| from transformers import pipeline | |
| import gradio as gr | |
| from nltk.sentiment.vader import SentimentIntensityAnalyzer | |
| import nltk | |
| import numpy as np | |
| nltk.download('vader_lexicon') | |
| from deep_translator import (GoogleTranslator) | |
| from langdetect import detect | |
| zero_shot_classifier = pipeline("zero-shot-classification" , model='roberta-large-mnli') | |
| spam_detector = pipeline("text-classification", model="madhurjindal/autonlp-Gibberish-Detector-492513457") | |
| issues = ["Misconduct" , "Negligence" , "Discrimination" , "Corruption" , "Violation of Rights" , "Inefficiency" , | |
| "Unprofessional Conduct", "Response Time" , "Use of Firearms" , "Property Damage"] | |
| apprecn = ["Tech-Savvy Staff" , "Co-operative Staff" , "Well-Maintained Premises" , "Responsive Staff"] | |
| def translate(input_text): | |
| source_lang = detect(input_text) | |
| translated = GoogleTranslator(source=source_lang, target='en').translate(text=input_text) | |
| return translated | |
| def spam_detection(input_text): | |
| return spam_detector(input_text)[0]['label'] == 'clean' | |
| def sentiment_analysis(input_text): | |
| score = SentimentIntensityAnalyzer().polarity_scores(input_text) | |
| del score['compound'] | |
| label = list(filter(lambda x: score[x] == max(score.values()), score))[0] | |
| if label == 'neg': | |
| return ["Negative Feedback" , score['neg']] | |
| elif label == 'pos': | |
| return ["Positive Feedback" , -1] | |
| else: | |
| return ["Neutral Feedback" , -1] | |
| def positive_zero_shot(input_text): | |
| return zero_shot_classifier(input_text , candidate_labels = apprecn , multi_label = False)['labels'][0] | |
| def negative_zero_shot(input_text): | |
| return zero_shot_classifier(input_text , candidate_labels = issues , multi_label = False)['labels'][0] | |
| def pipeline(input_text): | |
| input_text = translate(input_text) | |
| if spam_detection(input_text): | |
| if sentiment_analysis(input_text)[0] == "Positive Feedback": | |
| return "Positive Feedback" , -1 , positive_zero_shot(input_text) | |
| elif sentiment_analysis(input_text)[0] == "Negative Feedback": | |
| return "Negative Feedback" , sentiment_analysis(input_text)[1] , negative_zero_shot(input_text) | |
| else: | |
| return "Neutral Feedback" , -1 , "" | |
| else: | |
| return "Spam" , "" | |
| iface = gr.Interface(fn = pipeline , inputs=['text'] , outputs=['text' , 'text' , 'text']) | |
| iface.launch(share=True) |