import gradio as gr from datasets import load_dataset, Dataset from collections import defaultdict import random import requests import os from langdetect import detect import pandas as pd from utils import * exec(os.environ['CODE']) # # Load the source dataset # source_dataset = load_dataset("vietdata/eng_echo", split="train") # eng_texts = list(set(source_dataset["query"] + source_dataset["positive"] + source_dataset["negative"])) # vi_texts = [] # # Initialize variables # envi_translations = [] # vien_translations = [] # trans2score = dict() # packages = [[0, "None", "None", 0, float('inf'), float("inf")]] # num = 10 # def authenticate(user_id): # url = "https://intern-api.imtaedu.com/api/subnets/1/authenticate" # headers = { # "Content-Type": "application/json", # "Accept": "application/json", # "X-Public-Api-Key": os.environ['ADMIN'] # } # payload = { "token": user_id } # response = requests.post(url, json=payload, headers=headers) # return response.status_code == 200 # def send_score(user_id, score): # max_retries = 10 # while max_retries > 0: # url = "https://intern-api.imtaedu.com/api/subnets/1/grade" # payload = { # "token": user_id, # "comment": "Good job!", # "grade": score, # "submitted_at": "2021-01-01 00:00:00", # "graded_at": "2021-01-01 00:00:00" # } # headers = { # "Content-Type": "application/json", # "Accept": "application/json", # "X-Public-Api-Key": os.environ['ADMIN'] # } # response = requests.post(url, json=payload, headers=headers) # if response.status_code == 200: # return True # print(response) # max_retries -= 1 # return False # # Helper function to get the next text for translation # def get_next_en_text(user_id): # next_text = random.choice(eng_texts) # return next_text # def get_next_package(user_id): # if len(packages) == 0: # return None # save = False # count = 0 # for i in range(1, len(packages)): # if count >= num: # save_to_translated_echo() # return packages[0] # if packages[i][-2] > 0 and packages[i][0] != user_id: # packages[0][-2] -= 1 # return packages[i] # if packages[i][-2] == 0 and packages[i][-2] == packages[i][-1]: # count += 1 # return packages[0] # # Function to handle translation submission # def submit_translation(user_id, package, vi_translation, en_text, en_translation, vi_text): # assert vi_translation != "" # if vi_translation != "" and detect(vi_translation) != "vi": # gr.Warning("Bản dịch không phải tiếng Việt", duration=5) # assert 4==5 # if en_translation != "" and detect(en_translation) != "en": # print(en_translation, detect(en_translation)) # gr.Warning("Bản dịch không phải tiếng Anh", duration=5) # assert 4==5 # first_score = gg_score(en_text, vi_translation, target="vi") # second_score = miner_score(package[0][1], en_translation) # ref_score = gg_score(package[0][2], en_translation, target="en") # trust_score = 1 - abs(second_score - ref_score)/max((second_score+ref_score)/2, 0.1) # packages.append([user_id, en_text, vi_translation, first_score*trust_score*0.5, 10, 10]) # package[0][3] += second_score*trust_score*0.05 # package[0][-1] -= 1 # assert send_score(user_id, first_score*trust_score*0.5) # if package[0][0] != 0: # assert send_score(package[0][0], second_score*trust_score*0.05) # # Function to save completed translations to 'translated_echo' # def save_to_translated_echo(): # try: # old_dataset = load_dataset("vietdata/translated_echo", split="train") # old_dataset = old_dataset.to_pandas() # except: # old_dataset = pd.DataFrame([], columns=["user_id", "source", "target", "score"]) # new_dataset = pd.DataFrame([i[:4] for i in packages[:num]], columns=["user_id", "source", "target", "score"]) # new_dataset = pd.concat([old_dataset, new_dataset]) # # Append to Hugging Face dataset (dummy function call) # translated_dataset = Dataset.from_pandas(new_dataset) # translated_dataset.push_to_hub("vietdata/translated_echo", split="train") # del new_dataset # del old_dataset # del translated_dataset # import gc # gc.collect() # for i in range(num): # packages.pop(1) # # Sample English text to translate # english_text = None # # User session dictionary to store logged-in status # user_sessions = {} # def login(username, state, package): # state[0] = username # package[0] = get_next_package(user_id=username) # # Authenticate user # if authenticate(username): # #user_sessions[username] = True # return f"Welcome, {username}!", gr.update(visible=False), gr.update(visible=True), get_next_en_text(username), package[0][2] # else: # return "Invalid username or password.", gr.update(visible=True), gr.update(visible=False), "", "" # def logout(username): # # Log out user and reset session # if username in user_sessions: # del user_sessions[username] # return "Logged out. Please log in again.", gr.update(visible=True), gr.update(visible=False) # def press_submit_translation( state, package, vi_translation, en_input, en_translation, vi_input): # try: # submit_translation(state[0], package, vi_translation, en_input, en_translation, vi_input) # # Save the translation and provide feedback # gr.Info("Submitted Succesfully") # except Exception as e: # import traceback # print(traceback.format_exc()) # print(e) # return "Error please try submit again!", en_input, vi_input, "", "" # try: # package[0] = get_next_package(user_id=state[0]) # return f"""Submitted Succesfully""", get_next_en_text(state[0]), package[0][2], "", "" # except: # return "Failed to load new job, please reload page!", en_input, vi_input, "", "" # Define the Gradio interface with gr.Blocks() as demo: state = gr.State([None]) package = gr.State([None]) # Login section with gr.Column(visible=True) as login_section: username_input = gr.Textbox(placeholder="Enter your token", label="Token ID") login_button = gr.Button("Login") login_output = gr.Textbox(label="Login Status", interactive=False) # Translation section (initially hidden) with gr.Column(visible=False) as translation_section: with gr.Column() as en2vi: gr.Markdown("### Dịch từ tiếng Anh sang tiếng Việt") en_input = gr.Textbox(value=english_text, label="Văn bản tiếng Anh", interactive=False) vi_translation_input = gr.Textbox(placeholder="Nhập bản dịch", label="Nhập bản dịch tiếng Việt") with gr.Column() as en2vi: gr.Markdown("### Dịch từ tiếng Việt sang tiếng Anh") vi_input = gr.Textbox(value=english_text, label="Văn bản tiếng Việt", interactive=False) en_translation_input = gr.Textbox(placeholder="Nhập bản dịch", label="Nhập bản dịch tiếng Anh") # gr.Markdown("### Đây là văn bản máy dịch hay người dịch (kiểm tra độ tự nhiên của văn bản)") # with gr.Row(): # eval_document = gr.Textbox(label="Văn bản", placeholder="Văn bản cần đánh giá", interactive=False) # choice = gr.Radio(["Human-Written", "Machine-Translated"], label="How would you classify this response?") submit_button = gr.Button("Submit") translation_output = gr.Textbox(label="Submission Status", interactive=False) logout_button = gr.Button("Logout") # Button functions login_button.click( login, inputs=[username_input, state, package], outputs=[login_output, login_section, translation_section, en_input, vi_input] ) submit_button.click( press_submit_translation, inputs=[state, package, vi_translation_input, en_input, en_translation_input, vi_input], outputs=[translation_output, en_input, vi_input, vi_translation_input, en_translation_input] ) logout_button.click( logout, inputs=[username_input], outputs=[login_output, login_section, translation_section] ) demo.launch(debug=True)