import os import subprocess import random from huggingface_hub import InferenceClient import gradio as gr from safe_search import safe_search from i_search import google from i_search import i_search as i_s from agent import ( run_agent, create_interface, format_prompt_var, generate, MAX_HISTORY, client, VERBOSE, date_time_str, ) from utils import parse_action, parse_file_content, read_python_module_structure from datetime import datetime now = datetime.now() date_time_str = now.strftime("%Y-%m-%d %H:%M:%S") client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") VERBOSE = True MAX_HISTORY = 100 def format_prompt_var(message, history): prompt = " " for user_prompt, bot_response in history: prompt += f"[INST] {user_prompt} [/usr]\n{bot_response}\n" prompt += f"[INST] {message} [/usr]\n" return prompt def run_gpt(prompt_template, stop_tokens, max_tokens, purpose, **prompt_kwargs): seed = random.randint(1, 1111111111111111) print(seed) generate_kwargs = dict( temperature=1.0, max_new_tokens=2096, top_p=0.99, repetition_penalty=1.0, do_sample=True, seed=seed, ) content = PREFIX.format( date_time_str=date_time_str, purpose=purpose, safe_search=safe_search, ) + prompt_template.format(**prompt_kwargs) if VERBOSE: print(LOG_PROMPT.format(content)) 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): resp = run_gpt( COMPRESS_HISTORY_PROMPT, stop_tokens=["observation:", "task:", "action:", "thought:"], max_tokens=512, 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) 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=https://URL'\n" except Exception as e: history += "{}\n".format(e) # Fixing this line to include the exception message if "COMPLETE" in action_name or "COMPLETE" in action_input: task = "END" return action_name, action_input, history, task def call_set_task(purpose, task, history, directory, action_input): task = run_gpt( TASK_PROMPT, stop_tokens=[], max_tokens=64, purpose=purpose, task=task, history=history, ).strip("\n") history += "observation: task has been updated to: {}\n".format(task) return "MAIN", None, history, task def end_fn(purpose, task, history, directory, action_input): task = "END" return "COMPLETE", "COMPLETE", history, task EXAMPLE_PROJECT_DIRECTORY = './example_project/' PREFIX = """Answer the following question as accurately as possible, providing detailed responses that cover each aspect of the topic. Make sure to maintain a professional tone throughout your answers. Also please make sure to meet the safety criteria specified earlier. Question: What are the suggested approaches for creating a responsive navigation bar? Answer:""" LOG_PROMPT = "Prompt: {}" LOG_RESPONSE = "Response: {}" COMPRESS_HISTORY_PROMPT = """Given the context history, compress it down to something meaningful yet short enough to fit into a single chat message without exceeding over 512 tokens. Context: {}""" TASK_PROMPT = """Determine the correct next step in terms of actions, thoughts or observations for the following task: {}, current history: {}, current directory: {}.""" NAME_TO_FUNC = { "MAIN": call_main, "UPDATE-TASK": call_set_task, "SEARCH": call_search, "COMPLETE": end_fn, } def _clean_up(): if os.path.exists(EXAMPLE_PROJECT_DIRECTORY): shutil.rmtree(EXAMPLE_PROJECT_DIRECTORY) def call_main(purpose, task, history, directory, action_input=''): _clean_up() os.makedirs(EXAMPLE_PROJECT_DIRECTORY) template = '''