app.py

import os
import spaces
import torch
import gradio as gr


# cpu

zero = torch.Tensor([0]).cuda()
print(zero.device) # <-- 'cpu' 🤔


# gpu
model = None

@spaces.GPU
def greet(prompts, separator):
    # print(zero.device) # <-- 'cuda:0' 🤗
    from vllm import SamplingParams, LLM
    from transformers.utils import move_cache
    from huggingface_hub import snapshot_download, login

    global model

    if model is None:
        LLM_MODEL_ID = "DoctorSlimm/trim-music-31"
        # LLM_MODEL_ID = "mistral-community/Mistral-7B-v0.2"
        # LLM_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.2"
        os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1'
        fp = snapshot_download(LLM_MODEL_ID, token=os.getenv('HF_TOKEN'), revision='main')
        move_cache()
        model = LLM(fp)
    
    sampling_params = dict(
            temperature = 0.01,
            ignore_eos = False,
            max_tokens = int(512 * 2)
    )
    sampling_params = SamplingParams(**sampling_params)

    multi_prompt = False
    separator = separator.strip()
    if separator in prompts:
        multi_prompt = True
        prompts = prompts.split(separator)
    else:
        prompts = [prompts]
    for idx, pt in enumerate(prompts):
        print()
        print(f'[{idx}]:')
        print(pt)
        
    model_outputs = model.generate(prompts, sampling_params)
    generations = []
    for output in model_outputs:
        for outputs in output.outputs:
            generations.append(outputs.text)
    if multi_prompt:
        return separator.join(generations)
    return generations[0]


## make predictions via api ##
# https://www.gradio.app/guides/getting-started-with-the-python-client#connecting-a-general-gradio-app

demo = gr.Interface(
    fn=greet,
    inputs=[
        gr.Text(
            value='hello sir!<SEP>bonjour madame...',
            placeholder='hello sir!<SEP>bonjour madame...',
            label='list of prompts separated by separator'
        ),
        gr.Text(
            value='<SEP>',
            placeholder='<SEP>',
            label='separator for your prompts'
        )],
    outputs=gr.Text()
)
demo.launch(share=True)