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lodhrangpt
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363a70c
Create app.py
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app.py
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import gradio as gr
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
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# Load the translation model and tokenizer from Hugging Face
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# Here, we use the "Helsinki-NLP" translation models
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model_name = "Helsinki-NLP/opus-mt-en-fr" # Example model for English to French
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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translator = pipeline("translation", model=model, tokenizer=tokenizer)
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# Define the translation function
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def translate_text(text, target_language):
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model_name = f"Helsinki-NLP/opus-mt-en-{target_language}"
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translator = pipeline("translation", model=model_name)
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translation = translator(text)[0]['translation_text']
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return translation
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# Set up the Gradio interface
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languages = {
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"en": "English",
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"es": "Spanish",
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"fr": "French",
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"de": "German",
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"zh": "Chinese (Mandarin)",
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"ja": "Japanese",
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"ko": "Korean",
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"it": "Italian",
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"pt": "Portuguese",
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"ru": "Russian",
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"hi": "Hindi",
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"ar": "Arabic",
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"nl": "Dutch",
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"tr": "Turkish",
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"el": "Greek",
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"ur": "Urdu"
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}
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iface = gr.Interface(
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fn=translate_text,
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inputs=[
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gr.inputs.Textbox(label="Enter text to translate"),
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gr.inputs.Dropdown(list(languages.keys()), label="Target Language", type="value")
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],
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outputs="text",
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live=True,
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title="Text Translator",
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description="Translate text into multiple languages using Hugging Face models."
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)
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iface.launch()
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