app.py

import gradio as gr
import spaces
import torch
from torch.cuda.amp import autocast
import subprocess
from huggingface_hub import InferenceClient
import os
import psutil

"""
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
"""

from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch

subprocess.run(
    "pip install psutil",
   
    shell=True,
)

subprocess.run(
    "pip install pynvml gpustat",
   
    shell=True,
)
def print_s1ystem():
    ram_info = psutil.virtual_memory()
    print(f"Total RAM: {ram_info.total / (1024.0 ** 3)} GB")
    print(f"Available RAM: {ram_info.available / (1024.0 ** 3)} GB")

import psutil
import platform
import gpustat
from datetime import datetime

def get_size(bytes, suffix="B"):
    factor = 1024
    for unit in ["", "K", "M", "G", "T", "P"]:
        if bytes < factor:
            return f"{bytes:.2f}{unit}{suffix}"
        bytes /= factor

subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# pip install 'git+https://github.com/huggingface/transformers.git'



token=os.getenv('token')
print('token = ',token)

from transformers import AutoModelForCausalLM, AutoTokenizer

# model_id = "mistralai/Mistral-7B-v0.3"

model_id = "CohereForAI/aya-23-8B"


tokenizer = AutoTokenizer.from_pretrained(
    # model_id
    model_id
    , token= token,)



model = AutoModelForCausalLM.from_pretrained(model_id, token= token, 
                                                 # torch_dtype= torch.uint8, 
                                             torch_dtype=torch.float16,
                                              # torch_dtype=torch.fl,
                                             attn_implementation="flash_attention_2",
                                             low_cpu_mem_usage=True,
                                            
                                             device_map='cuda',
                                             
                                            )


# 


# device_map = infer_auto_device_map(model, max_memory={0: "79GB", "cpu":"65GB" })

# Load the model with the inferred device map
# model = load_checkpoint_and_dispatch(model, model_id, device_map=device_map, no_split_module_classes=["GPTJBlock"])
# model.half()



@spaces.GPU(duration=60)
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    print_s1ystem()
    uname = platform.uname()
    print(f"System: {uname.system}")
    print(f"Node Name: {uname.node}")
    print(f"Release: {uname.release}")
    print(f"Version: {uname.version}")
    print(f"Machine: {uname.machine}")
    print(f"Processor: {uname.processor}")

# GPU Information
gpu_stats = gpustat.GPUStatCollection.new_query()
for gpu in gpu_stats:
    print(f"GPU: {gpu.name}  Mem Free: {get_size(gpu.memory_free)}  Mem Used: {get_size(gpu.memory_used)}  Mem Total: {get_size(gpu.memory_total)}")
    messages = [{"role": "user", "content": "Hello, how are you?"}]
    input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
    # with autocast():
    gen_tokens = model.generate(
    input_ids, 
    max_new_tokens=100, 
    # do_sample=True, 
    temperature=0.3,
    )

    gen_text = tokenizer.decode(gen_tokens[0])
    print(gen_text)
    yield gen_text
   
  
    messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

    # inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")

    # outputs = model.generate(inputs, max_new_tokens=2000)
    # gen_text=tokenizer.decode(outputs[0], skip_special_tokens=True)
   
    # print(gen_text)
    # yield gen_text
    # 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()