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ibn-sidah-team
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Upload app.py
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app.py
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python -m venv .env
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# Activate the virtual environment
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source .env/bin/activate
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# Deactivate the virtual environment
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source .env/bin/deactivate
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pip install -r requirements.txt
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pip install faiss-cpu
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# In the requirements.txt but not installed correctly so we have to use pip command
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#!pip install gensim==3.8.1
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python -m spacy download en_core_web_sm
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# Install gradio for user interface
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pip install gradio
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import sensegram
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from wsd import WSD
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from gensim.models import KeyedVectors
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# Model files
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sense_vectors_fpath = "./best_sense_gram_model/best_model.sense_vectors"
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word_vectors_fpath = "./best_sense_gram_model/best_model.word_vectors"
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# Model parameters
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max_context_words = 3
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context_window_size = 5
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ignore_case = True
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lang = "ar" # to filter out stopwords
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# Model loading ... takes some time
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sv = sensegram.SenseGram.load_word2vec_format(sense_vectors_fpath, binary=False)
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wv = KeyedVectors.load_word2vec_format(word_vectors_fpath, binary=False, unicode_errors="ignore")
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# Method takes word and context and retirn the results of the model.
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def wsd_method(word, context):
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output = ""
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output += "Probabilities of the senses:\n{}\n\n".format(sv.get_senses(word, ignore_case=ignore_case))
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for sense_id, prob in sv.get_senses(word, ignore_case=ignore_case):
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output += sense_id
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output += ("\n"+"="*20+"\n")
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for rsense_id, sim in sv.wv.most_similar(sense_id):
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output += "{} {:f}\n".format(rsense_id, sim)
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output +="\n"
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# Disambiguate a word in a context
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wsd_model = WSD(sv, wv, window=context_window_size, lang=lang,
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max_context_words=max_context_words, ignore_case=ignore_case)
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output += str(wsd_model.disambiguate(context, word))
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return output
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import gradio as gr
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# Lanuching live demo
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demo = gr.Interface(
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fn=wsd_method,
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inputs=[gr.Textbox(lines=1, placeholder="الكلمة"),gr.Textbox(lines=2, placeholder="السياق")],
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outputs="text",
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title="فـك الالتباس الدلالي",
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description="فضلًا أدخل الكلمة ثم السياق ثم اضغط على زر إرسال، ولاستعراض المخرجات كاملة يرجى استخدام زر التمرير لأسفل.",
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)
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demo.launch()
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