Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+import face_recognition
+import os
+
+# Load images for face recognition
+Images = []
+classnames = []
+directory = "photos"
+
+myList = os.listdir(directory)
+
+for cls in myList:
+    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
+        img_path = os.path.join(directory, cls)
+        curImg = cv2.imread(img_path)
+        Images.append(curImg)
+        classnames.append(os.path.splitext(cls)[0])
+
+def findEncodings(Images):
+    encodeList = []
+    for img in Images:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        encode = face_recognition.face_encodings(img)[0]
+        encodeList.append(encode)
+    return encodeList
+
+encodeListknown = findEncodings(Images)
+
+# Function for face recognition
+def recognize_faces(img):
+    image = np.array(img)
+    imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
+    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+    facesCurFrame = face_recognition.face_locations(imgS)
+    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+
+    name = "Unknown"  # Default name for unknown faces
+
+    if len(encodesCurFrame) > 0:
+        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
+            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
+            matchIndex = np.argmin(faceDis)
+
+            if matches[matchIndex]:
+                name = classnames[matchIndex].upper()
+
+            y1, x2, y2, x1 = faceLoc
+            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
+            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+
+    return image
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=recognize_faces,
+    inputs="image",
+    outputs="image",
+    live=True,
+    capture_session=True,
+    interpretation="default",
+    title="Face Recognition App",
+    description="This app recognizes faces in an image and updates attendance."
+)
+
+iface.launch()