#!/usr/bin/env python #encoding=utf-8 try: # for Python2 from Tkinter import * import ttk import tkMessageBox as messagebox except ImportError: # for Python3 from tkinter import * from tkinter import ttk from tkinter import messagebox from tkinter import filedialog import os import sys import platform import pyperclip import base64 import time import threading import socket import asyncio import tornado from tornado.web import Application CONST_APP_VERSION = "MaxBot (2023.6.17)" CONST_MAXBOT_QUESTION_FILE = "MAXBOT_QUESTION.txt" CONST_SERVER_PORT_DEFAULT = 8888 CONST_SERVER_PORT = CONST_SERVER_PORT_DEFAULT def get_ip_address(): ip = [l for l in ([ip for ip in socket.gethostbyname_ex(socket.gethostname())[2] if not ip.startswith("127.")][:1], [[(s.connect(('8.8.8.8', 53)), s.getsockname()[0], s.close()) for s in [socket.socket(socket.AF_INET, socket.SOCK_DGRAM)]][0][1]]) if l][0][0] return ip def btn_copy_ip_clicked(): local_ip = get_ip_address() ip_address = "http://%s:%d/" % (local_ip,CONST_SERVER_PORT) pyperclip.copy(ip_address) def btn_copy_question_clicked(): global txt_question question_text = txt_question.get("1.0",END).strip() pyperclip.copy(question_text) def btn_paste_answer_by_user(): print("btn_paste_answer_by_user") def TextInput(root, UI_PADDING_X): row_count = 0 frame_group_header = Frame(root) group_row_count = 0 global lbl_ip lbl_ip = Label(frame_group_header, text="IP") lbl_ip.grid(column=0, row=group_row_count, sticky = E) local_ip = get_ip_address() ip_address = "http://%s:8888/" % (local_ip) global lbl_ip_address lbl_ip_address = Label(frame_group_header, text=ip_address) lbl_ip_address.grid(column=1, row=group_row_count, sticky = W) icon_copy_filename = "icon_copy_2.gif" icon_copy_img = PhotoImage(file=icon_copy_filename) lbl_icon_copy_ip = Label(frame_group_header, image=icon_copy_img, cursor="hand2") lbl_icon_copy_ip.image = icon_copy_img lbl_icon_copy_ip.grid(column=2, row=group_row_count, sticky = W+N) lbl_icon_copy_ip.bind("", lambda e: btn_copy_ip_clicked()) group_row_count += 1 global lbl_question lbl_question = Label(frame_group_header, text="Question") lbl_question.grid(column=0, row=group_row_count, sticky = E+N) global txt_question txt_question = Text(frame_group_header, width=50, height=15) txt_question.grid(column=1, row=group_row_count, sticky = W) txt_question.insert("1.0", "") lbl_icon_copy_question = Label(frame_group_header, image=icon_copy_img, cursor="hand2") lbl_icon_copy_question.image = icon_copy_img lbl_icon_copy_question.grid(column=2, row=group_row_count, sticky = W+N) lbl_icon_copy_question.bind("", lambda e: btn_copy_question_clicked()) group_row_count += 1 global lbl_answer lbl_answer = Label(frame_group_header, text="Answer") lbl_answer.grid(column=0, row=group_row_count, sticky = E) global txt_answer global txt_answer_value txt_answer_value = StringVar(frame_group_header, value="") txt_answer = Entry(frame_group_header, width=30, textvariable = txt_answer_value) txt_answer.grid(column=1, row=group_row_count, sticky = W) txt_answer.bind('', lambda e: btn_paste_answer_by_user()) frame_group_header.grid(column=0, row=row_count, padx=UI_PADDING_X, pady=15) def main_ui(): global root root = Tk() root.title(CONST_APP_VERSION) global UI_PADDING_X UI_PADDING_X = 20 TextInput(root, UI_PADDING_X) GUI_SIZE_WIDTH = 530 GUI_SIZE_HEIGHT = 360 GUI_SIZE_MACOS = str(GUI_SIZE_WIDTH) + 'x' + str(GUI_SIZE_HEIGHT) GUI_SIZE_WINDOWS=str(GUI_SIZE_WIDTH-50) + 'x' + str(GUI_SIZE_HEIGHT-55) GUI_SIZE =GUI_SIZE_MACOS if platform.system() == 'Windows': GUI_SIZE = GUI_SIZE_WINDOWS root.geometry(GUI_SIZE) # for icon. icon_filepath = 'tmp.ico' # icon format. iconImg = 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' if platform.system() == 'Linux': # PNG format. iconImg = 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' tmpIcon = open(icon_filepath, 'wb+') tmpIcon.write(base64.b64decode(iconImg)) tmpIcon.close() if platform.system() == 'Windows': root.iconbitmap(icon_filepath) if platform.system() == 'Darwin': #from PIL import Image, ImageTk #logo = ImageTk.PhotoImage(Image.open(icon_filepath).convert('RGB')) #root.call('wm', 'iconphoto', root._w, logo) pass if platform.system() == 'Linux': logo = PhotoImage(file=icon_filepath) root.call('wm', 'iconphoto', root._w, logo) os.remove(icon_filepath) root.mainloop() class MainHandler(tornado.web.RequestHandler): def format_config_keyword_for_json(self, user_input): if len(user_input) > 0: if not ('\"' in user_input): user_input = '"' + user_input + '"' return user_input def compose_as_json(self, user_input): user_input = self.format_config_keyword_for_json(user_input) return "{\"data\":[%s]}" % user_input def get(self): global txt_answer_value answer_text = "" try: answer_text = txt_answer_value.get().strip() except Exception as exc: pass answer_text_output = self.compose_as_json(answer_text) #print("answer_text_output:", answer_text_output) self.write(answer_text_output) class QuestionHandler(tornado.web.RequestHandler): def get(self): global txt_question txt_question.insert("1.0", "") async def main_server(): app = Application([ (r"/", MainHandler), (r"/query", MainHandler), (r"/question", QuestionHandler), ]) app.listen(CONST_SERVER_PORT) await asyncio.Event().wait() def web_server(): asyncio.run(main_server()) def preview_question_text_file(): if os.path.exists(CONST_MAXBOT_QUESTION_FILE): question_text = "" with open(CONST_MAXBOT_QUESTION_FILE, "r") as text_file: question_text = text_file.readline() global txt_question try: displayed_question_text = txt_question.get("1.0",END).strip() if displayed_question_text != question_text: # start to refresh txt_question.delete("1.0","end") if len(question_text) > 0: txt_question.insert("1.0", question_text) except Exception as exc: pass def text_server_timer(): while True: preview_question_text_file() time.sleep(0.2) if __name__ == "__main__": threading.Thread(target=text_server_timer, daemon=True).start() threading.Thread(target=web_server, daemon=True).start() main_ui()