Create app.py

app.py

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+import numpy as np
+import gradio as gr
+import cv2
+from insightface.app import FaceAnalysis
+
+# Initialize InsightFace
+app = FaceAnalysis(name='buffalo_s')
+app.prepare(ctx_id=0, det_size=(640, 640))  # Use GPU if available and onnxruntime-gpu is installed, otherwise CPU
+
+def compute_similarity(img1, img2):
+    # Convert RGB to BGR (InsightFace expects BGR format)
+    img1_bgr = cv2.cvtColor(img1, cv2.COLOR_RGB2BGR)
+    img2_bgr = cv2.cvtColor(img2, cv2.COLOR_RGB2BGR)
+    
+    # Get faces and embeddings
+    faces1 = app.get(img1_bgr)
+    faces2 = app.get(img2_bgr)
+    
+    # Check for no faces detected
+    if len(faces1) == 0 or len(faces2) == 0:
+        return "No faces detected in one or both images."
+    
+    # Check for multiple faces
+    if len(faces1) > 1 or len(faces2) > 1:
+        return "Multiple faces detected in one or both images. Please upload images with single faces."
+    
+    # Extract embeddings
+    embedding1 = faces1[0].embedding
+    embedding2 = faces2[0].embedding
+    
+    # Compute cosine similarity
+    similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
+    
+    # Interpret result
+    threshold = 0.5
+    if similarity > threshold:
+        result = "Same person"
+    else:
+        result = "Person mismatch"
+    
+    return f"Similarity Score: {similarity:.4f}\nResult: {result}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=compute_similarity,
+    inputs=[gr.Image(type="numpy", label="Image 1"), gr.Image(type="numpy", label="Image 2")],
+    outputs="text",
+    title="Face Similarity Checker",
+    description="Upload two images to check if they are of the same person."
+)
+
+# Launch the interface
+iface.launch()