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Test script for Pathora Colposcopy API endpoints
Demonstrates how to use both AI model and LLM endpoints
"""
import requests
import json
import base64
from pathlib import Path
# API Configuration
BASE_URL = "http://localhost:8000" # Change to your deployment URL
API_KEY = "your_gemini_api_key_here" # For local testing
def test_health_check():
"""Test the health check endpoint"""
print("=" * 60)
print("Testing Health Check Endpoint")
print("=" * 60)
response = requests.get(f"{BASE_URL}/health")
print(f"Status Code: {response.status_code}")
print(f"Response: {json.dumps(response.json(), indent=2)}")
print()
def test_acetowhite_detection(image_path: str):
"""Test acetowhite contour detection"""
print("=" * 60)
print("Testing Acetowhite Contour Detection")
print("=" * 60)
with open(image_path, 'rb') as f:
files = {'file': f}
data = {'conf_threshold': 0.5}
response = requests.post(
f"{BASE_URL}/api/infer-aw-contour",
files=files,
data=data
)
print(f"Status Code: {response.status_code}")
result = response.json()
# Print without base64 image for readability
print(f"Status: {result.get('status')}")
print(f"Detections: {result.get('detections')}")
print(f"Contours: {len(result.get('contours', []))}")
print(f"Confidence Threshold: {result.get('confidence_threshold')}")
# Save result image if available
if result.get('result_image'):
output_path = "test_output_aw.png"
img_data = base64.b64decode(result['result_image'])
with open(output_path, 'wb') as f:
f.write(img_data)
print(f"Result image saved to: {output_path}")
print()
def test_cervix_detection(image_path: str):
"""Test cervix bounding box detection"""
print("=" * 60)
print("Testing Cervix Bounding Box Detection")
print("=" * 60)
with open(image_path, 'rb') as f:
files = {'file': f}
data = {'conf_threshold': 0.4}
response = requests.post(
f"{BASE_URL}/api/infer-cervix-bbox",
files=files,
data=data
)
print(f"Status Code: {response.status_code}")
result = response.json()
print(f"Status: {result.get('status')}")
print(f"Detections: {result.get('detections')}")
print(f"Bounding Boxes: {json.dumps(result.get('bounding_boxes', []), indent=2)}")
# Save result image if available
if result.get('result_image'):
output_path = "test_output_cervix.png"
img_data = base64.b64decode(result['result_image'])
with open(output_path, 'wb') as f:
f.write(img_data)
print(f"Result image saved to: {output_path}")
print()
def test_batch_inference(image_paths: list):
"""Test batch inference on multiple images"""
print("=" * 60)
print("Testing Batch Inference")
print("=" * 60)
files = [('files', open(img, 'rb')) for img in image_paths]
data = {'conf_threshold': 0.5}
response = requests.post(
f"{BASE_URL}/api/batch-infer",
files=files,
data=data
)
# Close file handles
for _, f in files:
f.close()
print(f"Status Code: {response.status_code}")
result = response.json()
print(f"Status: {result.get('status')}")
print(f"Total Files: {result.get('total_files')}")
for i, res in enumerate(result.get('results', [])):
print(f"\nImage {i+1}: {res.get('filename')}")
print(f" Status: {res.get('status')}")
print(f" Detections: {res.get('detections')}")
print()
def test_chat():
"""Test LLM chat endpoint"""
print("=" * 60)
print("Testing Chat Endpoint")
print("=" * 60)
payload = {
"message": "What are the typical signs of a high-grade squamous intraepithelial lesion (HSIL) on colposcopy?",
"history": []
}
response = requests.post(
f"{BASE_URL}/api/chat",
json=payload
)
print(f"Status Code: {response.status_code}")
if response.status_code == 200:
result = response.json()
print(f"Status: {result.get('status')}")
print(f"Model: {result.get('model')}")
print(f"Response:\n{result.get('response')}")
else:
print(f"Error: {response.json()}")
print()
def test_chat_with_history():
"""Test chat with conversation history"""
print("=" * 60)
print("Testing Chat with History")
print("=" * 60)
payload = {
"message": "What about low-grade lesions?",
"history": [
{
"role": "user",
"text": "What are high-grade lesions?"
},
{
"role": "bot",
"text": "High-grade lesions (HSIL) show dense acetowhite epithelium, coarse punctation, and sharp borders."
}
]
}
response = requests.post(
f"{BASE_URL}/api/chat",
json=payload
)
print(f"Status Code: {response.status_code}")
if response.status_code == 200:
result = response.json()
print(f"Response:\n{result.get('response')}")
else:
print(f"Error: {response.json()}")
print()
def test_report_generation():
"""Test report generation endpoint"""
print("=" * 60)
print("Testing Report Generation")
print("=" * 60)
payload = {
"patient_data": {
"age": 35,
"gravida": 2,
"para": 2,
"lmp": "2024-02-01",
"indication": "Abnormal Pap smear - ASCUS",
"menstrual_status": "Regular"
},
"exam_findings": {
"native": {
"cervix_visible": True,
"transformation_zone": "Type 1 (fully visible)",
"ectropion": "Mild",
"discharge": "None"
},
"acetic_acid": {
"acetowhite_lesions": True,
"location": "6-9 o'clock position",
"density": "Dense white",
"borders": "Sharp, well-defined",
"size": "Moderate (covering 2 quadrants)"
},
"green_filter": {
"vascular_patterns": "Coarse punctation",
"mosaic": "Present",
"atypical_vessels": "None"
},
"lugol": {
"iodine_uptake": "Partial iodine negative area",
"pattern": "Corresponds to acetowhite area"
}
}
}
response = requests.post(
f"{BASE_URL}/api/generate-report",
json=payload
)
print(f"Status Code: {response.status_code}")
if response.status_code == 200:
result = response.json()
print(f"Status: {result.get('status')}")
print(f"Model: {result.get('model')}")
print(f"\nGenerated Report:\n{'-' * 60}")
print(result.get('report'))
print('-' * 60)
else:
print(f"Error: {response.json()}")
print()
def main():
"""Run all tests"""
print("\n" + "=" * 60)
print("PATHORA COLPOSCOPY API TEST SUITE")
print("=" * 60 + "\n")
# Test health check
test_health_check()
# Test AI model endpoints (you'll need to provide actual image paths)
# Uncomment and add your image paths:
# test_acetowhite_detection("path/to/your/image.jpg")
# test_cervix_detection("path/to/your/image.jpg")
# test_batch_inference(["image1.jpg", "image2.jpg"])
# Test LLM endpoints
test_chat()
test_chat_with_history()
test_report_generation()
print("\n" + "=" * 60)
print("ALL TESTS COMPLETED")
print("=" * 60 + "\n")
if __name__ == "__main__":
# Check if requests is installed
try:
import requests
except ImportError:
print("Please install requests: pip install requests")
exit(1)
main()
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