app.py

# import streamlit as st
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# import nltk
# import math
# import torch

# model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
# # model_name = "afnanmmir/t5-base-axriv-to-abstract-3"
# max_input_length = 1024
# max_output_length = 256

# st.header("Generate summaries")

# st_model_load = st.text('Loading summary generator model...')

# # @st.cache(allow_output_mutation=True)
# @st.cache_data
# def load_model():
#     print("Loading model...")
#     tokenizer = AutoTokenizer.from_pretrained(model_name)
#     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
#     nltk.download('punkt')
#     print("Model loaded!")
#     return tokenizer, model

# tokenizer, model = load_model()
# st.success('Model loaded!')
# st_model_load.text("")

# with st.sidebar:
#     # st.header("Model parameters")
#     # if 'num_titles' not in st.session_state:
#     #     st.session_state.num_titles = 5
#     # def on_change_num_titles():
#     #     st.session_state.num_titles = num_titles
#     # num_titles = st.slider("Number of titles to generate", min_value=1, max_value=10, value=1, step=1, on_change=on_change_num_titles)
#     # if 'temperature' not in st.session_state:
#     #     st.session_state.temperature = 0.7
#     # def on_change_temperatures():
#     #     st.session_state.temperature = temperature
#     # temperature = st.slider("Temperature", min_value=0.1, max_value=1.5, value=0.6, step=0.05, on_change=on_change_temperatures)
#     # st.markdown("_High temperature means that results are more random_")

# if 'text' not in st.session_state:
#     st.session_state.text = ""
# st_text_area = st.text_area('Text to generate the summary for', value=st.session_state.text, height=500)

# def generate_summary():
#     st.session_state.text = st_text_area

#     # tokenize text
#     inputs = ["summarize: " + st_text_area]
#     # print(inputs)
#     inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)
#     # print("Tokenized inputs: ")
#     # print(inputs)
    
#     outputs = model.generate(**inputs, do_sample=True, max_length=max_output_length, early_stopping=True, num_beams=8, length_penalty=2.0, no_repeat_ngram_size=2, min_length=64)
#     # print("outputs", outputs)
#     decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
#     # print("Decoded_outputs", decoded_outputs)
#     predicted_summaries = nltk.sent_tokenize(decoded_outputs.strip())
#     # print("Predicted summaries", predicted_summaries)

#     # decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
#     # predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]

#     st.session_state.summaries = predicted_summaries

# # generate title button
# st_generate_button = st.button('Generate summary', on_click=generate_summary)

# # title generation labels
# if 'summaries' not in st.session_state:
#     st.session_state.summaries = []

# if len(st.session_state.summaries) > 0:
#     # print("In summaries if")
#     with st.container():
#         st.subheader("Generated summaries")
#         for summary in st.session_state.summaries:
#             st.markdown("__" + summary + "__")


# -------------------------------

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk
import math
import torch

model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
max_input_length = 1024
max_output_length = 256
min_output_length = 64

st.header("Generate summaries for articles")

st_model_load = st.text('Loading summary generator model...')

@st.cache(allow_output_mutation=True)
def load_model():
    print("Loading model...")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    nltk.download('punkt')
    print("Model loaded!")
    return tokenizer, model

tokenizer, model = load_model()
st.success('Model loaded!')
st_model_load.text("")

if 'text' not in st.session_state:
    st.session_state.text = ""
st_text_area = st.text_area('Text to generate the summary for', value=st.session_state.text, height=500)

def generate_summary():
    st.session_state.text = st_text_area

    # tokenize text
    inputs = ["summarize: " + st_text_area]
    inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)

    # compute predictions
    outputs = model.generate(**inputs, do_sample=True, max_length=max_output_length, early_stopping=True, num_beams=8, length_penalty=2.0, no_repeat_ngram_size=2, min_length=min_output_length)
    decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]

    st.session_state.summaries = predicted_summaries

# generate summary button
st_generate_button = st.button('Generate summary', on_click=generate_summary)

# summary generation labels
if 'summaries' not in st.session_state:
    st.session_state.summaries = []

if len(st.session_state.summaries) > 0:
    with st.container():
        st.subheader("Generated summaries")
        for summary in st.session_state.summaries:
            st.markdown("__" + summary + "__")