Update app.py

app.py

@@ -1,64 +1,41 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+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):
+    # 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=["BERT", "GPT", "T5"], label="Model Type")
+        load_btn = gr.Button("Load Model")
+    
+    with gr.Tab("Quantization"):
+        quant_type = gr.Dropdown(choices=["INT8", "INT4", "FP16"], 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"):
+        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()