Create app.py

app.py

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+import cv2
+import face_recognition
+import numpy as np
+import gradio as gr
+
+# Load reference image and encode face
+reference_image = face_recognition.load_image_file("reference.jpg")
+reference_encoding = face_recognition.face_encodings(reference_image)[0]
+
+def detect_and_recognize(image_path):
+    # Load input image
+    image = face_recognition.load_image_file(image_path)
+    
+    # Detect faces
+    face_locations = face_recognition.face_locations(image)
+    face_encodings = face_recognition.face_encodings(image, face_locations)
+
+    # Convert image for OpenCV visualization
+    image_cv2 = cv2.imread(image_path)
+    
+    recognized_faces = []
+
+    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+        # Compare detected face with reference
+        matches = face_recognition.compare_faces([reference_encoding], face_encoding)
+        label = "Match" if matches[0] else "Unknown"
+        
+        # Draw a rectangle around the face
+        cv2.rectangle(image_cv2, (left, top), (right, bottom), (0, 255, 0), 2)
+        cv2.putText(image_cv2, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+        recognized_faces.append(label)
+    
+    output_path = "output.jpg"
+    cv2.imwrite(output_path, image_cv2)
+
+    return output_path, recognized_faces
+
+# Gradio Interface
+iface = gr.Interface(fn=detect_and_recognize, inputs="file", outputs=["image", "text"], title="Face Recognition")
+iface.launch()