Create app.py

app.py

@@ -0,0 +1,82 @@
+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)