app.py

import gradio as gr
from datasets import load_dataset, Dataset
from collections import defaultdict
import random
import requests
import os 
# Load the source dataset
source_dataset = load_dataset("vietdata/eng_echo", split="train")
source_texts = source_dataset["query"]

# Initialize variables
translations = defaultdict(list)
processed_data = []

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

# Helper function to get the next text for translation
def get_next_text(user_id):
    # Filter texts that already have 10 translations
    eligible_texts = [text for text in source_texts if len(translations[text]) < 10]
    if not eligible_texts:
        return "All texts are fully translated."

    # Select a random eligible text for translation
    next_text = random.choice(eligible_texts)
    return next_text

# Function to handle translation submission
def submit_translation(user_id, original_text, translation):
    # Check if text already has 10 translations
    if len(translations[original_text]) < 10:
        translations[original_text].append((user_id, translation))

        # Check if 100 texts have enough translations to save
        if len([t for t in translations if len(translations[t]) == 10]) >= 100:
            save_to_translated_echo()

        return "Translation submitted successfully."
    else:
        return "This text already has 10 translations. Please request a new text."

# Function to save completed translations to 'translated_echo'
def save_to_translated_echo():
    global translations, processed_data

    # Gather translations with exactly 10 versions
    completed_translations = [
        {"query": text, "translations": [t[1] for t in translations[text]]}
        for text in translations if len(translations[text]) == 10
    ]

    # Append to processed data
    processed_data.extend(completed_translations)

    # Reset translations
    translations = {text: val for text, val in translations.items() if len(val) < 10}

    # Convert to Hugging Face dataset format
    translated_dataset = Dataset.from_pandas(pd.DataFrame(processed_data))

    # Append to Hugging Face dataset (dummy function call)
    translated_dataset.push_to_hub("vietdata/translated_echo", split="train")

import gradio as gr

# Simulated user data for demonstration
user_data = {"hello": "hello"}

# Sample English text to translate
english_text = "Translate this text to Vietnamese."

# User session dictionary to store logged-in status
user_sessions = {}

def login(username, state):
    state[0] = username
    
    # Authenticate user
    if authenticate(username):
        #user_sessions[username] = True
        return f"Welcome, {username}!", gr.update(visible=False), gr.update(visible=True), get_next_text(username)
    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 submit_translation(translation, state, job_input):
    try:
        submit_translation(state[0], job_input, translation)
        origin = job_input
        # Save the translation and provide feedback
        return f"""Translation of "{origin}" submitted: {translation}""", get_next_text(state[0])
    except Exception as e:
        print(e)
        return "Error please try submit again!", job_input

# Define the Gradio interface
with gr.Blocks() as demo:
    state = 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:
        job_input = gr.Textbox(value=english_text, label="English Text", interactive=False)
        translation_input = gr.Textbox(placeholder="Enter your translation here", label="Your Translation")
        submit_button = gr.Button("Submit Translation")
        translation_output = gr.Textbox(label="Submission Status", interactive=False)
        logout_button = gr.Button("Logout")

    # Button functions
    login_button.click(
        login, inputs=[username_input, state], outputs=[login_output, login_section, translation_section, job_input]
    )
    submit_button.click(
        submit_translation, inputs=[translation_input, state, job_input], outputs=[translation_output, job_input]
    )
    logout_button.click(
        logout, inputs=[username_input], outputs=[login_output, login_section, translation_section]
    )

demo.launch(debug=True)