Fix/improve batch classification test (#319)

* feat: improve batch classification test to validate accuracy

Previously, the batch classification test only validated HTTP status
and result count, but never checked if the classifications were correct.
The expected_categories variable was created but never used for validation.

Changes:
- Extract actual categories from batch classification results
- Compare against expected categories and calculate accuracy percentage
- Add detailed output showing each classification result
- Assert that accuracy meets 75% threshold
- Maintain backward compatibility with existing HTTP/count checks

This improved test now properly catches classification accuracy issues
and will fail when the classification system returns incorrect results,
exposing problems that were previously hidden.

Related to issue #318: Batch Classification API Returns Incorrect Categories

Signed-off-by: Yossi Ovadia <[email protected]>

* style: apply black formatting to classification test

Automatic formatting applied by black pre-commit hook.

Signed-off-by: Yossi Ovadia <[email protected]>

---------

Signed-off-by: Yossi Ovadia <[email protected]>
Signed-off-by: liuhy <[email protected]>

Author: yossiovadia

e2e-tests/03-classification-api-test.py

@@ -189,29 +189,80 @@ def test_batch_classification(self):
         response_json = response.json()
         results = response_json.get("results", [])
 
+        # Extract actual categories from results
+        actual_categories = []
+        correct_classifications = 0
+
+        for i, result in enumerate(results):
+            if isinstance(result, dict):
+                actual_category = result.get("category", "unknown")
+            else:
+                actual_category = "unknown"
+
+            actual_categories.append(actual_category)
+
+            if (
+                i < len(expected_categories)
+                and actual_category == expected_categories[i]
+            ):
+                correct_classifications += 1
+
+        # Calculate accuracy
+        accuracy = (
+            (correct_classifications / len(expected_categories)) * 100
+            if expected_categories
+            else 0
+        )
+
         self.print_response_info(
             response,
             {
                 "Total Texts": len(texts),
                 "Results Count": len(results),
                 "Processing Time (ms)": response_json.get("processing_time_ms", 0),
+                "Accuracy": f"{accuracy:.1f}% ({correct_classifications}/{len(expected_categories)})",
             },
         )
 
-        passed = response.status_code == 200 and len(results) == len(texts)
+        # Print detailed classification results
+        print("\n📊 Detailed Classification Results:")
+        for i, (text, expected, actual) in enumerate(
+            zip(texts, expected_categories, actual_categories)
+        ):
+            status = "✅" if expected == actual else "❌"
+            print(f"  {i+1}. {status} Expected: {expected:<15} | Actual: {actual:<15}")
+            print(f"     Text: {text[:60]}...")
+
+        # Check basic requirements first
+        basic_checks_passed = response.status_code == 200 and len(results) == len(texts)
+
+        # Check classification accuracy (should be high for a working system)
+        accuracy_threshold = 75.0  # Expect at least 75% accuracy
+        accuracy_passed = accuracy >= accuracy_threshold
+
+        overall_passed = basic_checks_passed and accuracy_passed
 
         self.print_test_result(
-            passed=passed,
+            passed=overall_passed,
             message=(
-                f"Successfully classified {len(results)} texts"
-                if passed
-                else f"Batch classification failed or returned wrong count"
+                f"Successfully classified {len(results)} texts with {accuracy:.1f}% accuracy"
+                if overall_passed
+                else f"Batch classification issues: Basic checks: {basic_checks_passed}, Accuracy: {accuracy:.1f}% (threshold: {accuracy_threshold}%)"
             ),
         )
 
+        # Basic checks
         self.assertEqual(response.status_code, 200, "Batch request failed")
         self.assertEqual(len(results), len(texts), "Result count mismatch")
 
+        # NEW: Validate classification accuracy
+        self.assertGreaterEqual(
+            accuracy,
+            accuracy_threshold,
+            f"Classification accuracy too low: {accuracy:.1f}% < {accuracy_threshold}%. "
+            f"Expected: {expected_categories}, Actual: {actual_categories}",
+        )
+
 
 if __name__ == "__main__":
     unittest.main()