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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 |