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import gradio as gr
from transformers import pipeline
summarizer = pipeline('summarization')
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-base-squad2"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
examples = [
[ 'Question-Answer',
'',
'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.',
'Why is model conversion important?'
],
[ 'Question-Answer',
'',
"The Amazon rainforest is a moist broadleaf forest that covers most of the Amazon basin of South America",
"Which continent is the Amazon rainforest in?"
],
[ 'Question-Answer',
'',
'I am a Programmer.',
'Who am I?'
]
]
def summarize_text(text):
summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
summary = summary[0]['summary_text']
return summary
def question_answer(context, question):
QA_input = {
'context': context,
'question': question
}
res = nlp(QA_input)
return (res['answer'])
def home_func(model_choice, summ_text, qa_context, qa_question):
if model_choice=="Text Summarizer":
if summ_text == "":
return "Input correct text to be summarized"
return summarize_text(summ_text)
elif model_choice=="Question-Answer":
if qa_context == "" or qa_question == "":
return "Choose correct Context and associated questions"
return question_answer(qa_context, qa_question)
iface = gr.Interface(fn = home_func,
inputs = [gr.inputs.Dropdown(["Text Summarizer", "Question-Answer"], type="value"),
gr.inputs.Textbox(lines=5, placeholder="Enter your text here...", label="Text to be summarized"),
gr.inputs.Textbox(lines=5, placeholder="Choose from examples", label="Context"),
gr.inputs.Textbox(lines=5, placeholder="Choose from examples", label="Question")],
outputs="text",
examples=examples)
iface.launch() |