app/Hackathon_setup/test_api_simple.py

"""
Simple API test script that extracts the numerical score
"""

import requests
import base64
import numpy as np
import cv2
from PIL import Image
import io
import re

def create_face_image():
    """Create a simple face-like image"""
    img = np.zeros((100, 100), dtype=np.uint8)
    
    # Face outline
    cv2.ellipse(img, (50, 50), (40, 50), 0, 0, 360, 100, -1)
    
    # Eyes
    cv2.circle(img, (35, 40), 5, 200, -1)
    cv2.circle(img, (65, 40), 5, 200, -1)
    
    # Nose
    cv2.line(img, (50, 45), (50, 60), 150, 2)
    
    # Mouth
    cv2.ellipse(img, (50, 70), (15, 8), 0, 0, 180, 150, 2)
    
    # Convert to RGB
    img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
    return img_rgb

def test_api():
    """Test the API and extract the score"""
    url = "https://pavaniyerra-hackthon4.hf.space/predict_similarity/"
    
    print("Testing Face Similarity API")
    print("=" * 40)
    
    try:
        # Create two test face images
        face1 = create_face_image()
        face2 = create_face_image()
        
        # Convert to bytes
        def img_to_bytes(img):
            pil_img = Image.fromarray(img)
            buffer = io.BytesIO()
            pil_img.save(buffer, format='JPEG')
            return buffer.getvalue()
        
        face1_bytes = img_to_bytes(face1)
        face2_bytes = img_to_bytes(face2)
        
        # Prepare files for upload
        files = {
            'file1': ('face1.jpg', face1_bytes, 'image/jpeg'),
            'file2': ('face2.jpg', face2_bytes, 'image/jpeg')
        }
        
        print("Sending request to API...")
        response = requests.post(url, files=files, timeout=30)
        
        print(f"Status Code: {response.status_code}")
        
        if response.status_code == 200:
            print("SUCCESS! API is working")
            
            # Extract the dissimilarity score from HTML
            html_content = response.text
            
            # Look for the dissimilarity score in the HTML
            # Pattern: "Dissimilarity: X.X"
            pattern = r'Dissimilarity:</span>\s*<span[^>]*>\s*([0-9.]+)'
            match = re.search(pattern, html_content)
            
            if match:
                score = float(match.group(1))
                print(f"Dissimilarity Score: {score}")
                
                # Convert dissimilarity to similarity (assuming 1.0 = completely different, 0.0 = identical)
                similarity = 1.0 - score
                print(f"Similarity Score: {similarity:.4f}")
                
                # Interpret the result
                if similarity > 0.8:
                    print("Result: Very High Similarity (likely same person)")
                elif similarity > 0.6:
                    print("Result: High Similarity (possibly same person)")
                elif similarity > 0.4:
                    print("Result: Moderate Similarity (uncertain)")
                elif similarity > 0.2:
                    print("Result: Low Similarity (likely different persons)")
                else:
                    print("Result: Very Low Similarity (definitely different persons)")
            else:
                print("WARNING: Could not extract score from HTML response")
                print("HTML content preview:")
                print(html_content[:500] + "..." if len(html_content) > 500 else html_content)
        else:
            print(f"ERROR: {response.status_code}")
            print(f"Response: {response.text}")
            
    except Exception as e:
        print(f"ERROR: {e}")

def test_multiple_times():
    """Test the API multiple times to check consistency"""
    print("\n" + "=" * 40)
    print("Testing API Multiple Times")
    print("=" * 40)
    
    scores = []
    for i in range(3):
        print(f"\nTest {i+1}/3:")
        try:
            face1 = create_face_image()
            face2 = create_face_image()
            
            def img_to_bytes(img):
                pil_img = Image.fromarray(img)
                buffer = io.BytesIO()
                pil_img.save(buffer, format='JPEG')
                return buffer.getvalue()
            
            files = {
                'file1': ('face1.jpg', img_to_bytes(face1), 'image/jpeg'),
                'file2': ('face2.jpg', img_to_bytes(face2), 'image/jpeg')
            }
            
            response = requests.post("https://pavaniyerra-hackthon4.hf.space/predict_similarity/", 
                                   files=files, timeout=30)
            
            if response.status_code == 200:
                # Extract score
                pattern = r'Dissimilarity:</span>\s*<span[^>]*>\s*([0-9.]+)'
                match = re.search(pattern, response.text)
                if match:
                    score = float(match.group(1))
                    scores.append(score)
                    print(f"  Score: {score}")
                else:
                    print("  Could not extract score")
            else:
                print(f"  Error: {response.status_code}")
                
        except Exception as e:
            print(f"  Error: {e}")
    
    if scores:
        print(f"\nScore Statistics:")
        print(f"  Average: {sum(scores)/len(scores):.4f}")
        print(f"  Min: {min(scores):.4f}")
        print(f"  Max: {max(scores):.4f}")
        print(f"  Range: {max(scores) - min(scores):.4f}")

if __name__ == "__main__":
    # Test the API
    test_api()
    
    # Test multiple times for consistency
    test_multiple_times()
    
    print("\n" + "=" * 50)
    print("API Testing Complete!")
    print("\nYour API is working correctly!")
    print("The API expects:")
    print("- Method: POST")
    print("- Format: multipart/form-data")
    print("- Parameters: file1, file2 (image files)")
    print("- Response: HTML with dissimilarity score")