Update app.py

app.py

@@ -63,34 +63,35 @@ def create_user(email, password):
         return False, None
         
 # Update load_and_encode function to return encodings for all detected faces
-def load_and_encode(image_paths):
-    encodings_list = []
-    for image_path in image_paths:
-        try:
-            # Read the uploaded file as bytes
-            image_bytes = image_path.read()
-
-            # Convert the bytes to a NumPy array
-            nparr = np.frombuffer(image_bytes, np.uint8)
-
-            # Decode the NumPy array as an image
-            image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-            aligned_faces = detect_and_align_faces(image)
-
-            if aligned_faces is not None:
-                encodings = []
-                for aligned_face in aligned_faces:
-                    encoding = face_recognition.face_encodings(aligned_face)
-                    if encoding:
-                        encodings.append(encoding[0])
-                
-                if encodings:
-                    encodings_list.append(encodings)
-        except Exception as e:
-            print(f"Error loading and encoding image: {str(e)}")
-
-    return encodings_list
+def load_and_encode(image_file):
+    try:
+        # Read the uploaded file as bytes
+        image_bytes = image_file.read()
+
+        # Convert the bytes to a NumPy array
+        nparr = np.frombuffer(image_bytes, np.uint8)
+
+        # Decode the NumPy array as an image
+        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+        aligned_faces = detect_and_align_faces(image)
+
+        if aligned_faces is not None:
+            encodings = []
+            for aligned_face in aligned_faces:
+                encoding = face_recognition.face_encodings(aligned_face)
+                if encoding:
+                    encodings.append(encoding[0])
+            
+            if encodings:
+                return encodings
+            else:
+                return None
+        else:
+            return None
+    except Exception as e:
+        print(f"Error loading and encoding image: {str(e)}")
+        return None
 
 # Modify detect_and_align_faces function to detect and align multiple faces
 def detect_and_align_faces(image):
@@ -118,18 +119,18 @@ def detect_and_align_faces(image):
     return aligned_faces
 
 # Add person to database
-def add_person(name, image_paths, instagram_handle, email=None):
+def add_person(name, image_path, instagram_handle, email=None):
     try:
-        encodings = []
-        for image_path in image_paths:
-            encoding = load_and_encode(image_path)
-            if not encoding:
-                return f"No face found in the image: {image_path}"
-            encodings.append(encoding[0].tolist())
+        encoding = load_and_encode(image_path)
+        if not encoding:
+            return "No face found in the provided image."
+
+        # Convert NumPy arrays to lists for JSON serialization
+        encoding = encoding[0].tolist()
 
         # Save data to Firebase Realtime Database
         person_data = {
-            "encodings": encodings,
+            "encoding": encoding,
             "info": {
                 "instagram_handle": instagram_handle,
                 "instagram_link": f"https://www.instagram.com/{instagram_handle}/"
@@ -146,22 +147,24 @@ def add_person(name, image_paths, instagram_handle, email=None):
         return f"Failed to add person: {str(e)}"
 
 # Update recognize_face function to handle multiple face encodings
-def recognize_face(encodings_list):
-    if not encodings_list:
-        return "Please upload images."
+def recognize_face(image_path):
+    if not image_path:
+        return "Please upload an image."
 
     try:
+        unknown_encodings = load_and_encode(image_path)
+        if not unknown_encodings:
+            return "No face found in the provided image."
+
         matches = []
-        for unknown_encodings in encodings_list:
+        for unknown_encoding in unknown_encodings:
             face_matches = []
             for name, data in ref.get().items():
-                known_encodings = data["encodings"]
-                for known_encoding in known_encodings:
-                    for unknown_encoding in unknown_encodings:
-                        if face_recognition.compare_faces([known_encoding], unknown_encoding)[0]:
-                            info = data["info"]
-                            email = info.get("email", "Email not provided")
-                            face_matches.append((name, info["instagram_handle"], email))
+                known_encoding = np.array(data["encoding"])
+                if face_recognition.compare_faces([known_encoding], unknown_encoding)[0]:
+                    info = data["info"]
+                    email = info.get("email", "Email not provided")
+                    face_matches.append((name, info["instagram_handle"], email))
 
             if face_matches:
                 matches.extend(face_matches)
@@ -245,27 +248,23 @@ def send_feedback(feedback_data):
 def add_person_ui():
     st.title("😎 Add Person")
     name = st.text_input("Enter Name", help="Enter the name of the person")
-    image_paths = st.file_uploader("Upload Image(s)", accept_multiple_files=True, help="Upload 1 to 3 images containing the person's face")
+    image_path = st.file_uploader("Upload Image", help="Upload an image containing the person's face")
     email = st.text_input("Enter Email (Optional)", help="Enter the person's email address (optional)")
     instagram_handle = st.text_input("Enter Instagram Handle", help="Enter the person's Instagram handle")
     if st.button("Add Person"):
-        if not name or not image_paths or not instagram_handle:
+        if not name or not image_path or not instagram_handle:
             st.error("Please fill all the required fields.")
         else:
-            result = add_person(name, image_paths, instagram_handle, email)
+            result = add_person(name, image_path, instagram_handle, email)
             st.success(result)
 
 # Streamlit interface for recognizing face
 def recognize_face_ui():
     st.title("🔍 Recognize Face")
-    image_paths = st.file_uploader("Upload Image(s)", accept_multiple_files=True, help="Upload image(s) for face recognition")
+    image_path = st.file_uploader("Upload Image", help="Upload an image for face recognition")
     if st.button("Recognize Face"):
-        if not image_paths:
-            st.error("Please upload at least one image.")
-        else:
-            result = recognize_face(image_paths)
-            st.write(result, unsafe_allow_html=True)
-
+        result = recognize_face(image_path)
+        st.write(result, unsafe_allow_html=True)
 
 def recognize_face_optimal_ui():
     st.title("🔍 Recognize Face (Optimal)")
@@ -540,5 +539,4 @@ def main():
         
 # Run the tour guide
 if __name__ == "__main__":
-    authenticate_user_ui()
-    
+    authenticate_user_ui()