app.py

# from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
# import gradio as grad
# import ast
# # mdl_name = "deepset/roberta-base-squad2"
# mdl_name = "distilbert-base-cased-distilled-squad"
# my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)

# def answer_question(question,context):
#     text= "{"+"'question': '"+question+"','context': '"+context+"'}"
    
#     di=ast.literal_eval(text)
#     response = my_pipeline(di)
#     return response
# grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()

# from transformers import pipeline
# import gradio as grad
# mdl_name = "Helsinki-NLP/opus-mt-en-zh"
# opus_translator = pipeline("translation", model=mdl_name)

# def translate(text):
    
#     response = opus_translator(text)
#     return response
# grad.Interface(translate, inputs=["text",], outputs="text").launch()

# from transformers import pipeline
# import gradio as grad
# mdl_name = "Helsinki-NLP/opus-mt-en-zh"

# opus_translator = pipeline("translation", model=mdl_name)


# def translate(text):
#     response = opus_translator(text)
#     return response
# txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
# out=grad.Textbox(lines=1, label="Chinese")
# grad.Interface(translate, inputs=txt, outputs=out).launch()

################################5-6
from transformers import AutoModel,AutoTokenizer,AutoModelForSeq2SeqLM
import gradio as grad
mdl_name = "Helsinki-NLP/opus-mt-en-fr"
mdl = AutoModelForSeq2SeqLM.from_pretrained(mdl_name)
my_tkn = AutoTokenizer.from_pretrained(mdl_name)

#opus_translator = pipeline("translation", model=mdl_name)

def translate(text):
    inputs = my_tkn(text, return_tensors="pt")
    trans_output = mdl.generate(**inputs)
    response = my_tkn.decode(trans_output[0], skip_special_tokens=True)

    #response = opus_translator(text)
    return response
txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
out=grad.Textbox(lines=1, label="French")
grad.Interface(translate, inputs=txt, outputs=out).launch()