Create app.py

app.py

@@ -1,83 +1 @@
-import os
-import torch
-import gradio as 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-1.3B': 'facebook/nllb-200-distilled-1.3B'}
-
-    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-1.3B'
-
-    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 1.3B 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,
-                 examples=examples,
-                 examples_per_page=50,
-                 ).launch()
-
-
+Who is lebron