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import streamlit as st |
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import joblib,os |
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import scipy |
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import spacy |
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import pandas as pd |
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nlp = spacy.load("en_core_web_sm") |
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import matplotlib.pyplot as plt |
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import matplotlib |
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matplotlib.use("Agg") |
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from wordcloud import WordCloud |
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complaints_vectorizer = open("models/tfidf_vect.joblib","rb") |
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complaints_cv = joblib.load(complaints_vectorizer) |
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def load_prediction_models(model_file): |
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loaded_model = joblib.load(open(os.path.join(model_file),"rb")) |
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return loaded_model |
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def get_key(val,my_dict): |
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for key,value in my_dict.items(): |
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if val == value: |
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return key |
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def main(): |
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"""Telecom Complaints Classifier""" |
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st.title("Comcast Telecom Complaints App") |
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html_temp = """ |
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<div style="background-color:#D5CC8F;padding:10px;border-radius:10px;margin:10px;"> |
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<h1 style="color:white;text-align:center;"> ML - Telecom Complaints Classifier </h1> |
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<img src="https://www.w3schools.com/howto/img_avatar.png" alt="Avatar" style="vertical-align: middle;width: 50px;height: 50px;border-radius: 50%;" > |
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<p style="text-align:justify">{}</p> |
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</div> |
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""" |
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title_temp =""" |
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<div style="background-color:#D5CC8F;padding:10px;border-radius:10px;margin:10px;"> |
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<h4 style="color:white;text-align:center;">{Debmalya Ray}</h1> |
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<img src="https://www.w3schools.com/howto/img_avatar.png" alt="Avatar" style="vertical-align: middle;float:left;width: 50px;height: 50px;border-radius: 50%;" > |
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<h6>Author:{Debmalya Ray}</h6> |
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<br/> |
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<br/> |
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<p style="text-align:justify">{}</p> |
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</div> |
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""" |
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article_temp =""" |
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<div style="background-color:#D5CC8F;padding:10px;border-radius:5px;margin:10px;"> |
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<h4 style="color:white;text-align:center;">{Debmalya Ray}</h1> |
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<h6>Author:{Debmalya Ray}</h6> |
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<h6>Post Date: {}</h6> |
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<img src="https://www.w3schools.com/howto/img_avatar.png" alt="Avatar" style="vertical-align: middle;width: 50px;height: 50px;border-radius: 50%;" > |
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<br/> |
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<br/> |
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<p style="text-align:justify">{}</p> |
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</div> |
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""" |
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st.markdown(html_temp,unsafe_allow_html=True) |
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activity = ['Prediction','NLP','About'] |
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choice = st.sidebar.selectbox("Select Activity",activity) |
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if choice == 'Prediction': |
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st.info("Prediction with ML") |
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complaints_text = st.text_area("Enter Complaints Here","Type Here") |
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all_ml_models = ["Decision Tree", "GradientBoost"] |
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model_choice = st.selectbox("Select Model",all_ml_models) |
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prediction_labels = {'Closed': 0, 'Open': 1, 'Pending': 2, 'Solved': 3} |
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if st.button("Classify"): |
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st.text("Original Text:\n{}".format(complaints_text)) |
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vect_text = complaints_cv.transform([complaints_text]).toarray() |
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if model_choice == 'Decision Tree': |
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predictor = load_prediction_models("models/dtcpred.joblib") |
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prediction = predictor.predict(vect_text) |
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st.write(prediction) |
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elif model_choice == 'GradientBoost': |
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predictor = load_prediction_models("models/gbcpred.joblib") |
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prediction = predictor.predict(vect_text) |
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st.write(prediction) |
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final_result = get_key(prediction,prediction_labels) |
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st.success("Complaints Categorized as: {}".format(final_result)) |
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elif choice == 'NLP': |
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st.info("Natural Language Processing of Text") |
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raw_text = st.text_area("Enter Customer Complaints Here","Type Here") |
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nlp_task = ["Tokenization","Lemmatization","Named Entity Recognition(NER)","Parts-of-Speech(POS) Tags"] |
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task_choice = st.selectbox("Choose NLP Task",nlp_task) |
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if st.button("Analyze"): |
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st.info("Original Text:\n{}".format(raw_text)) |
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docx = nlp(raw_text) |
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if task_choice == 'Tokenization': |
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result = [token.text for token in docx ] |
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elif task_choice == 'Lemmatization': |
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result = ["'Token':{},'Lemma':{}".format(token.text,token.lemma_) for token in docx] |
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elif task_choice == 'Named Entity Recognition(NER)': |
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result = [(entity.text,entity.label_)for entity in docx.ents] |
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elif task_choice == 'Parts-of-Speech(POS) Tags': |
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result = ["'Token':{},'POS':{},'Dependency':{}".format(word.text,word.tag_,word.dep_) for word in docx] |
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st.json(result) |
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if st.button("Tabulize"): |
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docx = nlp(raw_text) |
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c_tokens = [token.text for token in docx ] |
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c_lemma = [token.lemma_ for token in docx ] |
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c_pos = [token.pos_ for token in docx ] |
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new_df = pd.DataFrame(zip(c_tokens,c_lemma,c_pos),columns=['Tokens','Lemma','POS']) |
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st.dataframe(new_df) |
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if st.checkbox("WordCloud"): |
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c_text = raw_text |
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wordcloud = WordCloud().generate(c_text) |
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plt.imshow(wordcloud,interpolation='bilinear') |
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plt.axis("off") |
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st.set_option('deprecation.showPyplotGlobalUse', False) |
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st.pyplot() |
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else: |
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st.write("") |
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st.subheader("About") |
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st.write("""**************************************************************************""") |
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st.markdown(""" |
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### NLP Complaints Classifier With Different Models (With Streamlit) |
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###### Python Tools Used: spacy, pandas, matplotlib, wordcloud, Pillow(PIL), Joblib |
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""") |
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st.write("""**************************************************************************""") |
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st.write(""" |
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361148 || Throttling service and unreasonable data caps || 24-06-2015 || Acworth || Georgia || 30101 || Pending |
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""") |
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st.write(""" |
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359792 || Comcast refuses to help troubleshoot and correct my service. || 23-06-2015 || Adrian || Michigan || 49221 || Solved |
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""") |
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st.write(""" |
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371214 || Comcast Raising Prices and Not Being Available To Ask Why || 28-06-2015 || Alameda || California || 94501 || Open |
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""") |
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st.write(""" |
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242732 || Speed and Service || 18-04-2015 || Acworth || Georgia || 30101 || Closed |
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""") |
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st.write("""**************************************************************************""") |
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if __name__ == '__main__': |
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main() |
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