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import streamlit as st
import pandas as pd
import json
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer
import re
from load_model import translate_T5, translate_BERT_BARTPho, translate_LSTM, translate_BiLSTM, translate_GRU, translate_MarianMT
# from load_model import translate_GRU
import sentencepiece as spm
# python -m streamlit run ui.py
MAX_LENGTH = 64
st.title("Machine Translation")
st.markdown('<p style="font-size:24px; font-weight:bold;">English - Vietnamese</p>',
unsafe_allow_html=True)
if 'summarize_model' not in st.session_state:
summarize_model_dir = "./Summarization"
st.session_state.summarize_tokenizer = AutoTokenizer.from_pretrained(summarize_model_dir)
st.session_state.summarize_model = AutoModelForSeq2SeqLM.from_pretrained(summarize_model_dir)
print("Summarize model loaded")
model_name = st.selectbox("Select Model", ["BERT_BARTPho" ,"T5", "BiLSTM", "GRU", "LSTM", "MarianMT"], index=None, placeholder="Select a Model")
input_text = st.text_area(
"Input Text:",
placeholder="Enter your text here...",
height=150,
key="input_text",
help = f"If your input text is more than {MAX_LENGTH} words. It will be summarized and then translated",
value= "Today, I go to school"
)
def summarize(input_text):
if (len(input_text.split()) > MAX_LENGTH):
st.write("Your input paragraph is more than 64 words!")
summarize_tokenizer = st.session_state.summarize_tokenizer
summarize_model = st.session_state.summarize_model
inputs = summarize_tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
outputs = summarize_model.generate(**inputs, max_length=100, num_beams=5, length_penalty=2.0, early_stopping=True)
summerized_input_text = summarize_tokenizer.decode(outputs[0], skip_special_tokens=True)
return summerized_input_text
def cut_sentence(input_text):
sentences = re.split(r'(?<=[.!?]) +', input_text.strip())
return sentences
st.write(summarize(input_text))
st.markdown(
f"""
<style>
input[type=text] {{
width: 500%;
}}
</style>
""",
unsafe_allow_html=True
)
if st.button("Translate"):
if model_name == "BERT_BARTPho":
translated_text = translate_BERT_BARTPho(input_text)
st.write(f"Translation for {model_name}:")
st.write(translated_text)
if model_name == "T5":
translated_text = translate_T5(input_text)
st.write(f"Translation for {model_name}:")
st.write(translated_text)
if model_name == "BiLSTM":
translated_text = translate_BiLSTM(input_text)
st.write(f"Translation for {model_name}:")
st.write(translated_text)
elif model_name == "GRU":
translated_text = translate_GRU(input_text)
st.write(f"Translation for {model_name}:")
st.write(translated_text)
elif model_name == "LSTM":
translated_text = translate_LSTM(input_text)
st.write(f"Translation for {model_name}:")
st.write(translated_text)
elif model_name == "MarianMT":
translated_text = translate_MarianMT(input_text)
st.write(f"Translation for {model_name}:")
st.write(translated_text)
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