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shylusakthi
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a3be9d0
Create app.py
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app.py
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import streamlit as st
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from transformers import MarianMTModel, MarianTokenizer
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# Initialize the MarianMT model and tokenizer
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@st.cache_resource
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def load_model_and_tokenizer(model_name):
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model = MarianMTModel.from_pretrained(model_name)
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tokenizer = MarianTokenizer.from_pretrained(model_name)
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return model, tokenizer
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# Language selection options (you can expand this list as needed)
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language_options = {
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"English to French": ("en", "fr"),
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"French to English": ("fr", "en"),
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"English to Spanish": ("en", "es"),
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"Spanish to English": ("es", "en"),
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"English to German": ("en", "de"),
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"German to English": ("de", "en"),
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"English to Italian": ("en", "it"),
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"Italian to English": ("it", "en"),
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# Add more language pairs as needed
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}
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# Streamlit UI setup
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st.title("Hugging Face Translation App")
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st.write("This app allows you to translate text between different languages using Hugging Face's translation models.")
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# Select the language pair
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language_pair = st.selectbox("Select language pair", list(language_options.keys()))
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src_lang, tgt_lang = language_options[language_pair]
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# Load the corresponding model and tokenizer
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model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
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try:
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model, tokenizer = load_model_and_tokenizer(model_name)
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except Exception as e:
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st.error(f"Error loading model {model_name}: {e}")
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st.stop() # Stop execution if the model loading fails
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# Input text to translate
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text = st.text_area("Enter text to translate")
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# Translate button
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if st.button("Translate"):
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if text.strip():
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# Prepare the input for the model
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
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# Generate translation
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translation = model.generate(**inputs)
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# Decode the output
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translated_text = tokenizer.decode(translation[0], skip_special_tokens=True)
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# Display translation
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st.write(f"**Original Text**: {text}")
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st.write(f"**Translated Text**: {translated_text}")
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else:
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st.warning("Please enter some text to translate.")
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