|
|
| import streamlit as st |
| import joblib,os |
| import spacy |
| import pandas as pd |
| nlp = spacy.load("en_core_web_sm") |
| import matplotlib.pyplot as plt |
| import matplotlib |
| matplotlib.use("Agg") |
| from wordcloud import WordCloud |
|
|
|
|
| |
| complaints_vectorizer = open("models/tfidf_vect.pickle","rb") |
| complaints_cv = joblib.load(complaints_vectorizer) |
|
|
| def load_prediction_models(model_file): |
|
|
| loaded_model = joblib.load(open(os.path.join(model_file),"rb")) |
| return loaded_model |
|
|
| |
| def get_key(val,my_dict): |
| for key,value in my_dict.items(): |
| if val == value: |
| return key |
|
|
|
|
| def main(): |
|
|
| """Telecom Complaints Classifier""" |
| st.title("Telecom Complaints - Classification App") |
| |
| |
| html_temp = """ |
| <div style="background-color:#464e5f;padding:10px;border-radius:10px;margin:10px;"> |
| <h1 style="color:white;text-align:center;"> ML - Telecom Complaints Classifier </h1> |
| <img src="https://www.w3schools.com/howto/img_avatar.png" alt="Avatar" style="vertical-align: middle;width: 50px;height: 50px;border-radius: 50%;" > |
| <p style="text-align:justify">{}</p> |
| </div> |
| """ |
| title_temp =""" |
| <div style="background-color:#464e5f;padding:10px;border-radius:10px;margin:10px;"> |
| <h4 style="color:white;text-align:center;">{}</h1> |
| <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%;" > |
| <h6>Author:{}</h6> |
| <br/> |
| <br/> |
| <p style="text-align:justify">{}</p> |
| </div> |
| """ |
| article_temp =""" |
| <div style="background-color:#464e5f;padding:10px;border-radius:5px;margin:10px;"> |
| <h4 style="color:white;text-align:center;">{}</h1> |
| <h6>Author:{}</h6> |
| <h6>Post Date: {}</h6> |
| <img src="https://www.w3schools.com/howto/img_avatar.png" alt="Avatar" style="vertical-align: middle;width: 50px;height: 50px;border-radius: 50%;" > |
| <br/> |
| <br/> |
| <p style="text-align:justify">{}</p> |
| </div> |
| """ |
|
|
| |
| st.markdown(html_temp,unsafe_allow_html=True) |
|
|
| activity = ['Prediction','NLP','About'] |
| choice = st.sidebar.selectbox("Select Activity",activity) |
|
|
|
|
| if choice == 'Prediction': |
| st.info("Prediction with ML") |
| complaints_text = st.text_area("Enter Complaints Here","Type Here") |
| all_ml_models = ["Decision Tree", "GradientBoost"] |
| model_choice = st.selectbox("Select Model",all_ml_models) |
|
|
| prediction_labels = {'Closed': 0, 'Open': 1, 'Pending': 2, 'Solved': 3} |
| if st.button("Classify"): |
| st.text("Original Text:\n{}".format(complaints_text)) |
| vect_text = complaints_cv.transform([complaints_text]).toarray() |
| if model_choice == 'Decision Tree': |
| predictor = load_prediction_models("models/dtcpred.pickle") |
| prediction = predictor.predict(vect_text) |
| |
| elif model_choice == 'GradientBoost': |
| predictor = load_prediction_models("models/gbcpred.pickle") |
| prediction = predictor.predict(vect_text) |
| |
| |
| |
| final_result = get_key(prediction,prediction_labels) |
| st.success("Complaints Categorized as: {}".format(final_result)) |
|
|
| elif choice == 'NLP': |
| st.info("Natural Language Processing of Text") |
| raw_text = st.text_area("Enter Customer Complaints Here","Type Here") |
| nlp_task = ["Tokenization","Lemmatization","Named Entity Recognition(NER)","Parts-of-Speech(POS) Tags"] |
| task_choice = st.selectbox("Choose NLP Task",nlp_task) |
| if st.button("Analyze"): |
| st.info("Original Text:\n{}".format(raw_text)) |
|
|
| docx = nlp(raw_text) |
| if task_choice == 'Tokenization': |
| result = [token.text for token in docx ] |
| elif task_choice == 'Lemmatization': |
| result = ["'Token':{},'Lemma':{}".format(token.text,token.lemma_) for token in docx] |
| elif task_choice == 'Named Entity Recognition(NER)': |
| result = [(entity.text,entity.label_)for entity in docx.ents] |
| elif task_choice == 'Parts-of-Speech(POS) Tags': |
| result = ["'Token':{},'POS':{},'Dependency':{}".format(word.text,word.tag_,word.dep_) for word in docx] |
|
|
| st.json(result) |
|
|
| if st.button("Tabulize"): |
| docx = nlp(raw_text) |
| c_tokens = [token.text for token in docx ] |
| c_lemma = [token.lemma_ for token in docx ] |
| c_pos = [token.pos_ for token in docx ] |
|
|
| new_df = pd.DataFrame(zip(c_tokens,c_lemma,c_pos),columns=['Tokens','Lemma','POS']) |
| st.dataframe(new_df) |
|
|
|
|
| if st.checkbox("WordCloud"): |
| c_text = raw_text |
| wordcloud = WordCloud().generate(c_text) |
| plt.imshow(wordcloud,interpolation='bilinear') |
| plt.axis("off") |
| st.set_option('deprecation.showPyplotGlobalUse', False) |
| st.pyplot() |
|
|
| else: |
| st.write("") |
| st.subheader("About") |
| st.write("") |
| |
| st.markdown(""" |
| ### NLP Complaints Classifier With Different Models (With Streamlit) |
| Python Tools Used: spacy, pandas, matplotlib, wordcloud, Pillow(PIL), Joblib |
| """) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
| |
| |
| |
| |
| |
