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import streamlit as st
from transformers import pipeline
import string
st.set_page_config(page_title="RLM-Translator", page_icon="🍕", layout="wide", initial_sidebar_state="collapsed")
@st.cache_resource
def load_model():
return pipeline(model="rudrashah/RLM-hinglish-translator")
pipe = load_model()
def check_sentence_end(sentence):
last_char = sentence[-1]
if last_char in string.punctuation:
return sentence
else:
return sentence + "."
def model_rlm(sentence):
text = "mere paas 100 rupaye hain"
text = check_sentence_end(sentence)
template = "Hinglish:\n{hi_en}\n\nEnglish:\n{en}"
english = pipe(template.format(hi_en=text,en=""), max_length=250)
english = english[0]['translation_text']
english = english.replace("<bos>","").replace("<eos>","")
english = english[len(template.format(hi_en=text,en="")):]
return english.strip()
def translate_hinglish_to_english(text):
translated_text = model_rlm(text)
st.session_state['translated_text'] = translated_text
return translated_text
st.title("RLM-Translator")
st.write("A simple Hinglish to English translator from Hugging Face by [Rudra Shah](https://huggingface.co/rudrashah/RLM-hinglish-translator).")
col1, col2 = st.columns(2)
with col1:
hinglish_text = st.text_area("Hinglish", height=300)
with col2:
if "translated_text" in st.session_state:
st.text_area("English", value=st.session_state["translated_text"], height=300, key="")
else:
st.text_area("English", height=300, key="")
if st.button("Translate", use_container_width=True, type="primary"):
if hinglish_text != "":
translated_text = translate_hinglish_to_english(hinglish_text)
st.session_state["translated_text"] = translated_text
st.rerun()
else:
st.error("Please enter some hinglish text to translate.")
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