File size: 4,406 Bytes
b10b0ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
import requests
import base64
import json
def test_gradio_api():
"""Test the Gradio /api/predict endpoint"""
# You would replace this with actual base64 encoded image data
sample_image_path = "test_image.jpg" # Replace with your test image
try:
with open(sample_image_path, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
except FileNotFoundError:
print("Please add a test_image.jpg file to test the API")
return
url = "http://localhost:7860/api/predict"
payload = {
"data": [
image_base64,
"mobilenet",
0.5,
0.4
]
}
response = requests.post(url, json=payload)
if response.status_code == 200:
result = response.json()
print("Success!")
print(f"API Response: {json.dumps(result, indent=2)}")
# Extract the actual detection data
if "data" in result and len(result["data"]) > 0:
detection_data = result["data"][0]
if "faces" in detection_data:
print(f"Detected {len(detection_data['faces'])} faces")
print(f"Processing time: {detection_data.get('processing_time', 'N/A'):.3f} seconds")
print(f"Model used: {detection_data.get('model_used', 'N/A')}")
for i, face in enumerate(detection_data['faces']):
print(f"Face {i+1}:")
print(f" Confidence: {face['confidence']:.3f}")
print(f" Bounding box: {face['bbox']}")
print(f" Landmarks: {face['landmarks']}")
else:
print("No face detection data in response")
else:
print("Unexpected response format")
else:
print(f"Error: {response.status_code}")
print(response.text)
def test_health_check():
"""Test the Gradio app health"""
url = "http://localhost:7860/"
response = requests.get(url)
if response.status_code == 200:
print("Gradio app is running!")
print("You can access the web interface at: http://localhost:7860")
else:
print(f"Health check failed: {response.status_code}")
def test_direct_api_call():
"""Test direct API call format that Thunkable would use"""
sample_image_path = "test_image.jpg" # Replace with your test image
try:
with open(sample_image_path, "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
except FileNotFoundError:
print("Please add a test_image.jpg file to test the API")
return
url = "http://localhost:7860/api/predict"
# This is the format Thunkable will use
payload = {
"data": [image_base64, "mobilenet", 0.5, 0.4]
}
headers = {
"Content-Type": "application/json"
}
print("Testing Thunkable-compatible API call...")
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
result = response.json()
print("✅ Thunkable API call successful!")
# Parse the response as Thunkable would
if "data" in result and result["data"]:
detection_result = result["data"][0]
print(f"Faces detected: {detection_result.get('total_faces', 0)}")
print(f"Model used: {detection_result.get('model_used', 'unknown')}")
print(f"Processing time: {detection_result.get('processing_time', 0):.3f}s")
else:
print("❌ Unexpected response format")
else:
print(f"❌ API call failed: {response.status_code}")
print(response.text)
if __name__ == "__main__":
print("Testing RetinaFace Gradio API...")
print("=" * 50)
print("\n1. Health Check:")
test_health_check()
print("\n2. Gradio API Test:")
test_gradio_api()
print("\n3. Thunkable-Compatible API Test:")
test_direct_api_call()
print("\n" + "=" * 50)
print("Testing complete!")
print("\nFor Thunkable integration:")
print("- Use URL: http://localhost:7860/api/predict")
print("- Method: POST")
print("- Body format: {\"data\": [\"base64_image\", \"mobilenet\", 0.5, 0.4]}")
print("- Response will be in: response.data[0]")
|