Update app.py

app.py

@@ -2,6 +2,7 @@ import google.generativeai as genai
 import gradio as gr
 from deep_translator import (GoogleTranslator)
 from transformers import pipeline
+from langdetect import detect
 
 
 api_key = "AIzaSyCmmus8HFPLXskU170_FR4j2CQeWZBKGMY"
@@ -11,15 +12,13 @@ spam_detector = pipeline("text-classification", model="madhurjindal/autonlp-Gibb
 model = genai.GenerativeModel('gemini-pro')
 genai.configure(api_key = api_key)
 
-def get_response(feedback):
-    
+def sentiment(feedback):
     try:
         #response = model.generate_content(f"State whether given response is positive, negative or neutral in one word: {feedback}")
         score = model.generate_content(f"Give me the polarity score between -1 to 1 for: {feedback}")
-        issue = model.generate_content(f'Issues should be from ["Tech-Savvy Staff" , "Co-operative Staff" , "Well-Maintained Premises" , "Responsive Staff", "Misconduct" , "Negligence" , "Discrimination" , "Corruption" , "Violation of Rights" , "Inefficiency" , "Unprofessional Conduct", "Response Time" , "Use of Firearms" , "Property Damage"]. Give me the issue faced by the feedback giver in less than four words: {feedback}')
-        return [score.text, issue.text]
+        return score.text
     except Exception as e:
-        return [-2, "Offensive"]
+        return "-1"
 
 def translate(input_text):
     source_lang = detect(input_text)
@@ -29,15 +28,38 @@ def translate(input_text):
 def spam_detection(input_text):
     return spam_detector(input_text)[0]['label'] == 'clean'
 
+def negative_zero_shot(input_text):
+    try:
+        return model.generate_content(f'Issues should be from ["Misconduct" , "Negligence" , "Discrimination" , "Corruption" , "Violation of Rights" , "Inefficiency" , "Unprofessional Conduct", "Response Time" , "Use of Firearms" , "Property Damage"] only. Give me the issue faced by the feedback giver in less than four words: {input_text}').text
+    except Exception as e:
+        return "Offensive"
+
+def positive_zero_shot(input_text):
+    try:
+        return model.generate_content(f'Issues should be from ["Miscellaneous", "Tech-Savvy Staff" , "Co-operative Staff" , "Well-Maintained Premises" , "Responsive Staff"] only. Give me the issue faced by the feedback giver in less than four words: {input_text}').text
+    except Exception as e:
+        return "Offensive"
 def pipeline(input_text):
 
     input_text = translate(input_text)
     
     if spam_detection(input_text):
-        return get_response(input_text)
+
+        sent = float(sentiment(input_text))
+        
+        if sent > 0:
+            
+            return str(sent), positive_zero_shot(input_text)
+        
+        elif sent < 0:
+            
+            return str(sent),  negative_zero_shot(input_text)
         
+        else:
+            
+            return "0", "No issue"
     else:
-        return "Spam" , ""
+        return "42", "Spam"
 
 iface = gr.Interface(
     fn = pipeline,