Create requirements.txt

requirements.txt

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Function to load model and tokenizer
+@st.cache_resource
+def load_model_and_tokenizer(model_name):
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    return tokenizer, model
+
+# Function to perform translation
+def translate_text(text, tokenizer, model):
+    tokenized_text = tokenizer.prepare_seq2seq_batch([text], return_tensors="pt", padding=True)
+    translated_tokens = model.generate(**tokenized_text)
+    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return translated_text
+
+# Available language pairs (from Helsinki-NLP models)
+language_pairs = {
+    "English to French": "Helsinki-NLP/opus-mt-en-fr",
+    "French to English": "Helsinki-NLP/opus-mt-fr-en",
+    "English to German": "Helsinki-NLP/opus-mt-en-de",
+    "German to English": "Helsinki-NLP/opus-mt-de-en",
+    "English to Spanish": "Helsinki-NLP/opus-mt-en-es",
+    "Spanish to English": "Helsinki-NLP/opus-mt-es-en",
+    # Add more pairs as needed
+}
+
+# Streamlit App
+st.title("Language Translation App")
+st.write("Translate text between multiple languages using open-source models.")
+
+# User selects language pair
+language_pair = st.selectbox("Select Language Pair:", list(language_pairs.keys()))
+model_name = language_pairs[language_pair]
+
+# Load model and tokenizer
+with st.spinner("Loading translation model..."):
+    tokenizer, model = load_model_and_tokenizer(model_name)
+
+# Input text
+input_text = st.text_area("Enter text to translate:")
+
+if st.button("Translate"):
+    if input_text.strip():
+        with st.spinner("Translating..."):
+            translated_text = translate_text(input_text, tokenizer, model)
+        st.success("Translation complete!")
+        st.text_area("Translated Text:", translated_text, height=200)
+    else:
+        st.warning("Please enter text to translate.")