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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)