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("","").replace("","") 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.")