asd / chatbot.py
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import gradio as gr
import random
import time, os
import copy
import re
import torch
from rich.console import Console
from rich.table import Table
from datetime import datetime
from threading import Thread
from typing import Optional
from transformers import TextIteratorStreamer
from utils.special_tok_llama2 import (
B_CODE,
E_CODE,
B_RESULT,
E_RESULT,
B_INST,
E_INST,
B_SYS,
E_SYS,
DEFAULT_PAD_TOKEN,
DEFAULT_BOS_TOKEN,
DEFAULT_EOS_TOKEN,
DEFAULT_UNK_TOKEN,
IGNORE_INDEX,
)
from finetuning.conversation_template import (
json_to_code_result_tok_temp,
msg_to_code_result_tok_temp,
)
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
from code_interpreter.LlamaCodeInterpreter import LlamaCodeInterpreter
class StreamingLlamaCodeInterpreter(LlamaCodeInterpreter):
streamer: Optional[TextIteratorStreamer] = None
# overwirte generate function
@torch.inference_mode()
def generate(
self,
prompt: str = "[INST]\n###User : hi\n###Assistant :",
max_new_tokens=512,
do_sample: bool = True,
use_cache: bool = True,
top_p: float = 0.95,
temperature: float = 0.1,
top_k: int = 50,
repetition_penalty: float = 1.0,
) -> str:
# Get the model and tokenizer, and tokenize the user text.
self.streamer = TextIteratorStreamer(
self.tokenizer, skip_prompt=True, Timeout=5
)
input_prompt = copy.deepcopy(prompt)
inputs = self.tokenizer([prompt], return_tensors="pt")
input_tokens_shape = inputs["input_ids"].shape[-1]
eos_token_id = self.tokenizer.convert_tokens_to_ids(DEFAULT_EOS_TOKEN)
e_code_token_id = self.tokenizer.convert_tokens_to_ids(E_CODE)
kwargs = dict(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
top_p=top_p,
temperature=temperature,
use_cache=use_cache,
top_k=top_k,
repetition_penalty=repetition_penalty,
eos_token_id=[
eos_token_id,
e_code_token_id,
], # Stop generation at either EOS or E_CODE token
streamer=self.streamer,
)
thread = Thread(target=self.model.generate, kwargs=kwargs)
thread.start()
return ""
def change_markdown_image(text: str):
modified_text = re.sub(r"!\[(.*?)\]\(\'(.*?)\'\)", r"![\1](/file=\2)", text)
return modified_text
def gradio_launch(model_path: str, load_in_4bit: bool = True, MAX_TRY: int = 5):
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
chatbot = gr.Chatbot(height=820, avatar_images="./assets/logo2.png")
msg = gr.Textbox()
clear = gr.Button("Clear")
interpreter = StreamingLlamaCodeInterpreter(
model_path=model_path, load_in_4bit=load_in_4bit
)
def bot(history):
user_message = history[-1][0]
interpreter.dialog.append({"role": "user", "content": user_message})
print(f"###User : [bold]{user_message}[bold]")
# print(f"###Assistant : ")
# setup
HAS_CODE = False # For now
INST_END_TOK_FLAG = False
full_generated_text = ""
prompt = interpreter.dialog_to_prompt(dialog=interpreter.dialog)
start_prompt = copy.deepcopy(prompt)
prompt = f"{prompt} {E_INST}"
_ = interpreter.generate(prompt)
history[-1][1] = ""
generated_text = ""
for character in interpreter.streamer:
history[-1][1] += character
generated_text += character
yield history
full_generated_text += generated_text
HAS_CODE, generated_code_block = interpreter.extract_code_blocks(
generated_text
)
attempt = 1
while HAS_CODE:
if attempt > MAX_TRY:
break
# if no code then doesn't have to execute it
# refine code block for history
history[-1][1] = (
history[-1][1]
.replace(f"{B_CODE}", "\n```python\n")
.replace(f"{E_CODE}", "\n```\n")
)
history[-1][1] = change_markdown_image(history[-1][1])
yield history
# replace unknown thing to none ''
generated_code_block = generated_code_block.replace(
"<unk>_", ""
).replace("<unk>", "")
(
code_block_output,
error_flag,
) = interpreter.execute_code_and_return_output(
f"{generated_code_block}"
)
code_block_output = interpreter.clean_code_output(code_block_output)
generated_text = (
f"{generated_text}\n{B_RESULT}\n{code_block_output}\n{E_RESULT}\n"
)
full_generated_text += (
f"\n{B_RESULT}\n{code_block_output}\n{E_RESULT}\n"
)
# append code output
history[-1][1] += f"\n```RESULT\n{code_block_output}\n```\n"
history[-1][1] = change_markdown_image(history[-1][1])
yield history
prompt = f"{prompt} {generated_text}"
_ = interpreter.generate(prompt)
for character in interpreter.streamer:
history[-1][1] += character
generated_text += character
history[-1][1] = change_markdown_image(history[-1][1])
yield history
HAS_CODE, generated_code_block = interpreter.extract_code_blocks(
generated_text
)
if generated_text.endswith("</s>"):
break
attempt += 1
interpreter.dialog.append(
{
"role": "assistant",
"content": generated_text.replace("<unk>_", "")
.replace("<unk>", "")
.replace("</s>", ""),
}
)
print("----------\n" * 2)
print(interpreter.dialog)
print("----------\n" * 2)
return history[-1][1]
def user(user_message, history):
return "", history + [[user_message, None]]
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue()
demo.launch()
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Process path for LLAMA2_FINETUNEED.")
parser.add_argument(
"--path",
type=str,
required=True,
help="Path to the finetuned LLAMA2 model.",
default="./output/llama-2-7b-codellama-ci",
)
args = parser.parse_args()
gradio_launch(model_path=args.path, load_in_4bit=True)