Create app.py

app.py

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Function to load the translation model and tokenizer
+def load_model(model_name):
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# Streamlit App
+st.title("Language Translation App")
+st.subheader("Translate multiple languages instantly!")
+
+# Available languages (you can expand this list)
+languages = {
+    'Azerbaijani': 'az',
+    'English': 'en',
+    'French': 'fr',
+    'German': 'de',
+    'Spanish': 'es',
+    'Italian': 'it',
+    'Portuguese': 'pt',
+}
+
+# User input for language selection
+input_lang = st.selectbox("Select Input Language:", list(languages.keys()))
+output_lang = st.selectbox("Select Output Language:", list(languages.keys()))
+
+# Prepare model names
+input_model_code = f"Helsinki-NLP/opus-mt-{languages[input_lang]}-{languages[output_lang]}"
+
+# Load model and tokenizer
+model, tokenizer = load_model(input_model_code)
+
+# User input for text to translate
+text_to_translate = st.text_area("Enter text to translate:")
+
+# Translation functionality
+if st.button("Translate"):
+    if text_to_translate:
+        # Tokenize and translate the input text
+        inputs = tokenizer(text_to_translate, return_tensors="pt", padding=True)
+        translated = model.generate(**inputs)
+        translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+        
+        # Show the result
+        st.subheader("Translated Text:")
+        st.write(translated_text)
+    else:
+        st.error("Please enter some text to translate.")