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import re
import pickle
import numpy as np
import pandas as pd
tfidf = pickle.load(open('models/tfidf.sav', 'rb'))
svc_sentiment = pickle.load(open('models/sentiment_model.sav', 'rb'))
tfidf_sentiment = pickle.load(open('models/tfidf_sentiment.sav', 'rb'))
svc = pickle.load(open('models/svc_model.sav', 'rb'))
labels = [
'Product quality', 'Knowledge',
'Appointment', 'Service etiquette', 'Waiting time',
'Repair speed', 'Repair cost', 'Repair quality', 'Warranty',
'Product replacement', 'Loan sets']
sample_file = pd.read_csv('sample.csv').to_csv(index=False).encode('utf-8')
print('utils imported!')
def get_single_prediction(text):
# manipulate data into a format that we pass to our model
text = text.lower().strip() #lower case
# Vectorise text and store in new dataframe. Sentence vector = average of word vectors
text_vectors = tfidf.transform(list(text))
# Make topic predictions
results = svc.predict_proba(text_vectors).squeeze().round(2)
pred_prob = pd.DataFrame({'topic': labels, 'probability': results}).sort_values('probability', ascending=True)
# Make sentiment predictions
text_vectors_sentiment = tfidf_sentiment.transform(list(text))
results_sentiment = svc_sentiment.predict_proba(text_vectors).squeeze().round(2)
pred_prob_sentiment = pd.DataFrame({'sentiment': ['Negative', 'Positive'], 'probability': results_sentiment}).sort_values('probability', ascending=True)
return (pred_prob, pred_prob_sentiment)
def get_multiple_predictions(csv):
df = pd.read_csv(csv)
df.columns = ['sequence']
df['sequence_clean'] = df['sequence'].str.lower().str.strip()
# Remove rows with blank string
invalid = df[(pd.isna(df['sequence_clean'])) | (df['sequence_clean'] == '')]
invalid.drop(columns=['sequence_clean'], inplace=True)
# Drop rows with blank string
df.dropna(inplace=True)
df = df[df['sequence_clean'] != ''].reset_index(drop=True)
# Vectorise text and get topic predictions
text_vectors = tfidf.transform(df['sequence_clean'])
pred_results = pd.DataFrame(svc.predict(text_vectors), columns = labels)
# Vectorise text and get sentiment predictions
text_vectors_sentiment = tfidf_sentiment.transform(df['sequence_clean'])
pred_results_sentiment = pd.DataFrame(svc_sentiment.predict(text_vectors_sentiment), columns = ['sentiment'])
# Join back to original sequence
final_results = df.join(pred_results).join(pred_results_sentiment)
final_results['others'] = final_results[labels].max(axis=1)
final_results['others'] = final_results['others'].apply(lambda x: 1 if x == 0 else 0)
final_results.drop(columns=['sequence_clean'], inplace=True)
# Append invalid rows
if len(invalid) == 0:
return final_results.to_csv(index=False).encode('utf-8')
else:
return pd.concat([final_results, invalid]).reset_index(drop=True).to_csv(index=False).encode('utf-8') |