app.py

# import os
# import torch
# import gradio s gr
# import time
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# from flores200_codes import flores_codes


# def load_models():
#     # build model and tokenizer
#     model_name_dict = {'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
#                   #'nllb-1.3B': 'facebook/nllb-200-1.3B',
#                   #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
#                   #'nllb-3.3B': 'facebook/nllb-200-3.3B',
#                   }

#     model_dict = {}

#     for call_name, real_name in model_name_dict.items():
#         print('\tLoading model: %s' % call_name)
#         model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
#         tokenizer = AutoTokenizer.from_pretrained(real_name)
#         model_dict[call_name+'_model'] = model
#         model_dict[call_name+'_tokenizer'] = tokenizer

#     return model_dict


# def translation(source, target, text):
#     if len(model_dict) == 2:
#         model_name = 'nllb-distilled-600M'

#     start_time = time.time()
#     source = flores_codes[source]
#     target = flores_codes[target]

#     model = model_dict[model_name + '_model']
#     tokenizer = model_dict[model_name + '_tokenizer']

#     translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
#     output = translator(text, max_length=400)

#     end_time = time.time()

#     output = output[0]['translation_text']
#     result = {'inference_time': end_time - start_time,
#               'source': source,
#               'target': target,
#               'result': output}
#     return result


# if __name__ == '__main__':
#     print('\tinit models')

#     global model_dict

#     model_dict = load_models()
    
#     # define gradio demo
#     lang_codes = list(flores_codes.keys())
#     #inputs = [gr.inputs.Radio(['nllb-distilled-600M', 'nllb-1.3B', 'nllb-distilled-1.3B'], label='NLLB Model'),
#     inputs = [gr.inputs.Dropdown(lang_codes, default='English', label='Source'),
#               gr.inputs.Dropdown(lang_codes, default='Korean', label='Target'),
#               gr.inputs.Textbox(lines=5, label="Input text"),
#               ]

#     outputs = gr.outputs.JSON()

#     title = "NLLB distilled 600M demo"

#     demo_status = "Demo is running on CPU"
#     description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}"
#     examples = [
#     ['English', 'Korean', 'Hi. nice to meet you']
#     ]

#     gr.Interface(translation,
#                  inputs,
#                  outputs,
#                  title=title,
#                  description=description,
#                  ).launch() 


import os
import time
from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from flores200_codes import flores_codes

app = Flask(__name__)

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(f'\tLoading model: {call_name}')
        model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
        tokenizer = AutoTokenizer.from_pretrained(real_name)
        model_dict[call_name + '_model'] = model
        model_dict[call_name + '_tokenizer'] = tokenizer

    return model_dict

global model_dict
model_dict = load_models()

@app.route('/api/translate', methods=['POST'])
def translate_text():
    data = request.json
    source_lang = data.get('source')
    target_lang = data.get('target')
    input_text = data.get('text')
    
    if not source_lang or not target_lang or not input_text:
        return jsonify({"error": "source, target, and text fields are required"}), 400
    
    model_name = 'nllb-distilled-600M'
    start_time = time.time()
    source = flores_codes.get(source_lang)
    target = flores_codes.get(target_lang)
    
    if not source or not target:
        return jsonify({"error": "Invalid source or target language code"}), 400

    model = model_dict[model_name + '_model']
    tokenizer = model_dict[model_name + '_tokenizer']
    
    translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target)
    output = translator(input_text, max_length=400)
    
    end_time = time.time()
    output_text = output[0]['translation_text']
    
    result = {
        'inference_time': end_time - start_time,
        'source': source_lang,
        'target': target_lang,
        'result': output_text
    }
    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True)