| import streamlit as st | |
| from transformers import pipeline | |
| from PIL import Image | |
| model_path = "abhisheky127/FeedbackSummarizerEnterpret" | |
| summarizer = pipeline("summarization", model=model_path) | |
| def postprocess(prediction, input): | |
| if len(input.split(" "))<5: | |
| return("None") | |
| else: | |
| return(prediction.split('.')[0]+".") | |
| st.title("Feedback Summarizer: Enterpret") | |
| st.markdown( | |
| """ | |
| #### Summarize reviews/feedbacks with fine-tuned T5-small language Model | |
| > *powered by Hugging Face T5, Streamlit* | |
| """ | |
| ) | |
| st.image("Screenshot 2023-06-25 at 11.49.26 PM.png") | |
| st.markdown("----") | |
| product = st.text_input('Product', '') | |
| type = st.text_input('Record Type', '') | |
| text = st.text_area('Text to Summarize', '''''') | |
| if st.button('Summarize'): | |
| if len(product) and len(type) and len(text): | |
| with st.spinner( | |
| "Summarizing your feedback : `{}` ".format(text) | |
| ): | |
| text = "<product>{}</product><type>{}</type><text>{}</text>".format(product, type, text) | |
| res = summarizer(text)[0]["summary_text"] | |
| res = post_process(res, text) | |
| st.info(res, icon="🤖") | |
| else: | |
| st.write('I think something is missing, please check your inputs!') | |
| st.markdown("----") | |
| st.image("Screenshot 2022-11-20 at 5.40.54 AM.png") | |