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import streamlit as st
from transformers import pipeline
from PIL import Image
model_path = "abhisheky127/FeedbackSummarizerEnterpret"
summarizer = pipeline("summarization", model=model_path)
st.title("Feedback Summarizer: Enterpret")
st.markdown(
"""
#### Summarize reviews/feedbacks with fine-tuned T5-small language Model
> *powered by Hugging Face T5, Streamlit*
----
"""
)
text = "<product>zoom</product><type>Appstore/Playstore</type><text>user: this is very successful meeting business</text>"
pred = summarizer(text)
st.write(pred)
# file_name = st.file_uploader("Upload a hot dog candidate image")
# if file_name is not None:
# col1, col2 = st.columns(2)
# image = Image.open(file_name)
# col1.image(image, use_column_width=True)
# predictions = pipeline(image)
# col2.header("Probabilities")
# for p in predictions:
# col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%") |