import os os.system("pip install -r requirements.txt") import base64 import openai import itertools import json from typing import Dict, List, Tuple import cv2 import gradio as gr from agentverse import TaskSolving from agentverse.simulation import Simulation from agentverse.message import Message def cover_img(background, img, place: Tuple[int, int]): """ Overlays the specified image to the specified position of the background image. :param background: background image :param img: the specified image :param place: the top-left coordinate of the target location """ back_h, back_w, _ = background.shape height, width, _ = img.shape for i, j in itertools.product(range(height), range(width)): if img[i, j, 3]: background[place[0] + i, place[1] + j] = img[i, j, :3] class GUI: """ the UI of frontend """ def __init__( self, task: str = "simulation/nlp_classroom_9players", tasks_dir: str = "agentverse/tasks", ): """ init a UI. default number of students is 0 """ self.messages = [] self.task = task self.tasks_dir = tasks_dir if task == "pipeline_brainstorming": self.backend = TaskSolving.from_task(task, tasks_dir) else: self.backend = Simulation.from_task(task, tasks_dir) self.turns_remain = 0 self.agent_id = { self.backend.agents[idx].name: idx for idx in range(len(self.backend.agents)) } self.stu_num = len(self.agent_id) - 1 self.autoplay = False self.image_now = None self.text_now = None self.tot_solutions = 5 self.solution_status = [False] * self.tot_solutions def get_avatar(self, idx): if idx == -1: img = cv2.imread("./imgs/db_diag/-1.png") elif self.task == "simulation/prisoner_dilemma": img = cv2.imread(f"./imgs/prison/{idx}.png") else: img = cv2.imread(f"./imgs/{idx}.png") base64_str = cv2.imencode(".png", img)[1].tostring() return "data:image/png;base64," + base64.b64encode(base64_str).decode("utf-8") def stop_autoplay(self): self.autoplay = False return ( gr.Button.update(interactive=False), gr.Button.update(interactive=False), gr.Button.update(interactive=False), ) def start_autoplay(self): self.autoplay = True yield ( self.image_now, self.text_now, gr.Button.update(interactive=False), gr.Button.update(interactive=True), gr.Button.update(interactive=False), *[gr.Button.update(visible=statu) for statu in self.solution_status], gr.Box.update(visible=any(self.solution_status)), ) while self.autoplay and self.turns_remain > 0: outputs = self.gen_output() self.image_now, self.text_now = outputs yield ( *outputs, gr.Button.update( interactive=not self.autoplay and self.turns_remain > 0 ), gr.Button.update(interactive=self.autoplay and self.turns_remain > 0), gr.Button.update( interactive=not self.autoplay and self.turns_remain > 0 ), *[gr.Button.update(visible=statu) for statu in self.solution_status], gr.Box.update(visible=any(self.solution_status)), ) def delay_gen_output( self, ): yield ( self.image_now, self.text_now, gr.Button.update(interactive=False), gr.Button.update(interactive=False), *[gr.Button.update(visible=statu) for statu in self.solution_status], gr.Box.update(visible=any(self.solution_status)), ) outputs = self.gen_output() self.image_now, self.text_now = outputs yield ( self.image_now, self.text_now, gr.Button.update(interactive=self.turns_remain > 0), gr.Button.update(interactive=self.turns_remain > 0), *[gr.Button.update(visible=statu) for statu in self.solution_status], gr.Box.update(visible=any(self.solution_status)), ) def delay_reset(self, task_dropdown, api_key_text, organization_text, api_base_text): self.autoplay = False self.image_now, self.text_now = self.reset( task_dropdown, api_key_text, organization_text, api_base_text ) return ( self.image_now, self.text_now, gr.Button.update(interactive=True), gr.Button.update(interactive=False), gr.Button.update(interactive=True), *[gr.Button.update(visible=statu) for statu in self.solution_status], gr.Box.update(visible=any(self.solution_status)), ) def reset( self, task_dropdown="simulation/nlp_classroom_9players", api_key_text="", organization_text="", api_base_text="" ): openai.api_key = api_key_text openai.organization = organization_text openai.api_base = api_base_text if api_base_text else None """ tell backend the new number of students and generate new empty image :param stu_num: :return: [empty image, empty message] """ # if not 0 <= stu_num <= 30: # raise gr.Error("the number of students must be between 0 and 30.") """ # [To-Do] Need to add a function to assign agent numbers into the backend. """ # self.backend.reset(stu_num) # self.stu_num = stu_num """ # [To-Do] Pass the parameters to reset """ if task_dropdown == "pipeline_brainstorming": self.backend = TaskSolving.from_task(task_dropdown, self.tasks_dir) else: self.backend = Simulation.from_task(task_dropdown, self.tasks_dir) self.agent_id = { self.backend.agents[idx].name: idx for idx in range(len(self.backend.agents)) } self.task = task_dropdown self.stu_num = len(self.agent_id) - 1 self.backend.reset() self.turns_remain = self.backend.environment.max_turns if task_dropdown == "simulation/prisoner_dilemma": background = cv2.imread("./imgs/prison/case_1.png") elif task_dropdown == "simulation/db_diag": background = cv2.imread("./imgs/db_diag/background.png") elif "sde" in task_dropdown: background = cv2.imread("./imgs/sde/background.png") else: background = cv2.imread("./imgs/background.png") back_h, back_w, _ = background.shape stu_cnt = 0 for h_begin, w_begin in itertools.product( range(800, back_h, 300), range(135, back_w - 200, 200) ): stu_cnt += 1 img = cv2.imread( f"./imgs/{(stu_cnt - 1) % 11 + 1 if stu_cnt <= self.stu_num else 'empty'}.png", cv2.IMREAD_UNCHANGED, ) cover_img( background, img, (h_begin - 30 if img.shape[0] > 190 else h_begin, w_begin), ) self.messages = [] self.solution_status = [False] * self.tot_solutions return [cv2.cvtColor(background, cv2.COLOR_BGR2RGB), ""] def gen_img(self, data: List[Dict]): """ generate new image with sender rank :param data: :return: the new image """ # The following code need to be more general. This one is too task-specific. # if len(data) != self.stu_num: if len(data) != self.stu_num + 1: raise gr.Error("data length is not equal to the total number of students.") if self.task == "simulation/prisoner_dilemma": img = cv2.imread("./imgs/speaking.png", cv2.IMREAD_UNCHANGED) if ( len(self.messages) < 2 or self.messages[-1][0] == 1 or self.messages[-2][0] == 2 ): background = cv2.imread("./imgs/prison/case_1.png") if data[0]["message"] != "": cover_img(background, img, (400, 480)) else: background = cv2.imread("./imgs/prison/case_2.png") if data[0]["message"] != "": cover_img(background, img, (400, 880)) if data[1]["message"] != "": cover_img(background, img, (550, 480)) if data[2]["message"] != "": cover_img(background, img, (550, 880)) elif self.task == "db_diag": background = cv2.imread("./imgs/db_diag/background.png") img = cv2.imread("./imgs/db_diag/speaking.png", cv2.IMREAD_UNCHANGED) if data[0]["message"] != "": cover_img(background, img, (750, 80)) if data[1]["message"] != "": cover_img(background, img, (310, 220)) if data[2]["message"] != "": cover_img(background, img, (522, 11)) elif "sde" in self.task: background = cv2.imread("./imgs/sde/background.png") img = cv2.imread("./imgs/sde/speaking.png", cv2.IMREAD_UNCHANGED) if data[0]["message"] != "": cover_img(background, img, (692, 330)) if data[1]["message"] != "": cover_img(background, img, (692, 660)) if data[2]["message"] != "": cover_img(background, img, (692, 990)) else: background = cv2.imread("./imgs/background.png") back_h, back_w, _ = background.shape stu_cnt = 0 if data[stu_cnt]["message"] not in ["", "[RaiseHand]"]: img = cv2.imread("./imgs/speaking.png", cv2.IMREAD_UNCHANGED) cover_img(background, img, (370, 1250)) for h_begin, w_begin in itertools.product( range(800, back_h, 300), range(135, back_w - 200, 200) ): stu_cnt += 1 if stu_cnt <= self.stu_num: img = cv2.imread( f"./imgs/{(stu_cnt - 1) % 11 + 1}.png", cv2.IMREAD_UNCHANGED ) cover_img( background, img, (h_begin - 30 if img.shape[0] > 190 else h_begin, w_begin), ) if "[RaiseHand]" in data[stu_cnt]["message"]: # elif data[stu_cnt]["message"] == "[RaiseHand]": img = cv2.imread("./imgs/hand.png", cv2.IMREAD_UNCHANGED) cover_img(background, img, (h_begin - 90, w_begin + 10)) elif data[stu_cnt]["message"] not in ["", "[RaiseHand]"]: img = cv2.imread("./imgs/speaking.png", cv2.IMREAD_UNCHANGED) cover_img(background, img, (h_begin - 90, w_begin + 10)) else: img = cv2.imread("./imgs/empty.png", cv2.IMREAD_UNCHANGED) cover_img(background, img, (h_begin, w_begin)) return cv2.cvtColor(background, cv2.COLOR_BGR2RGB) def return_format(self, messages: List[Message]): _format = [{"message": "", "sender": idx} for idx in range(len(self.agent_id))] for message in messages: if self.task == "db_diag": content_json: dict = message.content content_json[ "diagnose" ] = f"[{message.sender}]: {content_json['diagnose']}" _format[self.agent_id[message.sender]]["message"] = json.dumps( content_json ) elif "sde" in self.task: if message.sender == "code_tester": pre_message, message_ = message.content.split("\n") message_ = "{}\n{}".format( pre_message, json.loads(message_)["feedback"] ) _format[self.agent_id[message.sender]][ "message" ] = "[{}]: {}".format(message.sender, message_) else: _format[self.agent_id[message.sender]][ "message" ] = "[{}]: {}".format(message.sender, message.content) else: _format[self.agent_id[message.sender]]["message"] = "[{}]: {}".format( message.sender, message.content ) return _format def gen_output(self): """ generate new image and message of next step :return: [new image, new message] """ # data = self.backend.next_data() return_message = self.backend.next() data = self.return_format(return_message) # data.sort(key=lambda item: item["sender"]) """ # [To-Do]; Check the message from the backend: only 1 person can speak """ for item in data: if item["message"] not in ["", "[RaiseHand]"]: self.messages.append((item["sender"], item["message"])) message = self.gen_message() self.turns_remain -= 1 return [self.gen_img(data), message] def gen_message(self): # If the backend cannot handle this error, use the following code. message = "" """ for item in data: if item["message"] not in ["", "[RaiseHand]"]: message = item["message"] break """ for sender, msg in self.messages: if sender == 0: avatar = self.get_avatar(0) elif sender == -1: avatar = self.get_avatar(-1) else: avatar = self.get_avatar((sender - 1) % 11 + 1) if self.task == "db_diag": msg_json = json.loads(msg) self.solution_status = [False] * self.tot_solutions msg = msg_json["diagnose"] if msg_json["solution"] != "": solution: List[str] = msg_json["solution"] for solu in solution: if "query" in solu or "queries" in solu: self.solution_status[0] = True solu = solu.replace( "query", 'query' ) solu = solu.replace( "queries", 'queries' ) if "join" in solu: self.solution_status[1] = True solu = solu.replace( "join", 'join' ) if "index" in solu: self.solution_status[2] = True solu = solu.replace( "index", 'index' ) if "system configuration" in solu: self.solution_status[3] = True solu = solu.replace( "system configuration", 'system configuration', ) if ( "monitor" in solu or "Monitor" in solu or "Investigate" in solu ): self.solution_status[4] = True solu = solu.replace( "monitor", 'monitor' ) solu = solu.replace( "Monitor", 'Monitor' ) solu = solu.replace( "Investigate", 'Investigate', ) msg = f"{msg}
{solu}" if msg_json["knowledge"] != "": msg = f'{msg}
{msg_json["knowledge"]}' else: msg = msg.replace("<", "<") msg = msg.replace(">", ">") message = ( f'
' f'' f'
' f"{msg}" f"
" + message ) message = ( '
' + message + "
" ) return message def submit(self, message: str): """ submit message to backend :param message: message :return: [new image, new message] """ self.backend.submit(message) self.messages.append((-1, f"[User]: {message}")) return self.gen_img([{"message": ""}] * len(self.agent_id)), self.gen_message() def launch(self, single_agent=False, discussion_mode=False): if self.task == "pipeline_brainstorming": with gr.Blocks() as demo: chatbot = gr.Chatbot(height=800, show_label=False) msg = gr.Textbox(label="Input") def respond(message, chat_history): chat_history.append((message, None)) yield "", chat_history for response in self.backend.iter_run( single_agent=single_agent, discussion_mode=discussion_mode ): print(response) chat_history.append((None, response)) yield "", chat_history msg.submit(respond, [msg, chatbot], [msg, chatbot]) else: with gr.Blocks() as demo: with gr.Row(): task_dropdown = gr.Dropdown( choices=[ "simulation/nlp_classroom_9players", "simulation/prisoner_dilemma", ], value="simulation/nlp_classroom_9players", label="Task", ) api_key_text = gr.Textbox(label="OPENAI API KEY") organization_text = gr.Textbox(label="Organization") api_base_text = gr.Textbox(label="OpenAI Base URL", placeholder="if not set, will use openai's default url") with gr.Row(): with gr.Column(): image_output = gr.Image() with gr.Row(): reset_btn = gr.Button("Build/Reset") # next_btn = gr.Button("Next", variant="primary") next_btn = gr.Button("Next", interactive=False) stop_autoplay_btn = gr.Button( "Stop Autoplay", interactive=False ) start_autoplay_btn = gr.Button( "Start Autoplay", interactive=False ) with gr.Box(visible=False) as solutions: with gr.Column(): gr.HTML("Optimization Solutions:") with gr.Row(): rewrite_slow_query_btn = gr.Button( "Rewrite Slow Query", visible=False ) add_query_hints_btn = gr.Button( "Add Query Hints", visible=False ) update_indexes_btn = gr.Button( "Update Indexes", visible=False ) tune_parameters_btn = gr.Button( "Tune Parameters", visible=False ) gather_more_info_btn = gr.Button( "Gather More Info", visible=False ) # text_output = gr.Textbox() text_output = gr.HTML(self.reset()[1]) # Given a botton to provide student numbers and their inf. # stu_num = gr.Number(label="Student Number", precision=0) # stu_num = self.stu_num if self.task == "db_diag": user_msg = gr.Textbox() submit_btn = gr.Button("Submit", variant="primary") submit_btn.click( fn=self.submit, inputs=user_msg, outputs=[image_output, text_output], show_progress=False, ) else: pass # next_btn.click(fn=self.gen_output, inputs=None, outputs=[image_output, text_output], # show_progress=False) next_btn.click( fn=self.delay_gen_output, inputs=None, outputs=[ image_output, text_output, next_btn, start_autoplay_btn, rewrite_slow_query_btn, add_query_hints_btn, update_indexes_btn, tune_parameters_btn, gather_more_info_btn, solutions, ], show_progress=False, ) # [To-Do] Add botton: re-start (load different people and env) # reset_btn.click(fn=self.reset, inputs=stu_num, outputs=[image_output, text_output], # show_progress=False) # reset_btn.click(fn=self.reset, inputs=None, outputs=[image_output, text_output], show_progress=False) reset_btn.click( fn=self.delay_reset, inputs=[task_dropdown, api_key_text, organization_text, api_base_text], outputs=[ image_output, text_output, next_btn, stop_autoplay_btn, start_autoplay_btn, rewrite_slow_query_btn, add_query_hints_btn, update_indexes_btn, tune_parameters_btn, gather_more_info_btn, solutions, ], show_progress=False, ) stop_autoplay_btn.click( fn=self.stop_autoplay, inputs=None, outputs=[next_btn, stop_autoplay_btn, start_autoplay_btn], show_progress=False, ) start_autoplay_btn.click( fn=self.start_autoplay, inputs=None, outputs=[ image_output, text_output, next_btn, stop_autoplay_btn, start_autoplay_btn, rewrite_slow_query_btn, add_query_hints_btn, update_indexes_btn, tune_parameters_btn, gather_more_info_btn, solutions, ], show_progress=False, ) demo.queue(concurrency_count=5, max_size=20).launch() # demo.launch() GUI().launch()