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import streamlit as st
from transformers import pipeline
from googletrans import Translator
# Function to handle translation using Hugging Face or Google Translate
def translate_text(text, target_language="es"):
# Hugging Face pipeline for translation (can be used for various language pairs)
try:
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
result = translator(text, max_length=400)
return result[0]['translation_text']
except Exception as e:
st.error("Error translating text using Hugging Face. Trying Google Translate instead.")
# Fallback to Google Translate if Hugging Face fails
translator = Translator()
translated_text = translator.translate(text, dest=target_language).text
return translated_text
# Streamlit UI setup
st.set_page_config(page_title="AI-Powered Language Learning Assistant", page_icon="🧠", layout="wide")
# Header and introduction
st.title("🧠 AI-Powered Language Learning Assistant")
st.markdown("""
Welcome to your AI-powered language assistant! Here you can:
- Translate words or sentences to different languages
- Learn and practice new vocabulary
- Get pronunciation tips and grammar feedback.
""")
# Input field for text
text_input = st.text_input("Enter the text you want to translate or practice", "")
# Select target language for translation
language = st.selectbox("Select the language to translate to", ["es", "fr", "de", "it", "pt", "ru"])
if text_input:
# Translate text
st.subheader(f"Original Text: {text_input}")
translated_text = translate_text(text_input, target_language=language)
# Display translation
st.markdown(f"### Translated Text to {language.upper()}:")
st.write(translated_text)
# Show pronunciation tip
st.subheader("Pronunciation Tip:")
st.write("Use an app like Google Translate or Forvo to practice pronunciation. If you want help with specific words, type them below.")
# Grammar Check (simple demo)
st.subheader("Grammar Feedback:")
st.write("For more advanced grammar feedback, please use language tools like Grammarly or LanguageTool.")
# Vocabulary practice section
st.markdown("---")
st.header("Vocabulary Practice")
word_input = st.text_input("Enter a word to get its definition and synonyms", "")
if word_input:
# Using Hugging Face API to fetch definitions and synonyms (this part can be expanded with a dedicated model)
try:
word_model = pipeline("fill-mask", model="bert-base-uncased") # Using BERT to predict related words
result = word_model(f"The synonym of {word_input} is [MASK].")
st.write(f"Synonyms or related words for **{word_input}**: {result}")
except Exception as e:
st.error("Error fetching vocabulary practice data.")
# Footer for engagement
st.markdown("""
---
**Need more practice?** Visit [Google Translate](https://translate.google.com) for real-time translations and pronunciation!
""")
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