Spaces:
Sleeping
Sleeping
File size: 4,360 Bytes
2ca4976 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
"""
Test script for the deployed Hugging Face API
"""
import requests
import base64
import numpy as np
import cv2
from PIL import Image
import io
def create_test_image():
"""Create a test image for API testing"""
# Create a simple test image
img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
# Convert to PIL Image
pil_img = Image.fromarray(img)
# Convert to base64
buffer = io.BytesIO()
pil_img.save(buffer, format='JPEG')
img_str = base64.b64encode(buffer.getvalue()).decode()
return img_str
def test_api_similarity():
"""Test the similarity API endpoint"""
url = "https://pavaniyerra-hackthon4.hf.space/predict_similarity/"
print("Testing Hugging Face API...")
print("=" * 40)
try:
# Create two test images
img1_b64 = create_test_image()
img2_b64 = create_test_image()
# Prepare the request data - API expects file1 and file2
data = {
"file1": img1_b64,
"file2": img2_b64
}
print("Sending request to API...")
response = requests.post(url, json=data, timeout=30)
if response.status_code == 200:
result = response.json()
print("SUCCESS: API Response received successfully!")
print(f"Similarity Score: {result}")
# Interpret the similarity score
if isinstance(result, (int, float)):
if result > 0.8:
print("Result: Very High Similarity (likely same person)")
elif result > 0.6:
print("Result: High Similarity (possibly same person)")
elif result > 0.4:
print("Result: Moderate Similarity (uncertain)")
elif result > 0.2:
print("Result: Low Similarity (likely different persons)")
else:
print("Result: Very Low Similarity (definitely different persons)")
else:
print(f"Unexpected response format: {result}")
else:
print(f"ERROR: API Error: {response.status_code}")
print(f"Response: {response.text}")
except requests.exceptions.RequestException as e:
print(f"ERROR: Network Error: {e}")
except Exception as e:
print(f"ERROR: Error: {e}")
def test_api_classification():
"""Test the classification API endpoint (if available)"""
# Try different possible endpoints
possible_urls = [
"https://pavaniyerra-hackthon4.hf.space/predict_class/",
"https://pavaniyerra-hackthon4.hf.space/classify/",
"https://pavaniyerra-hackthon4.hf.space/predict/"
]
print("\nTesting Classification API...")
print("=" * 40)
for url in possible_urls:
try:
# Create a test image
img_b64 = create_test_image()
# Prepare the request data - try different parameter names
data = {
"file": img_b64
}
print(f"Trying endpoint: {url}")
response = requests.post(url, json=data, timeout=30)
if response.status_code == 200:
result = response.json()
print("SUCCESS: Classification API Response received successfully!")
print(f"Predicted Class: {result}")
return
else:
print(f"ERROR: {response.status_code} - {response.text[:100]}...")
except requests.exceptions.RequestException as e:
print(f"ERROR: Network Error for {url}: {e}")
except Exception as e:
print(f"ERROR: Error for {url}: {e}")
print("No working classification endpoint found.")
if __name__ == "__main__":
print("Hugging Face API Test")
print("=" * 50)
print(f"API URL: https://pavaniyerra-hackthon4.hf.space/predict_similarity/")
print()
# Test similarity API
test_api_similarity()
# Test classification API (if available)
test_api_classification()
print("\n" + "=" * 50)
print("API Testing Complete!")
print("\nNote: This test uses random images.")
print("For real testing, use actual face images.")
|