Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import face_recognition
+from PIL import Image
+
+# Simulated database of student details
+students_db = {
+    "student1": "Details for Student 1",
+    "student2": "Details for Student 2",
+}
+
+def load_image(image_file):
+    img = Image.open(image_file)
+    return np.array(img)
+
+def recognize_face(image):
+    # Find all face locations and encodings in the image
+    face_locations = face_recognition.face_locations(image)
+    face_encodings = face_recognition.face_encodings(image, face_locations)
+
+    recognized_students = []
+    for face_encoding in face_encodings:
+        # Compare with known faces (placeholder)
+        # In a real app, you'd load known face encodings from the database
+        # Here, it's just a mock example
+        matches = [True]  # Replace this with actual matching logic
+        if True in matches:
+            recognized_students.append("student1")  # Replace with actual name
+
+    return recognized_students
+
+st.title("Student Face Recognition")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
+if uploaded_file is not None:
+    # Load and display the image
+    image = load_image(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    
+    # Recognize faces
+    recognized_students = recognize_face(image)
+    
+    if recognized_students:
+        for student in recognized_students:
+            st.write(students_db.get(student, "Student not found"))
+    else:
+        st.write("No students recognized.")