app.py

# 1. The RoBERTa base model is used, fine-tuned using the SQuAD 2.0 dataset. 
# It’s been trained on question-answer pairs, including unanswerable questions, for the task of question and answering.

# 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)

# 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()

#---------------------------------------------------------------------------------
# 2. Same task, different model.

# from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
# import gradio as grad
# import ast

# 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()

#---------------------------------------------------------------------------------
# 3. Different task: language translation.

# from transformers import pipeline
# import gradio as grad

# First model translates English to German.
# mdl_name = "Helsinki-NLP/opus-mt-en-de"
# opus_translator = pipeline("translation", model=mdl_name)

# def translate(text):
#     response = opus_translator(text)
#     return response

# grad.Interface(translate, inputs=["text",], outputs="text").launch()

#----------------------------------------------------------------------------------
# 4. Language translation without pipeline API.
# Second model translates English to French.

# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# 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)

# 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)
#     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()

#-----------------------------------------------------------------------------------
# 5. Different task: abstractive summarization
# Abstractive summarization is more difficult than extractive summarization, 
# which pulls key sentences from a document and combines them to form a “summary.” 
# Because abstractive summarization involves paraphrasing words, it is also more time-consuming; 
# however, it has the potential to produce a more polished and coherent summary.

from transformers import PegasusForConditionalGeneration, PegasusTokenizer
import gradio as grad

mdl_name = "google/pegasus-xsum"
pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)

def summarize(text):
    tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt")
    txt_summary = mdl.generate(**tokens)
    response = pegasus_tkn.batch_decode(txt_summary, skip_special_tokens=True)
    return response

txt = grad.Textbox(lines=10, label="English", placeholder="English Text here")
out = grad.Textbox(lines=10, label="Summary")

grad.Interface(summarize, inputs=txt, outputs=out).launch()