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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 | |
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") | |
try: | |
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'\n" | |
except Exception as e: | |
history += "observation: {}'\n".format(e) | |
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 "COMPLETE" in action_name or "COMPLETE" in action_input: | |
task = "END" | |
return action_name, action_input, history, task | |
else: | |
return action_name, action_input, history, task | |
else: | |
history += "{}\n".format(line) | |
#history += "observation: the following command did not produce any useful output: '{}', I need to check the commands syntax, or use a different command\n".format(line) | |
#return action_name, action_input, history, task | |
#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 "RESPONSE" in action_name: | |
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,hist): | |
print(purpose) | |
print(hist) | |
task=None | |
directory="./" | |
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) | |