Create test_integration.py

tests/test_integration.py

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+import pytest
+
+class TestIntegration:
+    """Integration tests for the complete workflow"""
+    
+    def test_complete_workflow_text_input(self, sample_medical_text):
+        """Test complete workflow from text input to SOAP output"""
+        def mock_complete_workflow(medical_text):
+            # Step 1: Validate input
+            if not medical_text.strip():
+                return "❌ No input provided"
+            
+            # Step 2: Process text (mock preprocessing)
+            processed_text = medical_text.strip()
+            
+            # Step 3: Generate SOAP (mocked)
+            soap_sections = [
+                "SUBJECTIVE:",
+                "Patient reports chest pain for 2 hours.",
+                "",
+                "OBJECTIVE:", 
+                "Vital signs show elevated blood pressure.",
+                "",
+                "ASSESSMENT:",
+                "Acute chest pain, rule out cardiac causes.",
+                "",
+                "PLAN:",
+                "Order EKG and cardiac enzymes."
+            ]
+            
+            # Step 4: Format output
+            result = "\n".join(soap_sections)
+            result += f"\n\n--- Processing Summary ---"
+            result += f"\nProcessed {len(medical_text)} characters of medical text."
+            result += f"\nGenerated SOAP note with {len(soap_sections)} sections."
+            
+            return result
+        
+        result = mock_complete_workflow(sample_medical_text)
+        
+        assert "SUBJECTIVE:" in result
+        assert "OBJECTIVE:" in result
+        assert "ASSESSMENT:" in result
+        assert "PLAN:" in result
+        assert "Processing Summary" in result
+        assert "characters of medical text" in result
+
+    def test_workflow_with_image_and_text(self, sample_medical_text, sample_image):
+        """Test workflow with both image and text input"""
+        def mock_workflow_with_image(text_input, image_input):
+            combined_text = text_input or ""
+            
+            if image_input is not None:
+                # Mock OCR processing
+                ocr_text = "Additional findings: Patient appears anxious, diaphoretic"
+                if combined_text:
+                    combined_text += f"\n\n--- Extracted from image ---\n{ocr_text}"
+                else:
+                    combined_text = ocr_text
+            
+            if not combined_text:
+                return "❌ No input provided"
+            
+            # Mock SOAP generation
+            sections = ["SUBJECTIVE", "OBJECTIVE", "ASSESSMENT", "PLAN"]
+            soap_content = []
+            
+            for section in sections:
+                soap_content.append(f"{section}:")
+                soap_content.append(f"Content for {section.lower()} section")
+                soap_content.append("")
+            
+            result = "\n".join(soap_content)
+            result += f"\n--- Workflow Summary ---"
+            result += f"\nCombined input: {len(combined_text)} characters"
+            result += f"\nImage processing: {'Yes' if image_input else 'No'}"
+            
+            return result
+        
+        result = mock_workflow_with_image(sample_medical_text, sample_image)
+        assert "SUBJECTIVE:" in result
+        assert "OBJECTIVE:" in result
+        assert "ASSESSMENT:" in result
+        assert "PLAN:" in result
+        assert "Workflow Summary" in result
+        assert "Image processing: Yes" in result
+
+    def test_error_recovery_workflow(self):
+        """Test workflow error handling and recovery"""
+        def mock_error_recovery_workflow(input_data, simulate_error=False):
+            try:
+                if simulate_error:
+                    raise Exception("Simulated processing error")
+                
+                if not input_data:
+                    return "❌ No input provided"
+                
+                # Simulate successful processing
+                return "✅ SOAP note generated successfully"
+                
+            except Exception as e:
+                # Error recovery
+                error_msg = f"❌ Error occurred: {str(e)}"
+                recovery_msg = "\n💡 Please try again with different input or check your image quality."
+                return error_msg + recovery_msg
+        
+        # Test normal operation
+        result = mock_error_recovery_workflow("Valid input")
+        assert "✅ SOAP note generated successfully" in result
+        
+        # Test error handling
+        result = mock_error_recovery_workflow("Input", simulate_error=True)
+        assert "❌ Error occurred:" in result
+        assert "💡 Please try again" in result
+        
+        # Test empty input
+        result = mock_error_recovery_workflow("")
+        assert "❌ No input provided" in result
+
+    def test_performance_workflow(self, sample_medical_text):
+        """Test workflow performance characteristics"""
+        def mock_performance_workflow(input_text):
+            import time
+            
+            start_time = time.time()
+            
+            # Simulate processing steps
+            steps = [
+                "Validating input",
+                "Preprocessing text", 
+                "Extracting medical entities",
+                "Generating SOAP structure",
+                "Formatting output"
+            ]
+            
+            processing_log = []
+            for i, step in enumerate(steps):
+                step_time = time.time()
+                processing_log.append(f"Step {i+1}: {step} - {step_time - start_time:.3f}s")
+                time.sleep(0.01)  # Simulate processing time
+            
+            total_time = time.time() - start_time
+            
+            result = "Generated SOAP Note\n\n"
+            result += "--- Performance Log ---\n"
+            result += "\n".join(processing_log)
+            result += f"\nTotal processing time: {total_time:.3f}s"
+            
+            return result, total_time
+        
+        result, processing_time = mock_performance_workflow(sample_medical_text)
+        
+        assert "Generated SOAP Note" in result
+        assert "Performance Log" in result
+        assert "Total processing time:" in result
+        assert processing_time > 0
+        assert processing_time < 1.0  # Should be fast for mocked version
+
+    def test_batch_processing_workflow(self):
+        """Test batch processing of multiple medical notes"""
+        def mock_batch_workflow(medical_notes_list):
+            if not medical_notes_list:
+                return "❌ No notes provided for batch processing"
+            
+            results = []
+            
+            for i, notes in enumerate(medical_notes_list):
+                if not notes.strip():
+                    results.append(f"Note {i+1}: ❌ Empty input")
+                    continue
+                
+                # Mock SOAP generation for each note
+                soap_result = f"""Note {i+1} - SOAP Generated:
+SUBJECTIVE: Patient presentation from note {i+1}
+OBJECTIVE: Clinical findings 
+ASSESSMENT: Medical diagnosis
+PLAN: Treatment approach
+"""
+                results.append(soap_result)
+            
+            summary = f"\n--- Batch Summary ---"
+            summary += f"\nTotal notes processed: {len(medical_notes_list)}"
+            summary += f"\nSuccessful: {len([r for r in results if '❌' not in r])}"
+            summary += f"\nFailed: {len([r for r in results if '❌' in r])}"
+            
+            return "\n".join(results) + summary
+        
+        # Test batch processing
+        notes_batch = [
+            "Patient 1: Chest pain complaint",
+            "Patient 2: Diabetes follow-up", 
+            "",  # Empty note
+            "Patient 3: Pediatric fever case"
+        ]
+        
+        result = mock_batch_workflow(notes_batch)
+        
+        assert "Note 1 - SOAP Generated:" in result
+        assert "Note 2 - SOAP Generated:" in result
+        assert "Note 3: ❌ Empty input" in result
+        assert "Note 4 - SOAP Generated:" in result
+        assert "Batch Summary" in result
+        assert "Total notes processed: 4" in result
+        assert "Successful: 3" in result
+        assert "Failed: 1" in result
+
+    def test_gradio_integration(self, sample_medical_text, sample_image):
+        """Test Gradio interface integration"""
+        def mock_gradio_integration(text_input, image_input, example_selection=None):
+            # Simulate Gradio interface behavior
+            if example_selection:
+                # Load example
+                examples = {
+                    "chest_pain": "Example chest pain case",
+                    "diabetes": "Example diabetes case", 
+                    "pediatric": "Example pediatric case"
+                }
+                text_input = examples.get(example_selection, text_input)
+            
+            # Process inputs (same as gradio_generate_soap)
+            final_text = text_input or ""
+            
+            if image_input is not None:
+                ocr_result = "OCR extracted: Patient vital signs documented"
+                if final_text:
+                    final_text += f"\n\n--- From Image ---\n{ocr_result}"
+                else:
+                    final_text = ocr_result
+            
+            if not final_text:
+                return "❌ Please provide input"
+            
+            # Generate SOAP
+            soap_output = f"""📋 SOAP NOTE GENERATED
+
+SUBJECTIVE:
+{final_text[:100]}...
+
+OBJECTIVE:
+Clinical examination findings documented.
+
+ASSESSMENT:  
+Medical evaluation completed.
+
+PLAN:
+Treatment recommendations provided.
+
+--- Generated via Gradio Interface ---
+Input sources: {'Text + Image' if text_input and image_input else 'Text' if text_input else 'Image'}
+Processing timestamp: 2025-01-15 10:30:00
+"""
+            return soap_output
+        
+        # Test text only
+        result = mock_gradio_integration(sample_medical_text, None)
+        assert "📋 SOAP NOTE GENERATED" in result
+        assert "Input sources: Text" in result
+        
+        # Test image only  
+        result = mock_gradio_integration("", sample_image)
+        assert "📋 SOAP NOTE GENERATED" in result
+        assert "From Image" in result
+        
+        # Test text + image
+        result = mock_gradio_integration(sample_medical_text, sample_image)
+        assert "Input sources: Text + Image" in result
+        
+        # Test example selection
+        result = mock_gradio_integration("", None, "chest_pain")
+        assert "Example chest pain case" in result