--- a
+++ b/tests/API_test.py
@@ -0,0 +1,175 @@
+import unittest
+import requests
+import json
+from typing import Dict, List
+import time
+from src.preprocessing.preprocessing import create_ordered_medical_pipeline
+
+
+class TestPredictionAPI(unittest.TestCase):
+    """Test suite for the Prediction API with detailed results"""
+
+    BASE_URL = "http://localhost:5000"
+
+    # Sample texts for each disease category based on EDA
+    TEST_CASES = {
+        "Dementia": {
+            "case_1": {
+                "description": "Dementia patient showing progressive cognitive decline with memory loss. MMSE score: 22. Difficulties with daily activities and confusion about time and place.",
+                "expected_label": "Dementia"
+            },
+            "case_2": {
+                "description": "this is a test description about Dementia",
+                "expected_label": "Dementia"
+            }
+        },
+        "ALS": {
+            "case_1": {
+                "description": "ALS Patient presents with progressive muscle weakness. FVC at 65% with declining respiratory function. ALSFRS-R score: 38.",
+                "expected_label": "ALS"
+            },
+            "case_2": {
+                "description": "Bulbar onset ALS with respiratory complications. FVC measurements show decline. ALSFRS-R decreased from 42 to 35.",
+                "expected_label": "ALS"
+            }
+        },
+        "Obsessive Compulsive Disorder": {
+            "case_1": {
+                "description": "Obsessive Compulsive Disorder Patient exhibits ritualistic behaviors and intrusive thoughts. Y-BOCS score: 28. Significant impact on daily functioning.",
+                "expected_label": "Obsessive Compulsive Disorder"
+            },
+            "case_2": {
+                "description": "Severe Obsessive Compulsive Disorder symptoms with compulsive checking behavior. Y-BOCS assessment indicates severe symptoms at 26.",
+                "expected_label": "Obsessive Compulsive Disorder"
+            }
+        },
+        "Scoliosis": {
+            "case_1": {
+                "description": "Scoliosis adolescent with spinal curvature. Cobb angle measurement of 45 degrees. Surgical correction being considered.",
+                "expected_label": "Scoliosis"
+            },
+            "case_2": {
+                "description": "Scoliosis progressive thoracic curve with 38-degree Cobb angle. Brace treatment initiated.",
+                "expected_label": "Scoliosis"
+            }
+        },
+        "Parkinson's Disease": {
+            "case_1": {
+                "description": "Parkinson Patient presents with resting tremor and bradykinesia. UPDRS score: 45. Started on levodopa therapy.",
+                "expected_label": "Parkinson's Disease"
+            },
+            "case_2": {
+                "description": "Progressive Parkinson symptoms with rigidity. Hoehn and Yahr stage 2. Motor symptoms predominant.",
+                "expected_label": "Parkinson's Disease"
+            }
+        }
+    }
+
+    def setUp(self):
+        """Set up test case"""
+        try:
+            response = requests.get(f"{self.BASE_URL}/health")
+            response.raise_for_status()
+            print("\nAPI Health Check: OK")
+        except requests.exceptions.RequestException as e:
+            raise unittest.SkipTest(f"API is not running: {str(e)}")
+
+    def test_predictions_and_generate_report(self):
+        """Test predictions for all classes and generate detailed JSON report"""
+
+        results = {
+            "test_timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+            "total_cases": 0,
+            "successful_predictions": 0,
+            "failed_predictions": 0,
+            "results_by_disease": {},
+            "summary": {
+                "accuracy_by_disease": {}
+            }
+        }
+
+        # Test each disease category
+        for disease, cases in self.TEST_CASES.items():
+            disease_results = {
+                "total_cases": len(cases),
+                "successful_predictions": 0,
+                "predictions": []
+            }
+
+            # Test each case for the disease
+            for case_name, case_data in cases.items():
+                print(f"\nTesting {disease} - {case_name}")
+
+                # Make prediction request
+                try:
+                    response = requests.post(
+                        f"{self.BASE_URL}/predict",
+                        json={"description": case_data["description"]}
+                    )
+
+                    prediction_result = {
+                        "case": case_name,
+                        "expected_label": case_data["expected_label"],
+                        "description": case_data["description"],
+                        "api_response": {
+                            "status_code": response.status_code,
+                            "response_time": response.elapsed.total_seconds()
+                        }
+                    }
+
+                    if response.status_code == 200:
+                        response_data = response.json()
+                        prediction_result["prediction"] = response_data["prediction"]
+                        prediction_result["confidence"] = response_data.get("confidence")
+                        prediction_result["correct"] = (
+                                response_data["prediction"] == case_data["expected_label"]
+                        )
+
+                        if prediction_result["correct"]:
+                            disease_results["successful_predictions"] += 1
+                            results["successful_predictions"] += 1
+                    else:
+                        prediction_result["error"] = str(response.text)
+                        results["failed_predictions"] += 1
+
+                    disease_results["predictions"].append(prediction_result)
+                    results["total_cases"] += 1
+
+                except Exception as e:
+                    print(f"Error processing case {case_name}: {str(e)}")
+                    results["failed_predictions"] += 1
+
+            # Calculate accuracy for this disease
+            if disease_results["total_cases"] > 0:
+                accuracy = (
+                        disease_results["successful_predictions"] /
+                        disease_results["total_cases"]
+                )
+                results["summary"]["accuracy_by_disease"][disease] = accuracy
+
+            results["results_by_disease"][disease] = disease_results
+
+        # Calculate overall accuracy
+        total_successful = results["successful_predictions"]
+        total_cases = results["total_cases"]
+        results["summary"]["overall_accuracy"] = (
+            total_successful / total_cases if total_cases > 0 else 0
+        )
+
+        # Save results to file
+        with open('prediction_test_results.json', 'w') as f:
+            json.dump(results, f, indent=2)
+
+        print("\nTest Results Summary:")
+        print(f"Total Cases: {results['total_cases']}")
+        print(f"Successful Predictions: {results['successful_predictions']}")
+        print(f"Failed Predictions: {results['failed_predictions']}")
+        print("\nAccuracy by Disease:")
+        for disease, accuracy in results["summary"]["accuracy_by_disease"].items():
+            print(f"{disease}: {accuracy:.2%}")
+        print(f"\nOverall Accuracy: {results['summary']['overall_accuracy']:.2%}")
+        print("\nDetailed results saved to prediction_test_results.json")
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)