Create app.py

app.py

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# App Title and Description
+st.title("🌐 Universal Language Translator App")
+st.write("""
+Translate text from any language to any other language using an open-source multilingual model.  
+This app supports many languages and ensures a user-friendly experience.
+""")
+
+# Instructions
+st.sidebar.header("📝 Instructions")
+st.sidebar.write("""
+1. Enter the text you want to translate in the input box.
+2. Select the source language of the input text.
+3. Choose the target language for translation.
+4. Click the "Translate" button to see the translated text.
+""")
+
+# Supported Languages
+languages = {
+    "English": "en",
+    "Spanish": "es",
+    "French": "fr",
+    "German": "de",
+    "Chinese": "zh",
+    "Hindi": "hi",
+    "Arabic": "ar",
+    "Russian": "ru",
+    "Italian": "it",
+    "Portuguese": "pt",
+    "Japanese": "ja",
+    "Korean": "ko",
+    "Dutch": "nl",
+    "Bengali": "bn",
+    "Turkish": "tr",
+    "Urdu": "ur",
+    "Greek": "el",
+    "Polish": "pl",
+    "Thai": "th",
+    "Vietnamese": "vi",
+    "Hebrew": "he",
+    "Swahili": "sw",
+    "Amharic": "am",
+    "Tamil": "ta",
+    "Telugu": "te",
+    "Punjabi": "pa",
+    "Malayalam": "ml",
+}
+
+# UI for Input and Language Selection
+source_text = st.text_area("Enter text to translate:", height=150, placeholder="Type here...")
+source_language = st.selectbox("Source Language:", options=list(languages.keys()), index=0)
+target_language = st.selectbox("Target Language:", options=list(languages.keys()), index=1)
+
+if st.button("Translate"):
+    if source_text.strip() == "":
+        st.error("Please enter some text to translate.")
+    else:
+        try:
+            # Define model name dynamically
+            model_name = f"Helsinki-NLP/opus-mt-{languages[source_language]}-{languages[target_language]}"
+            
+            # Load tokenizer and model
+            tokenizer = MarianTokenizer.from_pretrained(model_name)
+            model = MarianMTModel.from_pretrained(model_name)
+            
+            # Translate
+            inputs = tokenizer(source_text, return_tensors="pt", padding=True, truncation=True)
+            translated_tokens = model.generate(**inputs)
+            translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+            
+            # Display Translated Text
+            st.subheader("🔄 Translated Text:")
+            st.text_area("Translation Output:", value=translated_text, height=150)
+        except Exception as e:
+            st.error("Translation failed. Please ensure the selected languages are supported.")
+            st.error(str(e))
+
+# Footer
+st.write("---")
+st.markdown("💡 **Developed by Abdullah**")