input = torch.randint(0, vocab_size, (1, 100), dtype=torch.long)
x=f"{namemodelonxx}.onnx"
torch.onnx.export(
model, # The model to be exported
example_input, # Example input for the model
x, # The filename for the exported ONNX model
opset_version=11, # Use an appropriate ONNX opset version
input_names=['input'], # Name of the input layer
output_names=['output'], # Name of the output layer
dynamic_axes={
'input': {0: 'batch_size', 1: 'sequence_length'}, # Dynamic axes for variable-length inputs
'output': {0: 'batch_size'}
}
)
return x
with gr.Blocks() as demo:
sesion_state = gr.State()
with gr.Column(scale=1, min_width=200,visible=True) as login_panal: # Login panel
gr.Markdown("## auth acess page")
token_login = gr.Textbox(label="token")
login_button = gr.Button("Login")
with gr.Row(scale=1, visible=False) as main_panel:
with gr.Column():
text_n_model=gr.Textbox(label="name model")
text_n_token=gr.Textbox(label="token")
text_n_onxx=gr.Textbox(label="name model onxx")
with gr.Column():
btn=gr.Button("convert")
label=gr.Label("return name model onxx")
btn.click(install_model,[text_n_model,text_n_token,text_n_onxx],[label])
login_button.click(self.login, inputs=[token_login], outputs=[login_panal,main_panel,sesion_state])
demo.load(self.load_demo, [sesion_state], [login_panal,main_panel])
demo.launch
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