Spaces:
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import os
from threading import Thread
from typing import Iterator
import gradio as gr
from typing import List, Tuple
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import spaces
MAX_INPUT_TOKEN_LENGTH= 50
LICENSE = """
<p/>
---
As a derivate work of [ConsistentAgents]() by Seonghee Lee.
"""
if torch.cuda.is_available():
model_id = "./backprop_llama2_69_1e-05"
HF_ACCESS_TOKEN = os.getenv('HF_ACCESS_TOKEN')
model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=HF_ACCESS_TOKEN, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.use_default_system_prompt = False
@spaces.GPU
def generate(
message: str,
chat_history: List[Tuple[str, str]],
system_prompt: str,
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = []
if system_prompt:
conversation.append({"role": "system", "content": system_prompt})
for user, assistant in chat_history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
# Create the Gradio interface
# gr.ChatInterface(
# yes_man,
# chatbot=gr.Chatbot(height=300),
# textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
# title="Yes Man",
# description="Ask Yes Man any question",
# theme="soft",
# examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
# cache_examples=True,
# retry_btn=None,
# undo_btn="Delete Previous",
# clear_btn="Clear",
# ).launch()
chat_interface = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Textbox(label="System prompt", lines=6),
],
)
with gr.Blocks(css="style.css") as demo:
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
chat_interface.render()
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.queue(max_size=20).launch(server_name='10.79.12.70',share=True) |