Add initial project files

.gitignore

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+# Firebase credentials
+serviceAccountKey.json
+
+# Encoded face data
+EncodeFile.p
+
+# Temporary files
+*.pyc
+__pycache__/
+.tmp/
+
+# Dataset directories (if they contain sensitive or large data)
+Resources/
+Images/
+
+# Operating system files
+.DS_Store
+Thumbs.db
+
+# Editor/IDE files
+.vscode/
+.idea/
+
+# Python virtual environment
+venv/
+env/
+
+# Jupyter notebook checkpoints
+.ipynb_checkpoints/
+
+# Gradio cached examples
+gradio_cached_examples/
+
+# Files and directories generated during execution (replace with your specific paths if they differ)
+output/
+*.log
+captures/
+
+# If using SQLite or other local DBs
+*.db
+*.sqlite
+*.sqlite3
+
+# Any additional directories you have that contain generated images or mode images
+Modes/

app.py

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+import gradio as gr
+import face_recognition
+import cv2
+import numpy as np
+import os
+from PIL import Image
+import io
+import firebase_admin
+from firebase_admin import credentials
+from firebase_admin import db
+from firebase_admin import storage
+from datetime import datetime
+
+# Initialize Firebase
+cred = credentials.Certificate("path/to/serviceAccountKey.json") # Update the path to your Firebase credentials
+firebase_admin.initialize_app(cred, {
+    'databaseURL': 'https://faceantendancerealtime-default-rtdb.firebaseio.com/',
+    'storageBucket': 'faceantendancerealtime.appspot.com'
+})
+bucket = storage.bucket()
+
+# Load the known face encodings and their IDs from Firebase
+def load_known_encodings():
+    # Code to download the 'EncodeFile.p' from Firebase Storage
+    # Assume 'EncodeFile.p' is already uploaded to Firebase Storage
+    blob = bucket.blob('EncodeFile.p')
+    blob.download_to_filename('/tmp/EncodeFile.p')
+    with open('/tmp/EncodeFile.p', 'rb') as file:
+        encodeListKnownWithIds = pickle.load(file)
+    return encodeListKnownWithIds
+
+encodeListKnownWithIds = load_known_encodings()
+encodeListKnown, studentsIds = encodeListKnownWithIds
+
+def recognize_face(input_image):
+    # Convert the PIL Image to a numpy array
+    img = np.array(input_image)
+    
+    # Convert the image to RGB
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    
+    # Resize image for faster processing
+    img_small = cv2.resize(img, (0, 0), None, 0.25, 0.25)
+    
+    # Find faces in the image
+    face_locations = face_recognition.face_locations(img_small)
+    face_encodings = face_recognition.face_encodings(img_small, face_locations)
+    
+    # Convert the coordinates to full scale since the image was scaled to 1/4 size
+    face_locations = [(top*4, right*4, bottom*4, left*4) for top, right, bottom, left in face_locations]
+    
+    # Recognize faces
+    for face_encoding, (top, right, bottom, left) in zip(face_encodings, face_locations):
+        matches = face_recognition.compare_faces(encodeListKnown, face_encoding)
+        name = "Unknown"
+        
+        # Use the known face with the smallest distance to the new face
+        face_distances = face_recognition.face_distance(encodeListKnown, face_encoding)
+        best_match_index = np.argmin(face_distances)
+        if matches[best_match_index]:
+            name = studentsIds[best_match_index]
+        
+        # Draw rectangles and names on the original image
+        cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
+        cv2.putText(img, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
+    
+    # Convert the image back to PIL format
+    return Image.fromarray(img)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=recognize_face,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Image(type="pil"),
+    title="Face Recognition Attendance System",
+    description="Upload an image to identify registered students."
+)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt.txt

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+gradio
+face_recognition
+opencv-python-headless
+numpy
+Pillow
+firebase-admin