| 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) |
| ): |
| text1 = "<product>{}</product><type>{}</type><text>{}</text>".format(product, type, text) |
| |
| res = summarizer(text1)[0]["summary_text"] |
| res = postprocess(res, text) |
| st.info(res, icon="🤖") |
| else: |
| st.write('I think something is missing, please check your inputs!') |
|
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|
| st.markdown("----") |
| st.image("Screenshot 2022-11-20 at 5.40.54 AM.png") |
|
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|
