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Update app.py
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app.py
CHANGED
@@ -2,20 +2,16 @@ import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("mohamedtolba/franco-
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model = AutoModelForSeq2SeqLM.from_pretrained("mohamedtolba/franco-
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def translate_text(text):
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print(f"Input text: {text}")
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# Tokenize the input text
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inputs = tokenizer(text, return_tensors="pt", truncation=True)
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print(f"Tokenized inputs: {inputs}")
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# Generate the output text
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outputs = model.generate(**inputs)
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print(f"Generated outputs: {outputs}")
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# Decode the generated text
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translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(f"Translated text: {translated_text}")
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return translated_text
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# Create the Gradio interface
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@@ -24,8 +20,9 @@ iface = gr.Interface(
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inputs=gr.Textbox(lines=2, placeholder="Enter Franco-Arabic text here..."),
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outputs="text",
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title="Franco-Arabic to Arabic Translator",
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description="Translate Franco-Arabic text to Arabic using the mohamedtolba/franco-
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)
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if __name__ == "__main__":
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iface.launch()
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("mohamedtolba/franco-arabic")
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model = AutoModelForSeq2SeqLM.from_pretrained("mohamedtolba/franco-arabic")
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def translate_text(text):
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# Tokenize the input text
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inputs = tokenizer(text, return_tensors="pt", truncation=True)
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# Generate the output text
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outputs = model.generate(**inputs)
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# Decode the generated text
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translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return translated_text
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# Create the Gradio interface
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inputs=gr.Textbox(lines=2, placeholder="Enter Franco-Arabic text here..."),
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outputs="text",
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title="Franco-Arabic to Arabic Translator",
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description="Translate Franco-Arabic text to Arabic using the mohamedtolba/franco-arabic model."
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)
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if __name__ == "__main__":
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iface.launch()
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