prepostprocess.ddd

WebGPU abstraction using .ddd with Gradio
preprocess_ddd and postprocess_webgpu as a ddd to WebGPU Gradio component abstraction
given a library for WebGPU interaction this adds support for .ddd files as input and output components to simplfy calls. Here's a basic template to illustrate the idea see: ddd.ddd

import gradio as gr import your_webgpu_library as wgpu # Replace 'your_webgpu_library' with the actual library name

Custom preprocessor for .ddd files

def preprocess_ddd(file_path): # Custom code to load and preprocess the .ddd file with open(file_path, 'r') as file: data = file.read() return data

Custom postprocessor for WebGPU output

def postprocess_webgpu(output): # Custom code to process WebGPU output before displaying it # This could involve converting the WebGPU output to a format suitable for display return output

Define the input component for .ddd files

ddd_input = gr.inputs.File(label="Upload your .ddd file", preprocess=preprocess_ddd)

Define the output component for WebGPU

webgpu_output = gr.outputs.Text(postprocess=postprocess_webgpu)

Your function that interacts with WebGPU

def webgpu_interaction(input_data): # Replace this with your actual code to interact with WebGPU # For example, you could pass the preprocessed .ddd data to your WebGPU library output = your_webgpu_library_function(input_data) return output

Create the Gradio interface

gr_interface = gr.Interface( fn=webgpu_interaction, inputs=ddd_input, outputs=webgpu_output, )

Launch the Gradio interface

gr_interface.launch()

defining two custom functions, preprocess_ddd and postprocess_webgpu, to handle the loading and processing of .ddd files and the postprocessing of WebGPU output, respectively.

The preprocess_ddd function is a preprocessor for the .ddd files taking the file path as input, loading the file's data, and preprocesses the data for use with WebGPU. Replace the placeholder logic with your actual .ddd file handling and processing code.

The postprocess_webgpu function is a postprocessor for the WebGPU output. It takes the raw WebGPU output as input and processes it before displaying it. The exact postprocessing logic will depend on the specific output format of your WebGPU library and how you want to visualize or present the results.

We then define a custom input component ddd_input using gr.inputs.File, specifying the preprocess_ddd function as the preprocessor.

Next, we define the output component webgpu_output using gr.outputs.Text, specifying the postprocess_webgpu function as the postprocessor.

Finally, we create the Gradio interface with gr.Interface, passing the webgpu_interaction function as the main function to interact with WebGPU, and specifying the ddd_input and webgpu_output components.

Keep in mind that this is a simplified example to illustrate the idea of custom preprocessors and postprocessors for interaction between WebGPU preprocess and .ddd file postprocess. Code adaptations to follow on a when ready deployment schedule.

ddd (c) 2023 codephreak MIT license
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