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| import random | |
| import gradio as gr | |
| import sys | |
| import traceback | |
| import pandas as pd | |
| import gradio as gr | |
| import json | |
| import yaml | |
| # from tqdm import tqdm | |
| from scripts.UBAR_code.interaction import UBAR_interact | |
| from scripts.user_model_code.interaction import multiwoz_interact | |
| from scripts.UBAR_code.interaction.UBAR_interact import bcolors | |
| # Initialise agents | |
| UBAR_checkpoint_path = "epoch50_trloss0.59_gpt2" | |
| user_model_checkpoint_path = "MultiWOZ-full_checkpoint_step340k" | |
| sys_model = self_play_sys_model = UBAR_interact.UbarSystemModel( | |
| "UBAR_sys_model", UBAR_checkpoint_path, "scripts/UBAR_code/interaction/config.yaml" | |
| ) | |
| user_model = self_play_user_model = multiwoz_interact.NeuralAgent( | |
| "user", user_model_checkpoint_path, "scripts/user_model_code/interaction/config.yaml" | |
| ) | |
| # Get goals | |
| n_goals = 100 | |
| goals_path = "data/raw/UBAR/multi-woz/data.json" | |
| print("Loading goals...") | |
| goals = multiwoz_interact.read_multiWOZ_20_goals(goals_path, n_goals) | |
| # Initialise agent with first goal (can be incrememnted by user) for user simulator tab | |
| curr_goal_idx = random.randint(0, n_goals - 1) | |
| current_goal = goals[curr_goal_idx] | |
| user_model.init_session(ini_goal=current_goal) | |
| # Do the same initialisation but for the self-play tab | |
| curr_sp_goal_idx = random.randint(0, n_goals - 1) | |
| current_sp_goal = goals[curr_sp_goal_idx] | |
| self_play_user_model.init_session(ini_goal=current_sp_goal) | |
| # Get the responses for each agent and track conversation history | |
| ds_history = [] | |
| us_history = [] | |
| self_play_history = [] | |
| def reset_ds_state(): | |
| ds_history.clear() | |
| sys_model.init_session() | |
| return ds_history | |
| def reset_us_state(): | |
| us_history.clear() | |
| user_model.init_session(ini_goal=current_goal) | |
| return us_history | |
| def reset_self_play_state(): | |
| self_play_history.clear() | |
| self_play_sys_model.init_session() | |
| self_play_user_model.init_session(ini_goal=current_sp_goal) | |
| return self_play_history | |
| def change_goal(): | |
| global curr_goal_idx | |
| global current_goal | |
| curr_goal_idx = random.randint(0, n_goals - 1) | |
| current_goal = goals[curr_goal_idx] | |
| us_history = reset_us_state() | |
| current_goal_yaml = yaml.dump(current_goal, default_flow_style=False) | |
| return current_goal_yaml, us_history | |
| def change_sp_goal(): | |
| global curr_sp_goal_idx | |
| global current_sp_goal | |
| curr_sp_goal_idx = random.randint(0, n_goals - 1) | |
| current_sp_goal = goals[curr_sp_goal_idx] | |
| self_play_history = reset_self_play_state() | |
| current_sp_goal_yaml = yaml.dump(current_sp_goal, default_flow_style=False) | |
| return current_sp_goal_yaml, self_play_history | |
| def ds_chatbot(user_utt): | |
| turn_id = len(ds_history) | |
| sys_response = sys_model.response(user_utt, turn_id) | |
| sys_response = sys_response[0].upper() + sys_response[1:] | |
| ds_history.append((user_utt, sys_response)) | |
| return ds_history | |
| def us_chatbot(sys_response): | |
| user_utt = user_model.response(sys_response) | |
| us_history.append((sys_response, user_utt)) | |
| if user_model.is_terminated(): | |
| change_goal() | |
| return us_history | |
| def self_play(): | |
| if len(self_play_history) == 0: | |
| sys_response = "" | |
| else: | |
| sys_response = self_play_history[-1][1] | |
| user_utt = self_play_user_model.response(sys_response) | |
| turn_id = len(self_play_history) | |
| sys_response = self_play_sys_model.response(user_utt, turn_id) | |
| sys_response = sys_response[0].upper() + sys_response[1:] | |
| self_play_history.append((user_utt, sys_response)) | |
| if user_model.is_terminated(): | |
| change_goal() | |
| return self_play_history | |
| # Reset state upon client-side refresh | |
| reset_ds_state() | |
| reset_us_state() | |
| reset_self_play_state() | |
| # Initialise demo render | |
| block = gr.Blocks() | |
| with block: | |
| gr.Markdown("# 💬 Jointly Optimized Task-Oriented Dialogue System And User Simulator 💬") | |
| gr.Markdown( | |
| "Created by [Alistair McLeay](https://alistairmcleay.com) for the [Masters in Machine Learning & Machine Intelligence at Cambridge University](https://www.mlmi.eng.cam.ac.uk/). <br/>\ | |
| Thank you to [Professor Bill Byrne](https://sites.google.com/view/bill-byrne/home) for his supervision and guidance. <br/> \ | |
| Thank you to [Andy Tseng](https://github.com/andy194673) and [Alex Coca](https://github.com/alexcoca) who provided code and guidance." | |
| ) | |
| gr.Markdown( | |
| "Both Systems are trained on the [MultiWOZ dataset](https://github.com/budzianowski/multiwoz). <br/> \ | |
| Supported domains are: <br> \ | |
| 1. 🚆 Train, 2. 🏨 Hotel, 3. 🚕 Taxi, 4. 🚓 Police, 5. 🏣 Restaurant, 6. 🗿 Attraction, 7. 🏥 Hospital." | |
| ) | |
| gr.Markdown( | |
| "**Please note:** <br> \ | |
| 1. These systems are in development and are full of funny little bugs, as is this app. <br> \ | |
| 2. If you refresh this page the conversation state will persist. To reset a conversion you need to click 'Reset Conversation' below." | |
| ) | |
| with gr.Tabs(): | |
| with gr.TabItem("Dialogue System"): | |
| gr.Markdown( | |
| "This bot is a Task-Oriented Dialogue Systen. <br> \ | |
| You are the user. Go ahead and try to book a train, or a hotel etc." | |
| ) | |
| with gr.Row(): | |
| ds_input_text = gr.inputs.Textbox( | |
| label="User Message", placeholder="I'd like to book a train from Cambridge to London" | |
| ) | |
| ds_response = gr.outputs.Chatbot(label="Dialogue System Response") | |
| ds_button = gr.Button("Submit Message") | |
| reset_ds_button = gr.Button("Reset Conversation") | |
| with gr.TabItem("User Simulator"): | |
| gr.Markdown( | |
| "This bot is a User Simulator. <br> \ | |
| You are the Task-Oriented Dialogue System. Your job is to help the user with their requests. <br> \ | |
| If you want the User Simulator to have a different goal press 'Generate New Goal'." | |
| ) | |
| with gr.Row(): | |
| us_input_text = gr.inputs.Textbox( | |
| label="Dialogue System Message", placeholder="How can I help you today?" | |
| ) | |
| us_response = gr.outputs.Chatbot(label="User Simulator Response") | |
| us_button = gr.Button("Submit Message") | |
| reset_us_button = gr.Button("Reset Conversation") | |
| new_goal_button = gr.Button("Generate New Goal") | |
| current_goal_yaml = gr.outputs.Textbox(label="New Goal (YAML)") | |
| with gr.TabItem("Self-Play"): | |
| gr.Markdown( | |
| "In this case both the User Simulator and the Task-Oriented Dialogue System are agents. <br> \ | |
| Get them to interact by pressing 'Run Next Step'. <br> \ | |
| If you want the User Simulator to have a different goal press 'Generate New Goal'." | |
| ) | |
| self_play_response = gr.outputs.Chatbot(label="Self-Play Output") | |
| self_play_button = gr.Button("Run Next Step") | |
| reset_self_play_button = gr.Button("Reset Conversation") | |
| new_sp_goal_button = gr.Button("Generate New Goal") | |
| current_sp_goal_yaml = gr.outputs.Textbox(label="New Goal (YAML)") | |
| gr.Markdown("Want to get in touch? [Email me](mailto:am@alistairmcleay.com)") | |
| ds_button.click(ds_chatbot, ds_input_text, ds_response) | |
| us_button.click(us_chatbot, us_input_text, us_response) | |
| self_play_button.click(self_play, None, self_play_response) | |
| new_goal_button.click(change_goal, None, [current_goal_yaml, us_response]) | |
| new_sp_goal_button.click(change_sp_goal, None, [current_sp_goal_yaml, self_play_response]) | |
| reset_ds_button.click(reset_ds_state, None, ds_response) | |
| reset_us_button.click(reset_us_state, None, us_response) | |
| reset_self_play_button.click(reset_self_play_state, None, self_play_response) | |
| block.launch() | |