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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk
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!")
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)
# inputs = tokenizer(inputs, return_tensors="pt")
# # compute span boundaries
# num_tokens = len(inputs["input_ids"][0])
# print(f"Input has {num_tokens} tokens")
# max_input_length = 500
# num_spans = math.ceil(num_tokens / max_input_length)
# print(f"Input has {num_spans} spans")
# overlap = math.ceil((num_spans * max_input_length - num_tokens) / max(num_spans - 1, 1))
# spans_boundaries = []
# start = 0
# for i in range(num_spans):
# spans_boundaries.append([start + max_input_length * i, start + max_input_length * (i + 1)])
# start -= overlap
# print(f"Span boundaries are {spans_boundaries}")
# spans_boundaries_selected = []
# j = 0
# for _ in range(num_titles):
# spans_boundaries_selected.append(spans_boundaries[j])
# j += 1
# if j == len(spans_boundaries):
# j = 0
# print(f"Selected span boundaries are {spans_boundaries_selected}")
# # transform input with spans
# tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
# tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected]
# inputs = {
# "input_ids": torch.stack(tensor_ids),
# "attention_mask": torch.stack(tensor_masks)
# }
# compute predictions
# outputs = model.generate(**inputs, do_sample=True, temperature=temperature, max_length=max_output_length)
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")
st.markdown(f"{' '.join(st.session_state.summaries)}")
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