Update app.py

app.py

@@ -1,8 +1,13 @@
 import google.generativeai as genai
 import gradio as gr
+from deep_translator import (GoogleTranslator)
+from transformers import pipeline
+
 
 api_key = "AIzaSyCmmus8HFPLXskU170_FR4j2CQeWZBKGMY"
 
+spam_detector = pipeline("text-classification", model="madhurjindal/autonlp-Gibberish-Detector-492513457")
+
 model = genai.GenerativeModel('gemini-pro')
 genai.configure(api_key = api_key)
 
@@ -11,14 +16,33 @@ def get_response(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 ["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}')
+        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]
     except Exception as e:
         return [-2, "Offensive"]
 
+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 pipeline(input_text):
+
+    input_text = translate(input_text)
+    
+    if spam_detection(input_text):
+        return get_response(input_text)
+        
+    else:
+        return "Spam" , ""
+
 iface = gr.Interface(
-    fn = get_response,
+    fn = pipeline,
     inputs = ["text"],
     outputs = ["text", "text"]
 )
+
 iface.launch(share=True)