Changes Made Keys p3

app.py

@@ -2,81 +2,63 @@ import gradio as gr
 import face_recognition
 import cv2
 import numpy as np
-import os
 from PIL import Image
 import pickle
-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("serviceAccountKey.json") # Update the path to your Firebase credentials
+cred = credentials.Certificate("serviceAccountKey.json")  # Update with your credentials path
 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
+# Function to download face encodings from Firebase Storage
+def download_encodings():
     blob = bucket.blob('EncodeFile.p')
-    blob.download_to_filename('/tmp/EncodeFile.p')
+    blob.download_to_filename('EncodeFile.p')
     with open('EncodeFile.p', 'rb') as file:
-        encodeListKnownWithIds = pickle.load(file)
-    return encodeListKnownWithIds
+        return pickle.load(file)
 
-encodeListKnownWithIds = load_known_encodings()
+encodeListKnownWithIds = download_encodings()
 encodeListKnown, studentsIds = encodeListKnownWithIds
 
 def recognize_face(input_image):
-    # Convert the PIL Image to a numpy array
+    # Convert PIL Image to 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]
-    
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    # Detect faces and encode
+    face_locations = face_recognition.face_locations(img)
+    face_encodings = face_recognition.face_encodings(img, face_locations)
+
     # Recognize faces
-    for face_encoding, (top, right, bottom, left) in zip(face_encodings, face_locations):
+    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
         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
+
+        # Draw rectangle and label
         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
+    # Convert back to PIL Image
+    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+# Gradio interface
 iface = gr.Interface(
     fn=recognize_face,
-    inputs=gr.inputs.Image(type="pil"),
-    outputs=gr.outputs.Image(type="pil"),
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
     title="Face Recognition Attendance System",
-    description="Upload an image to identify registered students."
+    description="Upload an image to identify individuals."
 )
 
-# Run the Gradio app
 if __name__ == "__main__":
     iface.launch(inline=False)