app.py

import torch
import torch.onnx
import onnx
from VitsModelSplit.vits_model_only_d import  Vits_models_only_decoder
from VitsModelSplit.vits_model import VitsModel
import gradio as gr
import os
def create_file(file_path):
    # مسار الملف المؤقت
    #file_path = "DDFGDdd.onnx"
    
    # إنشاء ملف ONNX تجريبي في حالة عدم وجوده
    if not os.path.exists(file_path):
        #with open(file_path, "w") as file:
            #file.write("This is a test ONNX model file.")
        return None
    # إرجاع مسار الملف حتى يمكن تنزيله
    return file_path
class OnnxModelConverter:
    def __init__(self):
        self.model = None
 
        
    def download_file(self,file_path):
         if not os.path.exists(file_path):
            #with open(file_path, "w") as file:
                #file.write("This is a test ONNX model.")
            return None
         return file_path
          

    def convert(self, model_name, token, onnx_filename, conversion_type):
        """
        Main function to handle different types of model conversions.

        Args:
            model_name (str): Name of the model to convert.
            token (str): Access token for loading the model.
            onnx_filename (str): Desired filename for the ONNX output.
            conversion_type (str): Type of conversion ('decoder', 'only_decoder', or 'full_model').

        Returns:
            str: The path to the generated ONNX file.
        """
        if conversion_type == "decoder":
            return self.convert_decoder(model_name, token, onnx_filename)
        elif conversion_type == "only_decoder":
            return self.convert_only_decoder(model_name, token, onnx_filename)
        elif conversion_type == "full_model":
            return self.convert_full_model(model_name, token, onnx_filename)
        else:
            raise ValueError("Invalid conversion type. Choose from 'decoder', 'only_decoder', or 'full_model'.")

    def convert_decoder(self, model_name, token, onnx_filename):
        """
        Converts only the decoder part of the Vits model to ONNX format.

        Args:
            model_name (str): Name of the model to convert.
            token (str): Access token for loading the model.
            onnx_filename (str): Desired filename for the ONNX output.

        Returns:
            str: The path to the generated ONNX file.
        """
        model = VitsModel.from_pretrained(model_name, token=token)
        onnx_file = f"/tmp/{onnx_filename}.onnx"
        example_input  = torch.randn(1, 192, 10)


        torch.onnx.export(
            model.decoder,
            example_input,
            onnx_file,
            opset_version=11,
            input_names=['input'],
            output_names=['output'],
            dynamic_axes={'input': {0: 'batch_size', 1: 'sequence_length'}, 'output': {0: 'batch_size'}}
        )

        return  self.download_file(onnx_file)


    def convert_only_decoder(self, model_name, token, onnx_filename):
        """
        Converts only the decoder part of the Vits model to ONNX format.

        Args:
            model_name (str): Name of the model to convert.
            token (str): Access token for loading the model.
            onnx_filename (str): Desired filename for the ONNX output.

        Returns:
            str: The path to the generated ONNX file.
        """
        model = Vits_models_only_decoder.from_pretrained(model_name, token=token)
        onnx_file = f"/tmp/{onnx_filename}.onnx"

        vocab_size = model.text_encoder.embed_tokens.weight.size(0)
        example_input = torch.randint(0, vocab_size, (1, 100), dtype=torch.LongTensor)

        torch.onnx.export(
            model,
            example_input,
            onnx_file,
            opset_version=11,
            input_names=['input'],
            output_names=['output'],
            dynamic_axes={'input': {0: 'batch_size', 1: 'sequence_length'}, 'output': {0: 'batch_size'}}
        )

        return  self.download_file(onnx_file)

    def convert_full_model(self, model_name, token, onnx_filename):
        """
        Converts the full Vits model (including encoder and decoder) to ONNX format.

        Args:
            model_name (str): Name of the model to convert.
            token (str): Access token for loading the model.
            onnx_filename (str): Desired filename for the ONNX output.

        Returns:
            str: The path to the generated ONNX file.
        """
        model = VitsModel.from_pretrained(model_name, token=token)
        onnx_file = f"/tmp/{onnx_filename}.onnx"

        vocab_size = model.text_encoder.embed_tokens.weight.size(0)
        example_input = torch.randint(0, vocab_size, (1, 100), dtype=torch.long)

        torch.onnx.export(
            model,
            example_input,
            onnx_file,
            opset_version=11,
            input_names=['input'],
            output_names=['output'],
            dynamic_axes={'input': {0: 'batch_size', 1: 'sequence_length'}, 'output': {0: 'batch_size'}}
        )

        return  self.download_file(onnx_file)
    def starrt(self):
        with gr.Blocks() as demo:
            with gr.Row():
                  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")
                    choice = gr.Dropdown(choices=["decoder", "only_decoder", "full_model"], label="My Dropdown")

                  with gr.Column():

                    btn=gr.Button("convert")
                    label=gr.Label("return  name model onxx")
                    btn.click(self.convert,[text_n_model,text_n_token,text_n_onxx,choice],[gr.File(label="Download ONNX File")])
                    btx=gr.Textbox("namefile")
                    
                    download_button1=gr.Button("send")
                    download_button = gr.File(label="Download ONNX File")
                    download_button1.click(create_file,[btx],[download_button])
                    
                    #choice.change(fn=function_change, inputs=choice, outputs=label)
        return demo
c=OnnxModelConverter()
cc=c.starrt()
cc.launch(share=True)