Update app.py

app.py

@@ -76,16 +76,22 @@ def load_and_encode(image_file):
 
         aligned_faces = detect_and_align_faces(image)
 
-        encodings = []
-        for aligned_face in aligned_faces:
-            encoding = face_recognition.face_encodings(aligned_face)
-            if encoding:
-                encodings.append(encoding[0])
-
-        return encodings
+        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 []
+        return None
 
 # Modify detect_and_align_faces function to detect and align multiple faces
 def detect_and_align_faces(image):
@@ -111,20 +117,20 @@ def detect_and_align_faces(image):
         aligned_faces.append(aligned_face)
     
     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 {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}/"
@@ -154,11 +160,11 @@ def recognize_face(image_path):
         for unknown_encoding in unknown_encodings:
             face_matches = []
             for name, data in ref.get().items():
-                for known_encoding in data["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)
@@ -192,11 +198,11 @@ def recognize_face_optimal(image_path):
         for unknown_encoding in unknown_encodings:
             face_matches = []
             for name, data in ref.get().items():
-                for known_encoding in data["encodings"]:
-                    similarity_score = face_recognition.face_distance([known_encoding], unknown_encoding)[0]
-                    if similarity_score > 0.50:  # Only consider matches above 50.00% similarity
-                        continue
-                    face_matches.append((name, similarity_score))
+                known_encoding = np.array(data["encoding"])
+                similarity_score = face_recognition.face_distance([known_encoding], unknown_encoding)[0]
+                if similarity_score > 0.50:  # Only consider matches above 50.00% similarity
+                    continue
+                face_matches.append((name, similarity_score))
 
             if face_matches:
                 best_match = min(face_matches, key=lambda x: x[1])
@@ -238,19 +244,18 @@ def send_feedback(feedback_data):
     except Exception as e:
         st.error(f"Failed to submit feedback: {str(e)}")
 
+# Streamlit interface for adding a person
 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 Images", 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.")
-        elif len(image_paths) < 1 or len(image_paths) > 3:
-            st.error("Please upload 1 to 3 images.")
         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