ddd.ddd

# ddd.ddd

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

# Custom preprocessor for WebGPU to postprocessor .ddd files using Gradio
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()