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import ast
import gradio as gr
import os
import re
import json
import logging
import torch
from datetime import datetime
from threading import Thread
from typing import Optional
from transformers import TextIteratorStreamer
from functools import partial
from huggingface_hub import CommitScheduler
from uuid import uuid4
from pathlib import Path
from code_interpreter.JupyterClient import JupyterNotebook
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
from code_interpreter.OpenCodeInterpreter import OpenCodeInterpreter
JSON_DATASET_DIR = Path("json_dataset")
JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
scheduler = CommitScheduler(
repo_id="opencodeinterpreter_user_data",
repo_type="dataset",
folder_path=JSON_DATASET_DIR,
path_in_repo="data",
private=True
)
logging.basicConfig(level=logging.INFO)
class StreamingOpenCodeInterpreter(OpenCodeInterpreter):
streamer: Optional[TextIteratorStreamer] = None
# overwirte generate function
@torch.inference_mode()
def generate(
self,
prompt: str = "",
max_new_tokens = 1024,
do_sample: bool = False,
top_p: float = 0.95,
top_k: int = 50,
) -> str:
# Get the model and tokenizer, and tokenize the user text.
self.streamer = TextIteratorStreamer(
self.tokenizer, skip_prompt=True, Timeout=5
)
inputs = self.tokenizer([prompt], return_tensors="pt", truncation=True, max_length=MAX_INPUT_TOKEN_LENGTH)
inputs = inputs.to(self.model.device)
kwargs = dict(
**inputs,
streamer=self.streamer,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
top_p=top_p,
top_k=top_k,
eos_token_id=self.tokenizer.eos_token_id
)
thread = Thread(target=self.model.generate, kwargs=kwargs)
thread.start()
return ""
def save_json(dialog, mode, json_file_path, dialog_id) -> None:
with scheduler.lock:
with json_file_path.open("a") as f:
json.dump({"id": dialog_id, "dialog": dialog, "mode": mode, "datetime": datetime.now().isoformat()}, f, ensure_ascii=False)
f.write("\n")
def convert_history(gradio_history: list[list], interpreter_history: list[dict]):
interpreter_history = [interpreter_history[0]] if interpreter_history and interpreter_history[0]["role"] == "system" else []
if not gradio_history:
return interpreter_history
for item in gradio_history:
if item[0] is not None:
interpreter_history.append({"role": "user", "content": item[0]})
if item[1] is not None:
interpreter_history.append({"role": "assistant", "content": item[1]})
return interpreter_history
def update_uuid(dialog_info):
new_uuid = str(uuid4())
logging.info(f"allocating new uuid {new_uuid} for conversation...")
return [new_uuid, dialog_info[1]]
def is_valid_python_code(code):
try:
ast.parse(code)
return True
except SyntaxError:
return False
class InputFunctionVisitor(ast.NodeVisitor):
def __init__(self):
self.found_input = False
def visit_Call(self, node):
if isinstance(node.func, ast.Name) and node.func.id == 'input':
self.found_input = True
self.generic_visit(node)
def has_input_function_calls(code):
try:
tree = ast.parse(code)
except SyntaxError:
return False
visitor = InputFunctionVisitor()
visitor.visit(tree)
return visitor.found_input
def gradio_launch(model_path: str, MAX_TRY: int = 3):
with gr.Blocks() as demo:
chatbot = gr.Chatbot(height=600, label="OpenCodeInterpreter", avatar_images=["assets/user.pic.jpg", "assets/assistant.pic.jpg"], show_copy_button=True)
with gr.Group():
with gr.Row():
msg = gr.Textbox(
container=False,
show_label=False,
label="Message",
placeholder="Type a message...",
scale=7,
autofocus=True
)
sub = gr.Button(
"Submit",
variant="primary",
scale=1,
min_width=150
)
# stop = gr.Button(
# "Stop",
# variant="stop",
# visible=False,
# scale=1,
# min_width=150
# )
with gr.Row():
# retry = gr.Button("🔄 Retry", variant="secondary")
# undo = gr.Button("↩️ Undo", variant="secondary")
clear = gr.Button("🗑️ Clear", variant="secondary")
session_state = gr.State([])
jupyter_state = gr.State(JupyterNotebook())
dialog_info = gr.State(["", 0])
demo.load(update_uuid, dialog_info, dialog_info)
def bot(user_message, history, jupyter_state, dialog_info, interpreter):
logging.info(f"user message: {user_message}")
interpreter.dialog = convert_history(gradio_history=history, interpreter_history=interpreter.dialog)
history.append([user_message, None])
interpreter.dialog.append({"role": "user", "content": user_message})
# setup
HAS_CODE = False # For now
prompt = interpreter.dialog_to_prompt(dialog=interpreter.dialog)
_ = interpreter.generate(prompt)
history[-1][1] = ""
generated_text = ""
for character in interpreter.streamer:
history[-1][1] += character
history[-1][1] = history[-1][1].replace("<|EOT|>","")
generated_text += character
yield history, history, jupyter_state, dialog_info
if is_valid_python_code(history[-1][1].strip()):
history[-1][1] = f"```python\n{history[-1][1].strip()}\n```"
generated_text = history[-1][1]
HAS_CODE, generated_code_block = interpreter.extract_code_blocks(
generated_text
)
interpreter.dialog.append(
{
"role": "assistant",
"content": generated_text.replace("<unk>_", "")
.replace("<unk>", "")
.replace("<|EOT|>", ""),
}
)
logging.info(f"saving current dialog to file {dialog_info[0]}.json...")
logging.info(f"current dialog: {interpreter.dialog}")
save_json(interpreter.dialog, mode="openci_only", json_file_path=JSON_DATASET_DIR/f"{dialog_info[0]}.json", dialog_id=dialog_info[0])
attempt = 1
while HAS_CODE:
if attempt > MAX_TRY:
break
# if no code then doesn't have to execute it
generated_text = "" # clear generated text
yield history, history, jupyter_state, dialog_info
# replace unknown thing to none ''
generated_code_block = generated_code_block.replace(
"<unk>_", ""
).replace("<unk>", "")
if has_input_function_calls(generated_code_block):
code_block_output = "Please directly assign the value of inputs instead of using input() function in your code."
else:
(
code_block_output,
error_flag,
) = interpreter.execute_code_and_return_output(
f"{generated_code_block}",
jupyter_state
)
if error_flag == "Timeout":
logging.info(f"{dialog_info[0]}: Restart jupyter kernel due to timeout")
jupyter_state = JupyterNotebook()
code_block_output = interpreter.clean_code_output(code_block_output)
if code_block_output.strip():
code_block_output = "Execution result: \n" + code_block_output
else:
code_block_output = "Code is executed, but result is empty. Please make sure that you include test case in your code."
history.append([code_block_output, ""])
interpreter.dialog.append({"role": "user", "content": code_block_output})
yield history, history, jupyter_state, dialog_info
prompt = interpreter.dialog_to_prompt(dialog=interpreter.dialog)
logging.info(f"generating answer for dialog {dialog_info[0]}")
_ = interpreter.generate(prompt)
for character in interpreter.streamer:
history[-1][1] += character
history[-1][1] = history[-1][1].replace("<|EOT|>","")
generated_text += character
yield history, history, jupyter_state, dialog_info
logging.info(f"finish generating answer for dialog {dialog_info[0]}")
HAS_CODE, generated_code_block = interpreter.extract_code_blocks(
history[-1][1]
)
interpreter.dialog.append(
{
"role": "assistant",
"content": generated_text.replace("<unk>_", "")
.replace("<unk>", "")
.replace("<|EOT|>", ""),
}
)
attempt += 1
logging.info(f"saving current dialog to file {dialog_info[0]}.json...")
logging.info(f"current dialog: {interpreter.dialog}")
save_json(interpreter.dialog, mode="openci_only", json_file_path=JSON_DATASET_DIR/f"{dialog_info[0]}.json", dialog_id=dialog_info[0])
if generated_text.endswith("<|EOT|>"):
continue
return history, history, jupyter_state, dialog_info
def reset_textbox():
return gr.update(value="")
def clear_history(history, jupyter_state, dialog_info, interpreter):
interpreter.dialog = []
jupyter_state.close()
return [], [], JupyterNotebook(), update_uuid(dialog_info)
interpreter = StreamingOpenCodeInterpreter(model_path=model_path)
sub.click(partial(bot, interpreter=interpreter), [msg, session_state, jupyter_state, dialog_info], [chatbot, session_state, jupyter_state, dialog_info])
sub.click(reset_textbox, [], [msg])
clear.click(partial(clear_history, interpreter=interpreter), [session_state, jupyter_state, dialog_info], [chatbot, session_state, jupyter_state, dialog_info], queue=False)
demo.queue(max_size=20)
demo.launch(share=True)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--path",
type=str,
required=False,
help="Path to the OpenCodeInterpreter Model.",
default="m-a-p/OpenCodeInterpreter-DS-6.7B",
)
args = parser.parse_args()
gradio_launch(model_path=args.path)
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