import os
import subprocess
import random
from huggingface_hub import InferenceClient
import gradio as gr
from i_search import google
from i_search import i_search as i_s
from agent import (
ACTION_PROMPT,
ADD_PROMPT,
COMPRESS_HISTORY_PROMPT,
LOG_PROMPT,
LOG_RESPONSE,
MODIFY_PROMPT,
PREFIX,
SEARCH_QUERY,
READ_PROMPT,
TASK_PROMPT,
UNDERSTAND_TEST_RESULTS_PROMPT,
)
from utils import parse_action, parse_file_content, read_python_module_structure
client = InferenceClient(
"mistralai/Mixtral-8x7B-Instruct-v0.1"
)
############################################
VERBOSE = True
MAX_HISTORY = 100
#MODEL = "gpt-3.5-turbo" # "gpt-4"
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
def run_gpt(
prompt_template,
stop_tokens,
max_tokens,
module_summary,
purpose,
**prompt_kwargs,
):
seed = random.randint(1,1111111111111111)
generate_kwargs = dict(
temperature=0.9,
max_new_tokens=10480,
top_p=0.95,
repetition_penalty=1.0,
do_sample=True,
seed=seed,
)
content = PREFIX.format(
purpose=purpose,
) + prompt_template.format(**prompt_kwargs)
if VERBOSE:
print(LOG_PROMPT.format(content))
#formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
#formatted_prompt = format_prompt(f'{content}', history)
stream = client.text_generation(content, **generate_kwargs, stream=True, details=True, return_full_text=False)
resp = ""
for response in stream:
resp += response.token.text
yield resp
'''
resp = openai.ChatCompletion.create(
model=MODEL,
messages=[
{"role": "system", "content": content},
],
temperature=0.0,
max_tokens=max_tokens,
stop=stop_tokens if stop_tokens else None,
)["choices"][0]["message"]["content"]
'''
if VERBOSE:
print(LOG_RESPONSE.format(resp))
return resp
def compress_history(purpose, task, history, directory):
module_summary, _, _ = read_python_module_structure(directory)
resp = run_gpt(
COMPRESS_HISTORY_PROMPT,
stop_tokens=["observation:", "task:", "action:", "thought:"],
max_tokens=512,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
)
history = "observation: {}\n".format(resp)
return history
def call_search(purpose, task, history, directory, action_input):
print("CALLING SEARCH")
if "http" in action_input:
if "<" in action_input:
action_input = action_input.strip("<")
if ">" in action_input:
action_input = action_input.strip(">")
response = i_s(action_input)
#response = google(search_return)
print(response)
history += "observation: search result is: {}\n".format(response)
else:
history += "observation: I need to provide a valid URL to 'action: SEARCH action_input=URL'"
return "MAIN", None, history, task
def call_main(purpose, task, history, directory, action_input):
module_summary, _, _ = read_python_module_structure(directory)
resp = run_gpt(
ACTION_PROMPT,
stop_tokens=["observation:", "task:"],
max_tokens=256,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
)
lines = resp.strip().strip("\n").split("\n")
for line in lines:
if line == "":
continue
if line.startswith("thought: "):
history += "{}\n".format(line)
elif line.startswith("action: "):
action_name, action_input = parse_action(line)
print (f'ACTION_NAME :: {action_name}')
print (f'ACTION_INPUT :: {action_input}')
history += "{}\n".format(line)
if action_name=="COMPLETE" or action_input=="COMPLETE":
task = "END"
return action_name, action_input, history, task
else:
return action_name, action_input, history, task
else:
assert False, "unknown action: {}".format(line)
return "MAIN", None, history, task
def call_test(purpose, task, history, directory, action_input):
result = subprocess.run(
["python", "-m", "pytest", "--collect-only", directory],
capture_output=True,
text=True,
)
if result.returncode != 0:
history += "observation: there are no tests! Test should be written in a test folder under {}\n".format(
directory
)
return "MAIN", None, history, task
result = subprocess.run(
["python", "-m", "pytest", directory], capture_output=True, text=True
)
if result.returncode == 0:
history += "observation: tests pass\n"
return "MAIN", None, history, task
module_summary, content, _ = read_python_module_structure(directory)
resp = run_gpt(
UNDERSTAND_TEST_RESULTS_PROMPT,
stop_tokens=[],
max_tokens=256,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
stdout=result.stdout[:5000], # limit amount of text
stderr=result.stderr[:5000], # limit amount of text
)
history += "observation: tests failed: {}\n".format(resp)
return "MAIN", None, history, task
def call_set_task(purpose, task, history, directory, action_input):
module_summary, content, _ = read_python_module_structure(directory)
task = run_gpt(
TASK_PROMPT,
stop_tokens=[],
max_tokens=64,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
).strip("\n")
history += "observation: task has been updated to: {}\n".format(task)
return "MAIN", None, history, task
def call_read(purpose, task, history, directory, action_input):
if not os.path.exists(action_input):
history += "observation: file does not exist\n"
return "MAIN", None, history, task
module_summary, content, _ = read_python_module_structure(directory)
f_content = (
content[action_input] if content[action_input] else "< document is empty >"
)
resp = run_gpt(
READ_PROMPT,
stop_tokens=[],
max_tokens=256,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
file_path=action_input,
file_contents=f_content,
).strip("\n")
history += "observation: {}\n".format(resp)
return "MAIN", None, history, task
def call_modify(purpose, task, history, directory, action_input):
if not os.path.exists(action_input):
history += "observation: file does not exist\n"
return "MAIN", None, history, task
(
module_summary,
content,
_,
) = read_python_module_structure(directory)
f_content = (
content[action_input] if content[action_input] else "< document is empty >"
)
resp = run_gpt(
MODIFY_PROMPT,
stop_tokens=["action:", "thought:", "observation:"],
max_tokens=2048,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
file_path=action_input,
file_contents=f_content,
)
new_contents, description = parse_file_content(resp)
if new_contents is None:
history += "observation: failed to modify file\n"
return "MAIN", None, history, task
with open(action_input, "w") as f:
f.write(new_contents)
history += "observation: file successfully modified\n"
history += "observation: {}\n".format(description)
return "MAIN", None, history, task
def call_add(purpose, task, history, directory, action_input):
d = os.path.dirname(action_input)
if not d.startswith(directory):
history += "observation: files must be under directory {}\n".format(directory)
elif not action_input.endswith(".py"):
history += "observation: can only write .py files\n"
else:
if d and not os.path.exists(d):
os.makedirs(d)
if not os.path.exists(action_input):
module_summary, _, _ = read_python_module_structure(directory)
resp = run_gpt(
ADD_PROMPT,
stop_tokens=["action:", "thought:", "observation:"],
max_tokens=2048,
module_summary=module_summary,
purpose=purpose,
task=task,
history=history,
file_path=action_input,
)
new_contents, description = parse_file_content(resp)
if new_contents is None:
history += "observation: failed to write file\n"
return "MAIN", None, history, task
with open(action_input, "w") as f:
f.write(new_contents)
history += "observation: file successfully written\n"
history += "obsertation: {}\n".format(description)
else:
history += "observation: file already exists\n"
return "MAIN", None, history, task
def end_fn(purpose, task, history, directory, action_input):
task = "END"
return "COMPLETE", None, history, task
NAME_TO_FUNC = {
"MAIN": call_main,
"UPDATE-TASK": call_set_task,
"SEARCH": call_search,
"COMPLETE": end_fn,
}
def run_action(purpose, task, history, directory, action_name, action_input):
if action_name == "COMPLETE":
task="END"
return action_name, action_input, history, task
# compress the history when it is long
if len(history.split("\n")) > MAX_HISTORY:
if VERBOSE:
print("COMPRESSING HISTORY")
history = compress_history(purpose, task, history, directory)
assert action_name in NAME_TO_FUNC
print("RUN: ", action_name, action_input)
return NAME_TO_FUNC[action_name](purpose, task, history, directory, action_input)
def run(purpose, directory, task=None):
history = ""
action_name = "UPDATE-TASK" if task is None else "MAIN"
action_input = None
while True:
print("")
print("")
print("---")
print("purpose:", purpose)
print("task:", task)
print("---")
print(history)
print("---")
action_name, action_input, history, task = run_action(
purpose,
task,
history,
directory,
action_name,
action_input,
)
if task == "END":
return history
################################################
def format_prompt(message, history):
prompt = "<s>"
for user_prompt, bot_response in history:
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s> "
prompt += f"[INST] {message} [/INST]"
return prompt
agents =[
"WEB_DEV",
"AI_SYSTEM_PROMPT",
"PYTHON_CODE_DEV"
]
def generate(
prompt, history, agent_name=agents[0], sys_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
seed = random.randint(1,1111111111111111)
agent=prompts.WEB_DEV
if agent_name == "WEB_DEV":
agent = prompts.WEB_DEV
if agent_name == "AI_SYSTEM_PROMPT":
agent = prompts.AI_SYSTEM_PROMPT
if agent_name == "PYTHON_CODE_DEV":
agent = prompts.PYTHON_CODE_DEV
system_prompt=agent
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=seed,
)
formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
additional_inputs=[
gr.Dropdown(
label="Agents",
choices=[s for s in agents],
value=agents[0],
interactive=True,
),
gr.Textbox(
label="System Prompt",
max_lines=1,
interactive=True,
),
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=1048*10,
minimum=0,
maximum=1048*10,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
),
]
examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
]
gr.ChatInterface(
fn=run,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
title="Mixtral 46.7B",
examples=examples,
concurrency_limit=20,
).launch(show_api=False)