app.py

import gradio as gr
from huggingface_hub import InferenceClient
import spaces #0.32.0
import torch
import os
import platform

model = ""
duration = 24
print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
print(f"CUDA version: {torch.version.cuda}")
print(f"Python version: {platform.python_version()}")
print(f"Pytorch version: {torch.__version__}")
print(f"Gradio version: {gr. __version__}")


"""
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
Packages that work::::::::::
Is CUDA available: True
CUDA device: NVIDIA A100-SXM4-80GB MIG 3g.40gb
CUDA version: 12.1
Python version: 3.10.13
Pytorch version: 2.4.0+cu121
Gradio version: 5.0.1
"""


def choose_model(model_name):
    if model_name == "DeepSeek-R1-Distill-Qwen-1.5B":
        model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"

    elif model_name == "DeepSeek-R1-Distill-Qwen-32B":
        model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
        
    elif model_name == "Llama3-8b-Instruct":    
        model = "meta-llama/Meta-Llama-3-8B-Instruct"

    elif model_name == "Llama3.1-8b-Instruct":
        model = "meta-llama/Llama-3.1-8B-Instruct"

    elif model_name == "Llama2-13b-chat":
        model = "meta-llama/Llama-2-13b-chat-hf"

    elif model_name == "Gemma-2-2b":
        model = "google/gemma-2-2b-it"

    elif model_name == "Gemma-7b":
        model = "google/gemma-7b"
    
    elif model_name == "Mixtral-8x7B-Instruct":
        model = "mistralai/Mixtral-8x7B-Instruct-v0.1"

    elif model_name == "Microsoft-phi-2":
        model = "microsoft/phi-2"

    else:    # default to zephyr if no model chosen
        model = "HuggingFaceH4/zephyr-7b-beta"
    
    return model
    

@spaces.GPU(duration=duration)
def respond(message, history: list[tuple[str, str]], model, system_message, max_tokens, temperature, top_p):

    print(model)
    model_name = choose_model(model)

    client = InferenceClient(model_name, token=os.getenv('deepseekv2'))
    
    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.Dropdown(["DeepSeek-R1-Distill-Qwen-1.5B", "DeepSeek-R1-Distill-Qwen-32B", "Gemma-2-2b", "Gemma-7b", "Llama2-13b-chat", "Llama3-8b-Instruct", "Llama3.1-8b-Instruct", "Microsoft-phi-2", "Mixtral-8x7B-Instruct", "Zephr-7b-beta"], label="Select Model"),
        gr.Textbox(value="You are a friendly and helpful Chatbot, be concise and straight to the point, avoid excessive reasoning.", 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()