import torch
import gradio as gr
import time
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from flores200_codes import flores_codes

# Load models and tokenizers once during initialization
def load_models():
    model_name_dict = {
        "nllb-distilled-600M": "facebook/nllb-200-distilled-600M",
    }

    model_dict = {}

    for call_name, real_name in model_name_dict.items():
        print("\tLoading model:", call_name)
        model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
        tokenizer = AutoTokenizer.from_pretrained(real_name)
        model_dict[call_name] = {
            "model": model,
            "tokenizer": tokenizer,
        }

    return model_dict

# Translate text using preloaded models and tokenizers
def translate_text(source_lang, target_lang, input_text, model_dict):
    model_name = "nllb-distilled-600M"

    start_time = time.time()
    source_code = flores_codes[source_lang]
    target_code = flores_codes[target_lang]

    model = model_dict[model_name]["model"]
    tokenizer = model_dict[model_name]["tokenizer"]

    translator = pipeline(
        "translation",
        model=model,
        tokenizer=tokenizer,
        src_lang=source_code,
        tgt_lang=target_code,
    )
    translated_output = translator(input_text, max_length=400)

    end_time = time.time()

    translated_result = {
        "inference_time": end_time - start_time,
        "source": source_lang,
        "target": target_lang,
        "result": translated_output[0]["translation_text"],
    }
    return translated_result

if __name__ == "__main__":
    print("\tInitializing models")

    # Load models and tokenizers
    model_dict = load_models()  # Ensure that this line initializes model_dict correctly

    lang_codes = list(flores_codes.keys())
    inputs = [
        gr.inputs.Dropdown(lang_codes, default="English", label="Source Language"),
        gr.inputs.Dropdown(lang_codes, default="Nepali", label="Target Language"),
        gr.inputs.Textbox(lines=5, label="Input Text"),
    ]

    outputs = gr.outputs.JSON()

    title = "The Master Betters Translator"

    app_description = (
        "This is a beta version of The Master Betters Translator that utilizes pre-trained language models for translation."
    )
    examples = [["English", "Nepali", "Hello, how are you?"]]

    gr.Interface(
        fn=translate_text,
        inputs=inputs,
        outputs=outputs,
        title=title,
        description=app_description,
        examples=examples,
        examples_per_page=50,
    ).launch()