Update app.py

app.py

@@ -1,76 +1,58 @@
 import streamlit as st
-from googletrans import Translator  # Free Google Translate API
-from transformers import pipeline
 import requests
+from transformers import pipeline
 
-# Initialize the Google Translator
-translator = Translator()
-
-# Streamlit UI setup
+# Set up the page
 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
+- Translate words or sentences to different languages (using LibreTranslate API)
 - Learn and practice new vocabulary
 - Get 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 using googletrans
-    st.subheader(f"Original Text: {text_input}")
-    translated_text = translator.translate(text_input, dest=language).text
-
-    # Display translation
-    st.markdown(f"### Translated Text to {language.upper()}:")
-    st.write(translated_text)
-
-    # Show pronunciation tip
-    st.subheader("Pronunciation Tip:")
-    st.write("Use Google Translate or Forvo to practice pronunciation.")
-
-    # Grammar Check (using LanguageTool)
-    st.subheader("Grammar Feedback:")
-    grammar_check_url = "https://api.languagetool.org/v2/check"
-    params = {
-        "text": text_input,
-        "language": "en-US"
+# Translation Function (Using LibreTranslate API)
+def translate_text(text, target_language):
+    url = "https://libretranslate.de/translate"  # Free LibreTranslate API
+    payload = {
+        'q': text,
+        'source': 'en',
+        'target': target_language
     }
-    response = requests.post(grammar_check_url, data=params)
+    response = requests.post(url, data=payload)
     if response.status_code == 200:
-        result = response.json()
-        if result['matches']:
-            st.write("### Grammar Issues Found:")
-            for match in result['matches']:
-                st.write(f"- **{match['message']}** at position {match['offset']}-{match['offset']+match['length']}")
-        else:
-            st.write("No grammar issues found!")
+        return response.json()['translatedText']
     else:
-        st.write("Grammar check failed. Try again later.")
+        return "Translation failed."
 
-# Vocabulary practice section using Hugging Face's BERT
+# Vocabulary Practice Section using Hugging Face's BERT Model
 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's BERT model for related words (synonyms)
     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.")
+        st.error(f"Error fetching vocabulary practice data: {e}")
+
+# Translation Section
+st.markdown("---")
+st.header("Translation")
+text_input = st.text_input("Enter the text you want to translate", "")
+language = st.selectbox("Select the language to translate to", ["es", "fr", "de", "it", "pt", "ru"])
+
+if text_input:
+    translated_text = translate_text(text_input, language)
+    st.subheader(f"Translated Text to {language.upper()}:")
+    st.write(translated_text)
 
 # Footer for engagement
 st.markdown("""
 ---
-**Need more practice?** Visit [Google Translate](https://translate.google.com) for real-time translations and pronunciation!
+**Need more practice?** Visit [LibreTranslate API](https://libretranslate.de/) for real-time translations and Hugging Face for more language models!
 """)