Update app.py

app.py

@@ -1,9 +1,7 @@
 import gradio as gr
-# import torch
-# from transformers import AutoModel, AutoTokenizer
+
 
 def load_model(model_link):
-    # model = AutoModel.from_pretrained(model_link)
     return "model"
 
 def update_config(quantization_type, bits, threshold):
@@ -18,15 +16,17 @@ def run_benchmark(model, config):
 with gr.Blocks() as demo:
     with gr.Tab("Model Loading"):
         model_input = gr.Textbox(label="Hugging Face Model Link")
-        model_type = gr.Dropdown(choices=["BERT", "GPT", "T5"], label="Model Type")
+        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=["INT8", "INT4", "FP16"], label="Quantization Type")
+        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")