# import sys
# sys.path.insert(0, './petals/')
 
# import torch
# import transformers
import gradio as gr

# from src.client.remote_model import DistributedBloomForCausalLM


# MODEL_NAME = "bigscience/test-bloomd-6b3" # select model you like
# INITIAL_PEERS = ["/ip4/193.106.95.184/tcp/31000/p2p/QmSg7izCDtowVTACbUmWvEiQZNY4wgCQ9T9Doo66K59X6q"]

# tokenizer = transformers.BloomTokenizerFast.from_pretrained("bigscience/test-bloomd-6b3")
# model = DistributedBloomForCausalLM.from_pretrained("bigscience/test-bloomd-6b3", initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32)

def inference(text, seq_length=1):
    return text
    # input_ids = tokenizer([text], return_tensors="pt").input_ids
    # output = model.generate(input_ids, max_new_tokens=seq_length)
    # return tokenizer.batch_decode(output)[0]
 
iface = gr.Interface(
fn=inference, 
inputs=[
gr.Textbox(lines=10, label="Input text"),
gr.inputs.Slider(
        minimum=0,
        maximum=1000,
        step=1,
        default=42,
        label="Sequence length for generation"
    )
], 
outputs="text"
)
iface.launch()