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'''from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
import gradio as grad
import ast
#mdl_name = "deepset/roberta-base-squad2"
#my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
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 = "VietAI/envit5-translation"
opus_translator = pipeline("translation", model=mdl_name)
def translate(text):
response = opus_translator(text)
return response
grad.Interface(translate, inputs=["text",], outputs="text").launch()
'''
'''5.11
from transformers import GPT2LMHeadModel,GPT2Tokenizer
import gradio as grad
mdl = GPT2LMHeadModel.from_pretrained('gpt2')
gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
def generate(starting_text):
tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
gpt2_tensors = mdl.generate(tkn_ids)
response = gpt2_tensors
return response
txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
out=grad.Textbox(lines=1, label="Generated Tensors")
grad.Interface(generate, inputs=txt, outputs=out).launch()
'''
'''5.12
from transformers import GPT2LMHeadModel,GPT2Tokenizer
import gradio as grad
mdl = GPT2LMHeadModel.from_pretrained('gpt2')
gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
def generate(starting_text):
tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
gpt2_tensors = mdl.generate(tkn_ids)
response=""
#response = gpt2_tensors
for i, x in enumerate(gpt2_tensors):
response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
return response
txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
out=grad.Textbox(lines=1, label="Generated Tensors")
grad.Interface(generate, inputs=txt, outputs=out).launch()
'''
#5.20
from transformers import AutoModelWithLMHead, AutoTokenizer
import gradio as grad
text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
def text2text(context,answer):
input_text = "answer: %s context: %s </s>" % (answer, context)
features = text2text_tkn ([input_text], return_tensors='pt')
output = mdl.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'],
max_length=64)
response=text2text_tkn.decode(output[0])
return response
context=grad.Textbox(lines=10, label="English", placeholder="Context")
ans=grad.Textbox(lines=1, label="Answer")
out=grad.Textbox(lines=1, label="Genereated Question")
grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()
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