Update app.py

enhanced to handle casual / malicuous / threat message and do NER only if mrkt research text.
Used Zero short classifier !

app.py

@@ -65,7 +65,7 @@ def predict_ner(text):
 # Define the classification function
 def classify_text(text):
     # Refined labels for better classification
-    candidate_labels = ["market research", "casual/social message"]
+    candidate_labels = ["market research", "casual/social message", "security threat", "malicious"]
     hypothesis_template = "This text is a {}."
 
     # Classify the text
@@ -81,7 +81,13 @@ def process_text(text):
     # Classify the text as social or research
     classification, confidence = classify_text(text)
 
-    if classification == "casual/social message" and confidence > 0.8:
+    print(classification)
+    if classification == "security threat" or classification == "malicious":
+        # If it's social text with high confidence, print it as a social message
+        print("This is a security threat:")
+        print(text)
+        return ["This is not a market research text!"]
+    elif classification == "casual/social message" and confidence > 0.8:
         # If it's social text with high confidence, print it as a social message
         print("This is a social message:")
         print(text)
@@ -94,7 +100,7 @@ def process_text(text):
         retail_confidence = retail_classification["scores"][0]
 
         if retail_label != "r" and retail_confidence > 0.2:
-            # If it's about retail, send it to the NER pipeline
+            # TODO If it's about retail, send it to the NER pipeline
             print("This is a research text about retail. Extracting entities...")
             predictions = predict_ner(text)