app.py

import gradio as gr


def load_model(model_link):
    return "model"

def update_config(quantization_type, bits, threshold):
    # Configuration logic here
    return {"quantization": quantization_type, "bits": bits, "threshold": threshold}

def run_benchmark(model, config):
    # Benchmarking logic here
    return {"speed": "X ms/token", "memory": "Y GB"}

# Create the interface
with gr.Blocks() as demo:
    with gr.Tab("Model Loading"):
        model_input = gr.Textbox(label="Hugging Face Model Link")
        model_type = gr.Dropdown(choices=["LLM", "CV", "MLP"], label="Model Type")
        model = gr.Dropdown(choices=["BERT", "GPT", "T5"], label="Model")
        load_btn = gr.Button("Load Model")
    
    with gr.Tab("Quantization"):
        quant_type = gr.Dropdown(choices=["awg", "gptq", "4bit"], label="Quantization Type")
        bits = gr.Slider(minimum=4, maximum=8, step=1, label="Bits")
        threshold = gr.Slider(minimum=0, maximum=1, label="Threshold")
        
    with gr.Tab("Benchmarking"):
        data_input = gr.Textbox(label="Hugging Face data Input")
        benchmark_btn = gr.Button("Run Benchmark")
        results = gr.JSON(label="Benchmark Results")

    # Set up event handlers
    load_btn.click(load_model, inputs=[model_input])
    benchmark_btn.click(
        run_benchmark,
        inputs=[model_type, quant_type, bits, threshold],
        outputs=[results]
    )

demo.launch()