import gradio as gr
from transformers import pipeline

qa_pipeline = pipeline(task="question-answering",model="Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa")

def greet(name):
    return "Hello " + name + "!!"

def predict(question="How many continents are there in the world?",context="There are seven continents in the world."):
    
    predictions = qa_pipeline(question=question,context=context)
    print(f'predictions={predictions}')
    return predictions

md = """
If you came looking for chatGPT, sorry to disappoint, but this is different. This prediction model is designed to answer a question about a text. It is designed to do reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, accomplishing accurate reading comprehension can be a very valuable task, especially if you are attempting to get quick answers from a large (and maybe boring!) document.

Training dataset: SQuADv1.1, based on the Rajpurkar et al. (2016) paper: [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://aclanthology.org/D16-1264/)

Based on the Zafrir et al. (2021) paper: [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) paper.

"""

predict()

# iface = gr.Interface(
#     fn=predict, 
#     inputs="Input your question.", 
#     outputs="text",
#     title = "Question & Answer with Sparse BERT using the SQuAD dataset",
#     description = md
#     )

# iface.launch()