import gradio as gr
from transformers import pipeline
import requests

# Load translation pipelines
translator_en_fi = pipeline(
    "translation_en_to_fi", 
    model="Helsinki-NLP/opus-mt-en-fi", 
    # Optional: cache_dir="./model_cache", 
    # Optional parameters:
    # max_length=512, num_beams=5
)
translator_fi_en = pipeline(
    "translation_fi_to_en", 
    model="Helsinki-NLP/opus-mt-fi-en",
    # max_length=512, num_beams=5
)

def translate(text, direction):
    text = text.strip()
    if not text:
        return "Please enter some text for translation."
    
    # Simple input validation
    if len(text) > 2000:
        return "Input text too long. Please shorten it."
    
    if direction == 'en-fi':
        result = translator_en_fi(text)[0]['translation_text']
    else:
        result = translator_fi_en(text)[0]['translation_text']
    return result

# Optional: Provide some example texts to guide users.
examples = [
    ["Hello, how are you?", "en-fi"],
    ["Mitä kuuluu?", "fi-en"]
]

iface = gr.Interface(
    fn=translate,
    inputs=[
        gr.Textbox(lines=3, placeholder="Enter text here..."),
        gr.Radio(choices=["en-fi", "fi-en"], label="Translation Direction", value="en-fi")
    ],
    outputs=gr.Textbox(label="Translated Text"),
    title="English-Finnish Translation App",
    description=(
        "This application uses Helsinki-NLP translation models "
        "to translate text between English and Finnish."
    ),
    examples=examples,
    allow_flagging="never",  # Disables any flagging if you don't need it
    enable_queue=True  # Allows request queuing if concurrency is needed
)

iface.launch()