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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import tensorflow | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import pickle | |
| from nltk.stem import WordNetLemmatizer | |
| import nltk | |
| import re | |
| import string | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| nltk.download('wordnet') | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize | |
| from PIL import Image | |
| import pickle | |
| # Load All Files | |
| with open('t.pickle', 'rb') as file_1: | |
| t = pickle.load(file_1) | |
| # Adding spanish to stopwords | |
| spanish = stopwords.words('spanish') | |
| additional_stopwords = [] | |
| for i in spanish: | |
| additional_stopwords.append(i) | |
| # Adding french to stopwords | |
| french = stopwords.words('french') | |
| for i in french: | |
| additional_stopwords.append(i) | |
| # Adding german to stopwords | |
| german = stopwords.words('german') | |
| for i in german: | |
| additional_stopwords.append(i) | |
| # Setting stopwords with english as default language | |
| stopwords = list(set(stopwords.words('english'))) | |
| for i in additional_stopwords: | |
| stopwords.append(i) | |
| lemmatizer = WordNetLemmatizer() | |
| def text_processing(text): | |
| # Converting all text to Lowercase | |
| text = text.lower() | |
| # Removing Unicode Characters | |
| text = re.sub("&#[A-Za-z0-9_]+", " ", text) | |
| # Removing punctuation | |
| text = text.translate(str.maketrans('', '', string.punctuation)) | |
| # Removing Whitespace | |
| text = text.strip() | |
| # Removing emoji | |
| text = re.sub("[^A-Za-z\s']", " ", text) | |
| # Removing double space | |
| text = re.sub("\s\s+" , " ", text) | |
| # Tokenizing words | |
| tokens = word_tokenize(text) | |
| # Removing Stopwords | |
| text = ' '.join([word for word in tokens if word not in stopwords]) | |
| # Lemmatizer | |
| text = lemmatizer.lemmatize(text) | |
| return text | |
| model_gruimp = load_model('best_model.h5', compile=False) | |
| def run(): | |
| with st.form(key='Amazon_Customer_Review'): | |
| st.title('Amazon Customer Review') | |
| image = Image.open('amazon1.jpg') | |
| st.image(image) | |
| review_title = st.text_input('Title',value='Mystery at Walt Disney World') | |
| review_text = st.text_input('Comments',value='Book arrived in good condition, but took quite a bit longer to arrive than normal. Overall, I am very satisfied.') | |
| submitted = st.form_submit_button('Submit') | |
| df_inf = { | |
| 'review_title': review_title, | |
| 'review_text': review_text, | |
| } | |
| df_inf = pd.DataFrame([df_inf]) | |
| # Data Inference | |
| df_inf_copy = df_inf.copy() | |
| # Applying all preprocessing in one document | |
| df_inf_copy['review_processed'] = df_inf_copy['review_text'].apply(lambda x: text_processing(x)) | |
| st.dataframe(df_inf_copy) | |
| # Transform Inference-Set | |
| df_inf_transform = df_inf_copy.review_processed | |
| df_inf_transform = t.texts_to_sequences(df_inf_transform) | |
| # Padding the dataset to a maximum review length in words | |
| df_inf_transform = pad_sequences(df_inf_transform, maxlen=117) | |
| if submitted: | |
| # Predict using Neural Network | |
| y_pred_inf = model_gruimp.predict(df_inf_transform) | |
| y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0) | |
| #st.write('# Is the customer at risk of churning ? :thinking_face:') | |
| if y_pred_inf == 0: | |
| st.subheader('Negative Feedback') | |
| st.write('Dear valued customer,') | |
| st.write('Thank you for taking the time to share your experience with us. We are sorry to hear that you were not satisfied with your recent purchase from Amazon. We take all feedback seriously and strive to provide the best possible shopping experience for our customers.') | |
| st.write('We apologize for any inconvenience caused and would like to make it right. Please let us know what specifically went wrong with your order and we will do everything we can to rectify the situation. We value your satisfaction and would appreciate the opportunity to earn back your trust.') | |
| st.write('Thank you again for bringing this to our attention. We hope to hear from you soon and look forward to the opportunity to serve you better in the future.') | |
| st.write('Best regards,') | |
| st.write('The Amazon Customer Service Team') | |
| else: | |
| st.subheader('Positive Feedback') | |
| st.write('Dear valued customer,') | |
| st.write('Thank you so much for taking the time to provide us with your positive feedback regarding your recent purchase from Amazon. We are thrilled to hear that you had a great shopping experience with us and that our product met your expectations.') | |
| st.write('At Amazon, we always strive to exceed our customers expectations and provide the best possible service. Your satisfaction is our top priority and we are delighted to know that we have achieved this with your recent purchase.') | |
| st.write('Your kind words have made our day and will serve as a motivation for us to continue delivering exceptional service to our customers. Thank you for choosing Amazon and we hope to have the pleasure of serving you again in the near future.') | |
| st.write('Best regards,') | |
| st.write('The Amazon Customer Service Team') | |
| if __name__ == '__main__': | |
| run() |