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| import streamlit as st | |
| from transformers import pipeline | |
| st.title('Question Generator by Eddevs') | |
| left_column, right_column = st.columns(2) | |
| left_column.selectbox('Type', ['Question Generator', 'Paraphrasing']) | |
| right_column.selectbox('Question Generator', ['T5', 'GPT Neo-X']) | |
| input = st.text_area("Input Text") | |
| def summarize(text): | |
| # Refer to https://huggingface.co/docs/transformers/v4.18.0/en/main_classes/pipelines#transformers.SummarizationPipeline | |
| # for further information about configuration. | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| # Refer to https://huggingface.co/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig | |
| # for further configuration of of the | |
| output: list = summarizer( | |
| text, | |
| max_length=130, | |
| min_length=30, | |
| do_sample=False) | |
| return output[0]['summary_text'] | |
| if st.button('Generate'): | |
| st.write(input) | |
| st.write(summarize(input)) | |
| st.success("We have generated 105 Questions for you") | |
| st.snow() | |
| ##else: | |
| ##nothing here | |