File size: 8,166 Bytes
c87c295 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 |
import json
import os
import sys
import time
import re
from pathlib import Path
from typing import List, Literal, Optional, Tuple, TypedDict, Dict
# Get the path from environment variable
prj_root_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(prj_root_path)
from code_interpreter.JuypyterClient import JupyterNotebook
from code_interpreter.BaseCodeInterpreter import BaseCodeInterpreter
from utils.const import *
from prompt.gpt4_prompt import CODE_INTERPRETER_SYSTEM_PROMPT
# from prompt.gpt4_prompt import CODE_INTERPRETER_SYSTEM_PROMPT
from colorama import init, Fore, Style
from rich.markdown import Markdown
import base64
import openai
from retrying import retry
import logging
from termcolor import colored
# load from key file
with open("./openai_api_key.txt") as f:
OPENAI_API_KEY = key = f.read()
openai.api_key = OPENAI_API_KEY
from utils.cleaner import clean_error_msg
def remove_string(s):
pattern = r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\.\d{6}:.*LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n"
return re.sub(pattern, "", s)
def clean_the_dialog(dialog, question):
question_idx = 0
for idx, item in enumerate(dialog):
if item["content"] == question:
question_idx = idx
filtered_dialog = dialog[question_idx:]
user_qinit_dict = filtered_dialog[0]
answer_fuse_str = "\n".join([i["content"].strip() for i in filtered_dialog[1::2]])
final_dialog_dict = [
{"role": "user", "content": user_qinit_dict["content"]},
{"role": "assistant", "content": answer_fuse_str},
]
return final_dialog_dict
class GPTCodeInterpreter(BaseCodeInterpreter):
def __init__(self, model="gpt-4"):
self.model = model
self.dialog = [
# {"role": "system", "content": CODE_INTERPRETER_SYSTEM_PROMPT },
{
"role": "system",
"content": CODE_INTERPRETER_SYSTEM_PROMPT,
},
# {"role": "user", "content": "How can I use BeautifulSoup to scrape a website and extract all the URLs on a page?"},
# {"role": "assistant", "content": "I think I need to use beatifulsoup to find current korean president,"}
]
# self.dialog += few_shot_4
self.response = None
assert os.path.isfile(
"./openai_api_key.txt"
), "The openai_api_key.txt file could not be found. Please make sure it is in the same directory as this script, and that it contains your OpenAI API key."
# load from key file
with open("./openai_api_key.txt") as f:
OPENAI_API_KEY = f.read()
openai.api_key = OPENAI_API_KEY
self.nb = JupyterNotebook()
out = self.nb.add_and_run(TOOLS_CODE) # tool import
def get_response_content(self):
if self.response:
return self.response["choices"][0]["message"]["content"]
else:
return None
@retry(
stop_max_attempt_number=7,
wait_exponential_multiplier=1000,
wait_exponential_max=10000,
)
def ChatCompletion(self):
try:
self.response = openai.ChatCompletion.create(
model=self.model, messages=self.dialog, temperature=0.2, top_p=0.9
)
except Exception as e:
print(f"error while OPENAI api call {e}")
def close(self):
"""
close jupyter notebook, and this class instance
"""
self.nb.close()
def save_dialog(self, path: str = "./output/dialog.json"):
with open(path, "w") as f:
json.dump(self.dialog, f)
print(f" ++Dialog saved to [{path}]")
def chat(
self,
user_message: str,
VERBOSE: bool = False,
MAX_TRY: int = 6,
code_exec_prefix: str = "",
feedback_prompt: str = "",
append_result: bool = True,
):
self.dialog.append({"role": "user", "content": user_message})
code_block_output = ""
attempt = 0
img_data = None
if VERBOSE:
print(
"###User : " + Fore.BLUE + Style.BRIGHT + user_message + Style.RESET_ALL
)
print("\n###Assistant : ")
for i in range(MAX_TRY):
# GPT response
self.ChatCompletion()
# Get code block
generated_text = self.get_response_content()
generated_code_blocks = self.extract_code_blocks(generated_text)
# execute code
if len(generated_code_blocks) > 0:
# Find the position of the first code block in the last answer
first_code_block_pos = (
generated_text.find(generated_code_blocks[0])
if generated_code_blocks
else -1
)
text_before_first_code_block = (
generated_text
if first_code_block_pos == -1
else generated_text[:first_code_block_pos]
)
if VERBOSE:
print(Fore.GREEN + text_before_first_code_block + Style.RESET_ALL)
if VERBOSE:
print(
Fore.YELLOW
+ generated_code_blocks[0]
+ "\n```\n"
+ Style.RESET_ALL
)
code_block_output, error_flag = self.execute_code_and_return_output(
generated_code_blocks[0]
)
code_block_output = f"{code_block_output}"
if code_block_output is not None:
code_block_output = code_block_output.strip()
code_block_output = remove_string(code_block_output)
if len(code_block_output) > 500:
code_block_output = (
code_block_output[:200] + "⋯(skip)⋯" + code_block_output[-200:]
)
code_block_output_str = f"\n```RESULT\n{code_block_output}\n```\n"
if append_result:
gen_final = f"{text_before_first_code_block}{generated_code_blocks[0]}\n```{code_block_output_str}"
if VERBOSE:
print(
Fore.LIGHTBLACK_EX + code_block_output_str + Style.RESET_ALL
)
else:
gen_final = (
f"{text_before_first_code_block}{generated_code_blocks[0]}\n```"
)
self.dialog.append(
{
"role": "assistant",
"content": gen_final,
}
)
if len(feedback_prompt) < 5:
feedback_dict = {
"role": "user",
"content": "Keep going. if you think debugging tell me where you got wrong and better code.\nNeed conclusion to question only text (Do not leave result part alone).\nif doesn't need to generated anything then just say <done>",
}
else:
feedback_dict = {
"role": "user",
"content": f"{feedback_prompt}",
}
self.dialog.append(feedback_dict)
else:
if "<done>" in generated_text:
generated_text = generated_text.split("<done>")[0].strip()
if len(generated_text) <= 0:
break
if VERBOSE:
print(Fore.GREEN + generated_text + Style.RESET_ALL)
self.dialog.append(
{
"role": "assistant",
"content": f"{generated_text}",
}
)
break
self.dialog = [self.dialog[0]] + clean_the_dialog(
self.dialog, question=user_message
) # delete retrospections after generation step
return self.dialog[-1]
|