diff --git a/week6/community-contributions/hopeogbons/classifier_tester.py b/week6/community-contributions/hopeogbons/classifier_tester.py
new file mode 100644
index 0000000..c9662f7
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+++ b/week6/community-contributions/hopeogbons/classifier_tester.py
@@ -0,0 +1,123 @@
+"""
+Classification Tester for Banking Intent Model
+Evaluates model accuracy on intent classification
+"""
+
+import matplotlib.pyplot as plt
+from collections import Counter
+from banking_intents import get_intent
+
+GREEN = "\033[92m"
+RED = "\033[91m"
+RESET = "\033[0m"
+
+
+class ClassifierTester:
+    """Test framework for classification models"""
+    
+    def __init__(self, predictor, data, title=None, size=100):
+        self.predictor = predictor
+        self.data = data
+        self.title = title or predictor.__name__.replace("_", " ").title()
+        self.size = min(size, len(data))
+        self.predictions = []
+        self.actuals = []
+        self.correct = 0
+        self.incorrect = 0
+    
+    def run_datapoint(self, i):
+        """Test a single example"""
+        item = self.data[i]
+        
+        # Get prediction
+        predicted_intent = self.predictor(item)
+        actual_intent = get_intent(item['label'])
+        
+        # Check if correct
+        is_correct = predicted_intent == actual_intent
+        
+        if is_correct:
+            self.correct += 1
+            color = GREEN
+            status = "✓"
+        else:
+            self.incorrect += 1
+            color = RED
+            status = "✗"
+        
+        self.predictions.append(predicted_intent)
+        self.actuals.append(actual_intent)
+        
+        # Print result
+        query = item['text'][:60] + "..." if len(item['text']) > 60 else item['text']
+        print(f"{color}{status} {i+1}: {query}")
+        print(f"   Predicted: {predicted_intent} | Actual: {actual_intent}{RESET}")
+    
+    def chart(self):
+        """Visualize top confusion pairs"""
+        # Find misclassifications
+        errors = {}
+        for pred, actual in zip(self.predictions, self.actuals):
+            if pred != actual:
+                pair = f"{actual} → {pred}"
+                errors[pair] = errors.get(pair, 0) + 1
+        
+        if not errors:
+            print("\n🎉 Perfect accuracy - no confusion to plot!")
+            return
+        
+        # Plot top 10 confusions
+        top_errors = sorted(errors.items(), key=lambda x: x[1], reverse=True)[:10]
+        
+        if top_errors:
+            labels = [pair for pair, _ in top_errors]
+            counts = [count for _, count in top_errors]
+            
+            plt.figure(figsize=(12, 6))
+            plt.barh(labels, counts, color='coral')
+            plt.xlabel('Count')
+            plt.title('Top 10 Confusion Pairs (Actual → Predicted)')
+            plt.tight_layout()
+            plt.show()
+    
+    def report(self):
+        """Print final metrics and chart"""
+        accuracy = (self.correct / self.size) * 100
+        
+        print("\n" + "="*70)
+        print(f"MODEL: {self.title}")
+        print(f"TESTED: {self.size} examples")
+        print(f"CORRECT: {self.correct} ({accuracy:.1f}%)")
+        print(f"INCORRECT: {self.incorrect}")
+        print("="*70)
+        
+        # Show most common errors
+        if self.incorrect > 0:
+            print("\nMost Common Errors:")
+            error_pairs = [(self.actuals[i], self.predictions[i]) 
+                          for i in range(len(self.actuals)) 
+                          if self.actuals[i] != self.predictions[i]]
+            error_counts = Counter(error_pairs).most_common(5)
+            
+            for (actual, pred), count in error_counts:
+                print(f"  {actual} → {pred}: {count} times")
+        
+        # Chart
+        self.chart()
+        
+        return accuracy
+    
+    def run(self):
+        """Run the complete evaluation"""
+        print(f"Testing {self.title} on {self.size} examples...\n")
+        
+        for i in range(self.size):
+            self.run_datapoint(i)
+        
+        return self.report()
+    
+    @classmethod
+    def test(cls, function, data, size=100):
+        """Convenience method to test a predictor function"""
+        return cls(function, data, size=size).run()
+