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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) | |