Update app.py

app.py

@@ -37,9 +37,8 @@ class ONNXInferencePipeline:
         self.session.set_providers(self.providers)
 
     def load_banned_keywords(self):
-        # Define a list of banned keywords
-        # This can be loaded from a file or defined directly here
-        # For this example, I'm using a shortened list from your document
+        # Load your comprehensive list of banned keywords
+        # In a production environment, this would be your full list of 500+ keywords
         return [
     # Original banned keywords
     "fuck", "shit", "bitch", "cunt", "asshole", "faggot", "nigger", "bastard", "damn", "crap",
@@ -837,9 +836,7 @@ class ONNXInferencePipeline:
         if self.contains_banned_keyword(text):
             return {
                 'label': 'Inappropriate Content',
-                'confidence': 1.0,
-                'probabilities': [1.0, 0.0],  # Assuming [inappropriate, appropriate]
-                'keyword_matched': True  # Flag to indicate keyword match
+                'probabilities': [1.0, 0.0]  # Assuming [inappropriate, appropriate]
             }
         
         # If no banned keywords, proceed with model prediction
@@ -861,9 +858,7 @@ class ONNXInferencePipeline:
         class_labels = ['Inappropriate Content', 'Appropriate']
         return {
             'label': class_labels[predicted_class],
-            'confidence': float(probabilities[0][predicted_class]),
-            'probabilities': probabilities[0].tolist(),
-            'keyword_matched': False  # Flag to indicate model prediction
+            'probabilities': probabilities[0].tolist()
         }
 
 # Example usage
@@ -873,27 +868,23 @@ if __name__ == "__main__":
 
     # Example texts for testing
     example_texts = [
-        "You're a worthless piece of garbage who should die",
-        "Hello, how are you doing today?"
+        "You're a worthless piece of garbage who should die"
     ]
     
     for text in example_texts:
         result = pipeline.predict(text)
         print(f"Input: {text}")
-        print(f"Prediction: {result['label']}")
-        if result['keyword_matched']:
-            print("(Detected by keyword filter)")
-        else:
-            print(f"Confidence: {result['confidence']:.2%}")
+        print(f"Prediction: {result['label']} ")
+        #print(f"Probabilities: Inappropriate={result['probabilities'][0]:.2%}, Not Inappropriate={result['probabilities'][1]:.2%}")
         print("-" * 80)
 
     # Define a function for Gradio to use
     def gradio_predict(text):
         result = pipeline.predict(text)
-        if result['keyword_matched']:
-            return f"Prediction: {result['label']} (Detected by keyword filter)"
-        else:
-            return f"Prediction: {result['label']} (Confidence: {result['confidence']:.2%})"
+        return (
+            f"Prediction: {result['label']} \n"
+            #f"Probabilities: Inappropriate={result['probabilities'][0]:.2%}, Not Inappropriate={result['probabilities'][1]:.2%}"
+        )
 
     # Create a Gradio interface
     iface = gr.Interface(