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)}%")