Create app.py

app.py

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+import gradio as gr
+import face_recognition
+import cv2
+import numpy as np
+import pickle
+
+# Load the known face encodings and their IDs
+with open('EncodeFile.p', 'rb') as file:
+    encodeListKnownWithIds = pickle.load(file)
+encodeListKnown, studentsIds = encodeListKnownWithIds
+
+def recognize_face(input_image):
+    # Convert the image to RGB
+    img = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
+    
+ 
+    facesCurFrame = face_recognition.face_locations(img)
+    encodesCurFrame = face_recognition.face_encodings(img, facesCurFrame)
+
+    names = []
+    for encodeFace in encodesCurFrame:
+        # Compare faces and get the distance
+        matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
+        faceDist = face_recognition.face_distance(encodeListKnown, encodeFace)
+        
+        matchIndex = np.argmin(faceDist)
+        if matches[matchIndex]:
+            name = studentsIds[matchIndex]
+            names.append(name)
+        else:
+            names.append("Unknown")
+    
+
+    for (top, right, bottom, left), name in zip(facesCurFrame, names):
+        cv2.rectangle(input_image, (left, top), (right, bottom), (0, 0, 255), 2)
+        cv2.putText(input_image, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 1.0, (255, 255, 255), 1)
+    
+    return input_image
+
+# Create a Gradio interface
+iface = gr.Interface(fn=recognize_face,
+                     inputs=gr.inputs.Image(shape=(480, 360)),
+                     outputs="image",
+                     title="Face Recognition Attendance System",
+                     description="Upload an image to identify registered students.")
+
+if __name__ == "__main__":
+    iface.launch()