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moThecarpenter80
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429ace2
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1adc6b4
Update app.py
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app.py
CHANGED
@@ -1,9 +1,12 @@
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import streamlit as st
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from transformers import MarianTokenizer, MarianMTModel , BertTokenizer, AutoModelForSeq2SeqLM, pipeline
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from
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import mishkal.tashkeel
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from arabert.preprocess import ArabertPreprocessor
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# Initialize Mishkal vocalizer
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vocalizer = mishkal.tashkeel.TashkeelClass()
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@@ -19,7 +22,7 @@ tokenizer_summarization = BertTokenizer.from_pretrained(model_name)
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model_summarization = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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pipeline_summarization = pipeline("text2text-generation", model=model_summarization, tokenizer=tokenizer_summarization)
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def main():
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st.title("U3reb Demo")
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@@ -42,7 +45,7 @@ def main():
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# Arabic Text Correction
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st.subheader("Arabic Text Correction (ar_correct)")
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if input_text:
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corrected_text =
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st.write("Corrected Text:", corrected_text)
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# Text Summarization
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import streamlit as st
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from transformers import MarianTokenizer, MarianMTModel , BertTokenizer, AutoModelForSeq2SeqLM, pipeline
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from ar_corrector.corrector import Corrector
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import mishkal.tashkeel
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from arabert.preprocess import ArabertPreprocessor
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# Initialize Mishkal vocalizer
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vocalizer = mishkal.tashkeel.TashkeelClass()
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model_summarization = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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pipeline_summarization = pipeline("text2text-generation", model=model_summarization, tokenizer=tokenizer_summarization)
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corr = Corrector()
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def main():
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st.title("U3reb Demo")
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# Arabic Text Correction
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st.subheader("Arabic Text Correction (ar_correct)")
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if input_text:
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corrected_text = corr.contextual_correct(input_text)
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st.write("Corrected Text:", corrected_text)
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# Text Summarization
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