app.py

import gradio as gr
import spaces
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, AutoModel, pipeline
from transformers import logging as hflogging
import languagecodes
import httpx, os
import polars as pl 

hflogging.set_verbosity_error()
favourite_langs = {"German": "de", "Romanian": "ro", "English": "en", "-----": "-----"}
df = pl.read_parquet("isolanguages.parquet")
non_empty_isos = df.slice(1).filter(pl.col("ISO639-1") != "").rows()
# all_langs = languagecodes.iso_languages_byname
all_langs = {iso[0]: (iso[1], iso[2], iso[3]) for iso in non_empty_isos} # {'Romanian': ('ro', 'rum', 'ron')}
# iso1_to_name = {codes[0]: lang for entry in all_langs for lang, codes in entry.items()} # {'ro': 'Romanian', 'de': 'German'}
iso1_to_name = {iso[1]: iso[0] for iso in non_empty_isos} # {'ro': 'Romanian', 'de': 'German'}
langs = list(favourite_langs.keys())
langs.extend(list(all_langs.keys())) # Language options as list, add favourite languages first

models = ["Helsinki-NLP",
          "Helsinki-NLP/opus-mt-tc-bible-big-mul-mul", "Helsinki-NLP/opus-mt-tc-bible-big-mul-deu_eng_nld",
          "Helsinki-NLP/opus-mt-tc-bible-big-mul-deu_eng_fra_por_spa", "Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mul",
          "Helsinki-NLP/opus-mt-tc-bible-big-roa-deu_eng_fra_por_spa", "Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-roa",
          "facebook/nllb-200-distilled-600M", "facebook/nllb-200-distilled-1.3B", "facebook/nllb-200-1.3B", "facebook/nllb-200-3.3B",
          "facebook/mbart-large-50-many-to-many-mmt", "facebook/mbart-large-50-one-to-many-mmt", "facebook/mbart-large-50-many-to-one-mmt",
          "facebook/m2m100_418M", "facebook/m2m100_1.2B", "Lego-MT/Lego-MT",
          "bigscience/mt0-small", "bigscience/mt0-base", "bigscience/mt0-large", "bigscience/mt0-xl",
          "bigscience/bloomz-560m", "bigscience/bloomz-1b1", "bigscience/bloomz-1b7", "bigscience/bloomz-3b",
          "t5-small", "t5-base", "t5-large",
          "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large", "google/flan-t5-xl",
          "google/madlad400-3b-mt", "jbochi/madlad400-3b-mt",
          "Argos", "Google",
          "HuggingFaceTB/SmolLM3-3B", "winninghealth/WiNGPT-Babel-2",
          "utter-project/EuroLLM-1.7B", "utter-project/EuroLLM-1.7B-Instruct",
          "Unbabel/Tower-Plus-2B", "Unbabel/TowerInstruct-7B-v0.2", "Unbabel/TowerInstruct-Mistral-7B-v0.2",
          "openGPT-X/Teuken-7B-instruct-commercial-v0.4", "openGPT-X/Teuken-7B-instruct-v0.6"
          ]
DEFAULTS = [langs[0], langs[1], models[0]]

def model_to_cuda(model):
    # Move the model to GPU if available
    if torch.cuda.is_available():
        model = model.to('cuda')
        print("CUDA is available! Using GPU.")
    else:
        print("CUDA not available! Using CPU.")
    return model

def HelsinkiNLPAutoTokenizer(sl, tl, input_text): # deprecated
    if model_name == "Helsinki-NLP":
        message_text = f'Translated from {sl} to {tl} with {model_name}.'
        try:
            model_name = f"Helsinki-NLP/opus-mt-{sl}-{tl}"
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = model_to_cuda(AutoModelForSeq2SeqLM.from_pretrained(model_name))
        except EnvironmentError:
            try:   
                model_name = f"Helsinki-NLP/opus-tatoeba-{sl}-{tl}"
                tokenizer = AutoTokenizer.from_pretrained(model_name)
                model = model_to_cuda(AutoModelForSeq2SeqLM.from_pretrained(model_name))
                input_ids = tokenizer.encode(prompt, return_tensors="pt")
                output_ids = model.generate(input_ids, max_length=512)
                translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
                return translated_text, message_text
            except EnvironmentError as error:
                return f"Error finding model: {model_name}! Try other available language combination.", error
                      
class Translators:
    def __init__(self, model_name: str, sl: str, tl: str, input_text: str):
        self.model_name = model_name
        self.sl, self.tl = sl, tl
        self.input_text = input_text
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
    def google(self):  
        url = os.environ['GCLIENT'] + f'sl={self.sl}&tl={self.tl}&q={self.input_text}'
        response = httpx.get(url)
        return response.json()[0][0][0]
    
    @classmethod
    def download_argos_model(cls, from_code, to_code):
        import argostranslate.package
        print('Downloading model', from_code, to_code) 
        # Download and install Argos Translate package
        argostranslate.package.update_package_index()
        available_packages = argostranslate.package.get_available_packages()
        package_to_install = next(
            filter(lambda x: x.from_code == from_code and x.to_code == to_code, available_packages)
        )
        argostranslate.package.install_from_path(package_to_install.download())
    
    def argos(self):
        import argostranslate.translate, argostranslate.package
        try:
            Translators.download_argos_model(self.sl, self.tl) # Download model
            translated_text = argostranslate.translate.translate(self.input_text, self.sl, self.tl) # Translate
        except StopIteration:
            # packages_info = ', '.join(f"{pkg.get_description()}->{str(pkg.links)} {str(pkg.source_languages)}" for pkg in argostranslate.package.get_available_packages())
            packages_info = ', '.join(f"{pkg.from_name} ({pkg.from_code}) -> {pkg.to_name} ({pkg.to_code})" for pkg in argostranslate.package.get_available_packages())
            translated_text = f"No Argos model for {self.sl} to {self.tl}. Try other model or languages combination from the available Argos models: {packages_info}."
        except Exception as error:
            translated_text = error
        return translated_text

    def HelsinkiNLP_mulroa(self):
        try:
            pipe = pipeline("translation", model=self.model_name, device=self.device)                
            iso1to3 = {iso[1]: iso[3] for iso in non_empty_isos} # {'ro': 'ron'}
            iso3tl = iso1to3.get(self.tl) # 'deu', 'ron', 'eng', 'fra'
            translation = pipe(f'>>{iso3tl}<< {self.input_text}')
            return translation[0]['translation_text'], f'Translated from {iso1_to_name[self.sl]} to {iso1_to_name[self.tl]} with {self.model_name}.'
        except Exception as error:
            return f"Error translating with model: {self.model_name}! Try other available language combination.", error
    
    def HelsinkiNLP(self):
        try: # Standard bilingual model
            model_name = f"Helsinki-NLP/opus-mt-{self.sl}-{self.tl}"
            pipe = pipeline("translation", model=model_name, device=self.device)
            translation = pipe(self.input_text)
            return translation[0]['translation_text'], f'Translated from {iso1_to_name[self.sl]} to {iso1_to_name[self.tl]} with {model_name}.'
        except EnvironmentError:
            try: # Tatoeba models
                model_name = f"Helsinki-NLP/opus-tatoeba-{self.sl}-{self.tl}"
                pipe = pipeline("translation", model=model_name, device=self.device)
                translation = pipe(self.input_text)
                return translation[0]['translation_text'], f'Translated from {iso1_to_name[self.sl]} to {iso1_to_name[self.tl]} with {model_name}.'
            except EnvironmentError as error:
                self.model_name = "Helsinki-NLP/opus-mt-tc-bible-big-mul-mul" # Last resort: try multi to multi
                return self.HelsinkiNLP_mulroa()
        except KeyError as error:
            return f"Error: Translation direction {self.sl} to {self.tl} is not supported by Helsinki Translation Models", error
  
    def LegoMT(self):
        from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
        model = M2M100ForConditionalGeneration.from_pretrained(self.model_name) # "Lego-MT/Lego-MT"
        tokenizer = M2M100Tokenizer.from_pretrained(self.model_name)
        tokenizer.src_lang = self.sl
        encoded = tokenizer(self.input_text, return_tensors="pt")
        generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.get_lang_id(self.tl))
        return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
        
    def madlad(self):
        model = T5ForConditionalGeneration.from_pretrained(self.model_name, device_map="auto")
        tokenizer = T5Tokenizer.from_pretrained(self.model_name)
        text = f"<2{self.tl}> {self.input_text}"
        # input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
        # outputs = model.generate(input_ids=input_ids, max_new_tokens=512)    
        # return tokenizer.decode(outputs[0], skip_special_tokens=True)
        # return tokenizer.batch_decode(outputs, skip_special_tokens=True)
        # Use a pipeline as a high-level helper
        translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=self.sl, tgt_lang=self.tl)
        translated_text = translator(text, max_length=512)
        return translated_text[0]['translation_text']
    
    def smollm(self):
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        model = AutoModelForCausalLM.from_pretrained(self.model_name)
        prompt = f"""Translate the following {self.sl} text to {self.tl}, generating only the translated text and maintaining the original meaning and tone:
        {self.input_text}
        Translation:"""
        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(
            inputs.input_ids,
            max_length=len(inputs.input_ids[0]) + 150,
            temperature=0.3,
            do_sample=True
        ) 
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(response)
        return response.split("Translation:")[-1].strip()

    def flan(self):
        tokenizer = T5Tokenizer.from_pretrained(self.model_name, legacy=False)
        model = T5ForConditionalGeneration.from_pretrained(self.model_name)
        prompt = f"translate {self.sl} to {self.tl}: {self.input_text}"
        input_ids = tokenizer(prompt, return_tensors="pt").input_ids
        outputs = model.generate(input_ids)
        return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

    def tfive(self):
        tokenizer = T5Tokenizer.from_pretrained(self.model_name)
        model = T5ForConditionalGeneration.from_pretrained(self.model_name, device_map="auto")
        prompt = f"translate {self.sl} to {self.tl}: {self.input_text}"
        input_ids = tokenizer.encode(prompt, return_tensors="pt")
        output_ids = model.generate(input_ids, max_length=512)
        translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
        return translated_text
    
    def mbart_many_to_many(self):
        from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
        model = MBartForConditionalGeneration.from_pretrained(self.model_name)
        tokenizer = MBart50TokenizerFast.from_pretrained(self.model_name)
        # translate source to target
        tokenizer.src_lang = languagecodes.mbart_large_languages[self.sl]
        encoded = tokenizer(self.input_text, return_tensors="pt")
        generated_tokens = model.generate(
            **encoded,
            forced_bos_token_id=tokenizer.lang_code_to_id[languagecodes.mbart_large_languages[self.tl]]
        )
        return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    
    def mbart_one_to_many(self):
        # translate from English
        from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
        model = MBartForConditionalGeneration.from_pretrained(self.model_name)
        tokenizer = MBart50TokenizerFast.from_pretrained(self.model_name, src_lang="en_XX")
        model_inputs = tokenizer(self.input_text, return_tensors="pt")
        langid = languagecodes.mbart_large_languages[self.tl]
        generated_tokens = model.generate(
            **model_inputs,
            forced_bos_token_id=tokenizer.lang_code_to_id[langid]
        )
        return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    
    def mbart_many_to_one(self):
        # translate to English
        from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
        model = MBartForConditionalGeneration.from_pretrained(self.model_name)
        tokenizer = MBart50TokenizerFast.from_pretrained(self.model_name)
        tokenizer.src_lang = languagecodes.mbart_large_languages[self.sl]
        encoded = tokenizer(self.input_text, return_tensors="pt")
        generated_tokens = model.generate(**encoded)
        return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
        
    def mtom(self):
        from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
        model = M2M100ForConditionalGeneration.from_pretrained(self.model_name)
        tokenizer = M2M100Tokenizer.from_pretrained(self.model_name)
        tokenizer.src_lang = self.sl
        encoded = tokenizer(self.input_text, return_tensors="pt")
        generated_tokens = model.generate(**encoded, forced_bos_token_id=tokenizer.get_lang_id(self.tl))
        return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]

    def bigscience(self):  
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
        self.input_text = self.input_text if self.input_text.endswith('.') else f'{self.input_text}.'
        inputs = tokenizer.encode(f"Translate to {self.tl}: {self.input_text}", return_tensors="pt")
        outputs = model.generate(inputs)
        translation = tokenizer.decode(outputs[0])
        translation = translation.replace('<pad> ', '').replace('</s>', '')
        return translation
    
    def bloomz(self):  
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        model = AutoModelForCausalLM.from_pretrained(self.model_name)
        self.input_text = self.input_text if self.input_text.endswith('.') else f'{self.input_text}.'
        # inputs = tokenizer.encode(f"Translate from {self.sl} to {self.tl}: {self.input_text} Translation:", return_tensors="pt")
        inputs = tokenizer.encode(f"Translate to {self.tl}: {self.input_text}", return_tensors="pt")
        outputs = model.generate(inputs)
        translation = tokenizer.decode(outputs[0])
        translation = translation.replace('<pad> ', '').replace('</s>', '')
        translation = translation.split('Translation:')[-1].strip() if 'Translation:' in translation else translation.strip()
        return translation
    
    def nllb(self):
        tokenizer = AutoTokenizer.from_pretrained(self.model_name, src_lang=self.sl)
        # model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name, device_map="auto", torch_dtype=torch.bfloat16)
        model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
        translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=self.sl, tgt_lang=self.tl)
        translated_text = translator(self.input_text, max_length=512)
        return translated_text[0]['translation_text']
    
    def wingpt(self):
        model = AutoModelForCausalLM.from_pretrained(
           self.model_name,
           torch_dtype="auto",
           device_map="auto"
        )
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        # input_json = '{"input_text": self.input_text}'
        messages = [
           {"role": "system", "content": f"Translate this to {self.tl} language"}, 
           {"role": "user", "content": self.input_text}
        ]
        
        text = tokenizer.apply_chat_template(
           messages,
           tokenize=False,
           add_generation_prompt=True
        )
        model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
        
        generated_ids = model.generate(
           **model_inputs,
           max_new_tokens=512,
           temperature=0.1
        )
        
        generated_ids = [
           output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
        ]
        print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))
        output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
        result = output.split('\n')[-1].strip() if '\n' in output else output.strip()
        return result
    
    def eurollm(self):
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        model = AutoModelForCausalLM.from_pretrained(self.model_name)  
        prompt = f"{self.sl}: {self.input_text} {self.tl}:"
        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(**inputs, max_new_tokens=512)
        output = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(output)
        # result = output.rsplit(f'{self.tl}:')[-1].strip() if f'{self.tl}:' in output else output.strip()
        result = output.rsplit(f'{self.tl}:')[-1].strip() if '\n' in output or f'{self.tl}:' in output else output.strip()
        return result
    
    def eurollm_instruct(self):
        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        model = AutoModelForCausalLM.from_pretrained(self.model_name)
        text = f'<|im_start|>system\n<|im_end|>\n<|im_start|>user\nTranslate the following {self.sl} source text to {self.tl}:\n{self.sl}: {self.input_text} \n{self.tl}: <|im_end|>\n<|im_start|>assistant\n'
        inputs = tokenizer(text, return_tensors="pt")
        outputs = model.generate(**inputs, max_new_tokens=512)
        output = tokenizer.decode(outputs[0], skip_special_tokens=True)
        if f'{self.tl}:' in output:
            output = output.rsplit(f'{self.tl}:')[-1].strip().replace('assistant\n', '').strip()
        return output

    def teuken(self):
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model = AutoModelForCausalLM.from_pretrained(
            self.model_name,
            trust_remote_code=True,
            torch_dtype=torch.bfloat16,
        )
        model = model.to(device).eval()
        tokenizer = AutoTokenizer.from_pretrained(
            self.model_name,
            use_fast=False,
            trust_remote_code=True,
        )
        translation_prompt = f"Translate the following text from {self.sl} into {self.tl}: {self.input_text}"
        messages = [{"role": "User", "content": translation_prompt}]
        prompt_ids = tokenizer.apply_chat_template(messages, chat_template="EN", tokenize=True, add_generation_prompt=False, return_tensors="pt")
        prediction = model.generate(
            prompt_ids.to(model.device),
            max_length=512,
            do_sample=True,
            top_k=50,
            top_p=0.95,
            temperature=0.7,
            num_return_sequences=1,
        )
        translation = tokenizer.decode(prediction[0].tolist())
        return translation

    def unbabel(self):
        pipe = pipeline("text-generation", model=self.model_name, torch_dtype=torch.bfloat16, device_map="auto")
        messages = [{"role": "user",
                     "content": f"Translate the following text from {self.sl} into {self.tl}.\n{self.sl}: {self.input_text}.\n{self.tl}:"}]
        prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
        tokenized_input = pipe.tokenizer(self.input_text, return_tensors="pt")
        num_input_tokens = len(tokenized_input["input_ids"][0])
        max_new_tokens = round(num_input_tokens + 0.5 * num_input_tokens)
        outputs = pipe(prompt, max_new_tokens=max_new_tokens, do_sample=False)
        translated_text = outputs[0]["generated_text"]
        print(f"Input chars: {len(self.input_text)}", f"Input tokens: {num_input_tokens}", f"max_new_tokens: {max_new_tokens}",
              "Chars to tokens ratio:", round(len(self.input_text) / num_input_tokens, 2), f"Raw translation: {translated_text}")
        markers = ["<end_of_turn>", "<|im_end|>", "<|im_start|>assistant"] # , "\n" 
        for marker in markers:
            if marker in translated_text:
                translated_text = translated_text.split(marker)[1].strip()
        translated_text = translated_text.replace('Answer:', '', 1).strip() if translated_text.startswith('Answer:') else translated_text
        translated_text = translated_text.split("Translated text:")[0].strip() if "Translated text:" in translated_text else translated_text
        split_translated_text = translated_text.split('\n', translated_text.count('\n'))
        translated_text = '\n'.join(split_translated_text[:self.input_text.count('\n')+1])
        return translated_text

    def bergamot(model_name: str = 'deen', sl: str = 'de', tl: str = 'en', input_text: str = 'Hallo, mein Freund'):
        try:
            import bergamot
            # input_text = [input_text] if isinstance(input_text, str) else input_text           
            config = bergamot.ServiceConfig(numWorkers=4)
            service = bergamot.Service(config)
            model = service.modelFromConfigPath(f"./{model_name}/bergamot.config.yml")
            options = bergamot.ResponseOptions(alignment=False, qualityScores=False, HTML=False)
            rawresponse = service.translate(model, bergamot.VectorString(input_text), options)
            translated_text: str = next(iter(rawresponse)).target.text
            message_text = f"Translated from {sl} to {tl} with Bergamot {model_name}."
        except Exception as error:
            response = error
        return translated_text, message_text

@spaces.GPU
def translate_text(input_text: str, s_language: str, t_language: str, model_name: str) -> tuple[str, str]:
    """
    Translates the input text from the source language to the target language  using a specified model.

    Parameters:
        input_text (str): The source text to be translated
        s_language (str): The source language of the input text
        t_language (str): The target language in which the input text is translated
        model_name (str): The selected translation model name

    Returns:
        tuple: 
            translated_text(str): The input text translated to the selected target language
            message_text(str):  A descriptive message summarizing the translation process. Example: "Translated from English to German with Helsinki-NLP."
    
    Example:
        >>> translate_text("Hello world", "English", "German", "Helsinki-NLP")
        ("Hallo Welt", "Translated from English to German with Helsinki-NLP.")
    """
    
    sl = all_langs[s_language][0]
    tl = all_langs[t_language][0]
    message_text = f'Translated from {s_language} to {t_language} with {model_name}'
    if sl == tl:
        translated_text = f'Source language {s_language} identical to target language {t_language}!'
        message_text = 'Please choose different target and source language!'
        return translated_text, message_text 
    translated_text = None
    try:    
        if "-mul" in model_name.lower() or "mul-" in model_name.lower() or "-roa" in model_name.lower():
            translated_text, message_text = Translators(model_name, sl, tl, input_text).HelsinkiNLP_mulroa()
        
        elif model_name == "Helsinki-NLP":
            translated_text, message_text = Translators(model_name, sl, tl, input_text).HelsinkiNLP()
        
        elif model_name == 'Argos':
            translated_text = Translators(model_name, sl, tl, input_text).argos()
    
        elif model_name == 'Google':
            translated_text = Translators(model_name, sl, tl, input_text).google()
    
        elif "m2m" in model_name.lower():
            translated_text = Translators(model_name, sl, tl, input_text).mtom()

        elif "lego" in model_name.lower():
            translated_text = Translators(model_name, sl, tl, input_text).LegoMT()
        
        elif model_name.startswith('t5'):
            translated_text = Translators(model_name, s_language, t_language, input_text).tfive()
            
        elif 'flan' in model_name.lower():
            translated_text = Translators(model_name, s_language, t_language, input_text).flan()

        elif 'madlad' in model_name.lower():
            translated_text = Translators(model_name, sl, tl, input_text).madlad()
            
        elif 'mt0' in model_name.lower():
            translated_text = Translators(model_name, s_language, t_language, input_text).bigscience()
    
        elif 'bloomz' in model_name.lower():
            translated_text = Translators(model_name, s_language, t_language, input_text).bloomz()
            
        elif 'nllb' in model_name.lower():
            nnlbsl, nnlbtl = languagecodes.nllb_language_codes[s_language], languagecodes.nllb_language_codes[t_language]
            translated_text = Translators(model_name, nnlbsl, nnlbtl, input_text).nllb()
        
        elif model_name == "facebook/mbart-large-50-many-to-many-mmt":
            translated_text = Translators(model_name, s_language, t_language, input_text).mbart_many_to_many()
    
        elif model_name == "facebook/mbart-large-50-one-to-many-mmt":
            translated_text = Translators(model_name, s_language, t_language, input_text).mbart_one_to_many()
    
        elif model_name == "facebook/mbart-large-50-many-to-one-mmt":
            translated_text = Translators(model_name, s_language, t_language, input_text).mbart_many_to_one()
        
        elif 'teuken' in model_name.lower():
            translated_text = Translators(model_name, s_language, t_language, input_text).teuken()
    
        elif model_name == "utter-project/EuroLLM-1.7B-Instruct":
            translated_text = Translators(model_name, s_language, t_language, input_text).eurollm_instruct()
        
        elif model_name == "utter-project/EuroLLM-1.7B":
            translated_text = Translators(model_name, s_language, t_language, input_text).eurollm()
                
        elif 'Unbabel' in model_name:   
            translated_text = Translators(model_name, s_language, t_language, input_text).unbabel()
            
        elif model_name == "HuggingFaceTB/SmolLM3-3B":
            translated_text = Translators(model_name, s_language, t_language, input_text).smollm()

        elif model_name == "winninghealth/WiNGPT-Babel-2":      
            translated_text = Translators(model_name, s_language, t_language, input_text).wingpt()

        elif model_name == "Bergamot":
            translated_text, message_text = Translators(model_name, s_language, t_language, input_text).bergamot()
            
    except Exception as error:
        translated_text = error
    finally:
        print(input_text, translated_text, message_text)
        return translated_text, message_text

# Function to swap dropdown values
def swap_languages(src_lang, tgt_lang):
    return tgt_lang, src_lang 

def get_info(model_name: str, sl: str = None, tl: str = None):
    helsinki = '### [Helsinki-NLP](https://huggingface.co/Helsinki-NLP "Helsinki-NLP")'
    if model_name == "Helsinki-NLP" and sl and tl:
        url = f'https://huggingface.co/{model_name}/opus-mt-{sl}-{tl}/raw/main/README.md'
        response = httpx.get(url).text
        if 'Repository not found' in response or 'Invalid username or password' in response:
            return helsinki
        return response
    elif model_name == "Argos":
        return httpx.get(f'https://huggingface.co/TiberiuCristianLeon/Argostranslate/raw/main/README.md').text
    elif model_name == "Google":
        return "Google Translate Online"
    else:
        return httpx.get(f'https://huggingface.co/{model_name}/raw/main/README.md').text

def create_interface():
    with gr.Blocks() as interface:
        gr.Markdown("### Machine Text Translation with Gradio API and MCP Server")
        input_text = gr.Textbox(label="Enter text to translate:", placeholder="Type your text here, maximum 512 tokens", autofocus=True, submit_btn='Translate', max_length=512)
        
        with gr.Row(variant="compact"):
            s_language = gr.Dropdown(choices=langs, value = DEFAULTS[0], label="Source language", interactive=True, scale=2)
            t_language = gr.Dropdown(choices=langs, value = DEFAULTS[1], label="Target language", interactive=True, scale=2)
            swap_btn = gr.Button("Swap Languages", size="md", scale=1)
            swap_btn.click(fn=swap_languages, inputs=[s_language, t_language], outputs=[s_language, t_language], api_name=False, show_api=False)
        # with gr.Row(equal_height=True):
            model_name = gr.Dropdown(choices=models, label=f"Select a model. Default is {DEFAULTS[2]}.", value=DEFAULTS[2], interactive=True, scale=2)
            # translate_btn = gr.Button(value="Translate", scale=1)

        translated_text = gr.Textbox(label="Translated text:", placeholder="Display field for translation", interactive=False, show_copy_button=True, lines=2)
        message_text = gr.Textbox(label="Messages:", placeholder="Display field for status and error messages", interactive=False,
                                  value=f'Default translation settings: from {s_language.value} to {t_language.value} with {model_name.value}.')
        allmodels = gr.HTML(label="Model links:", value=', '.join([f'<a href="https://huggingface.co/{model}">{model}</a>' for model in models]))
        model_info = gr.Markdown(label="Model info:", value=get_info(DEFAULTS[2], DEFAULTS[0], DEFAULTS[1]), show_copy_button=True)
        model_name.change(fn=get_info, inputs=[model_name, s_language, t_language], outputs=model_info, api_name=False, show_api=False)

        # translate_btn.click(
        #     fn=translate_text, 
        #     inputs=[input_text, s_language, t_language, model_name], 
        #     outputs=[translated_text, message_text]
        # )
        input_text.submit(
        fn=translate_text,
        inputs=[input_text, s_language, t_language, model_name],
        outputs=[translated_text, message_text]
        )

    return interface

interface = create_interface()
if __name__ == "__main__":
    interface.launch(mcp_server=True)
    # interface.queue().launch(server_name="0.0.0.0", show_error=True, server_port=7860, mcp_server=True)