Update app.py
Browse files
app.py
CHANGED
@@ -1,105 +1,67 @@
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import streamlit as st
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import joblib
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from transformers import pipeline
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#import string, re
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def features(sentence, index):
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return {
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'word': sentence[index],
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'is_first': index == 0,
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'is_last': index == len(sentence) - 1,
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'lword': len(sentence[index]),
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'prefix-1': sentence[index][:1],
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'prefix-2': sentence[index][:2],
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'prefix-3': sentence[index][:3],
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'prefix-4': sentence[index][:4],
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'prefix-5': sentence[index][:5],
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'suffix-1': sentence[index][-1],
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'suffix-2': sentence[index][-2:],
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'suffix-3': sentence[index][-3:],
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'suffix-4': sentence[index][-4:],
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'suffix-5': sentence[index][-5:],
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'prev_word_4': prvwords_4(sentence, index),
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'prev_word_3': prvwords_3(sentence, index),
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'prev_word_2': prvwords_2(sentence, index),
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'prev_word_1': prvwords_1(sentence, index),
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'next_word_1': nextwords_1(sentence, index),
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'next_word_2': nextwords_2(sentence, index),
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'next_word_3': nextwords_3(sentence, index),
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'next_word_4': nextwords_4(sentence, index),
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'is_numeric': sentence[index].isdigit(),
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def rebuildxx(ww, xres):
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numprfx = xres.count('p')
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numsufx = xres.count('f')
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resfinal = ''
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if numprfx != 0 and numsufx != 0 :
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resfinal = "{}+{}+{}".format(ww[:numprfx] , ww[numprfx:-numsufx] , ww[-numsufx:] )
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if numprfx == 0 and numsufx == 0 :
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#resfinal = "{}+{}+{}".format("", ww , "" )
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resfinal = "{}".format(ww )
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if numprfx == 0 and numsufx != 0 :
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#resfinal = "{}+{}+{}".format("" , ww[:-numsufx], ww[-numsufx:] )
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resfinal = "{}+{}".format(ww[:-numsufx], ww[-numsufx:] )
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if numprfx != 0 and numsufx == 0 :
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#resfinal = "{}+{}+{}".format(ww[:numprfx] , ww[numprfx:], "")
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resfinal = "{}+{}".format(ww[:numprfx] , ww[numprfx:])
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return resfinal
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import re
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def prepare_text(text):
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symbol_pattern = r'([^\w\s\d])' # Capture non-word, non-space, non-digit characters
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prepared_text = re.sub(symbol_pattern, r' \1 ', text)
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prepared_text = re.sub(r'\s+', ' ', prepared_text)
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import streamlit as st
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from transformers import pipeline
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# Load the model using the Hugging Face model hub
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model = pipeline("text-classification", model="Alshargi/arabic-msa-dialects-segmentation")
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# Slider to select a value
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x = st.text_input('Enter a text')
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# Check if text input is not empty
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if x:
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# Make prediction using the loaded model
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result = model(x)
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# Display the prediction
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st.write("Prediction:",
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else:
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st.write("Please enter some text.")
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import streamlit as st
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import joblib
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import re
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from transformers import pipeline
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# Load the scikit-learn model
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sklearn_model = joblib.load("sklearn_model.pkl")
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# Wrap the scikit-learn model inside a Hugging Face pipeline
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pipeline_model = pipeline(task="feature-extraction", model=sklearn_model)
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# Define feature functions
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def features(sentence, index):
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return {
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'word': sentence[index],
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'is_first': index == 0,
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'is_last': index == len(sentence) - 1,
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'lword': len(sentence[index]),
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'prefix-1': sentence[index][:1],
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'prefix-2': sentence[index][:2],
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'prefix-3': sentence[index][:3],
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'prefix-4': sentence[index][:4],
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'prefix-5': sentence[index][:5],
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'suffix-1': sentence[index][-1],
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'suffix-2': sentence[index][-2:],
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'suffix-3': sentence[index][-3:],
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'suffix-4': sentence[index][-4:],
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'suffix-5': sentence[index][-5:],
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'prev_word_4': prvwords_4(sentence, index),
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'prev_word_3': prvwords_3(sentence, index),
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'prev_word_2': prvwords_2(sentence, index),
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'prev_word_1': prvwords_1(sentence, index),
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'next_word_1': nextwords_1(sentence, index),
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'next_word_2': nextwords_2(sentence, index),
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'next_word_3': nextwords_3(sentence, index),
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'next_word_4': nextwords_4(sentence, index),
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'is_numeric': sentence[index].isdigit(),
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}
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# Function to prepare text
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def prepare_text(text):
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symbol_pattern = r'([^\w\s\d])'
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prepared_text = re.sub(symbol_pattern, r' \1 ', text)
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prepared_text = re.sub(r'\s+', ' ', prepared_text)
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return prepared_text.strip()
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# Text input field for user input
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text_input = st.text_input("Enter some text:")
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# Check if the user input is not empty
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if text_input:
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# Prepare text
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prepared_text = prepare_text(text_input)
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# Tokenize text
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tokenized_text = word_tokenize(prepared_text)
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# Extract features
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features_list = [features(tokenized_text, i) for i in range(len(tokenized_text))]
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# Use the Hugging Face pipeline to make predictions
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prediction = pipeline_model(features_list)
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# Display the prediction
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st.write("Prediction:", prediction)
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else:
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st.write("Please enter some text.")
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