Update app.py

app.py

@@ -63,35 +63,34 @@ 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_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
+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
 
 # Modify detect_and_align_faces function to detect and align multiple faces
 def detect_and_align_faces(image):
@@ -147,24 +146,22 @@ def add_person(name, image_path, instagram_handle, email=None):
         return f"Failed to add person: {str(e)}"
 
 # Update recognize_face function to handle multiple face encodings
-def recognize_face(image_path):
-    if not image_path:
-        return "Please upload an image."
+def recognize_face(encodings_list):
+    if not encodings_list:
+        return "Please upload images."
 
     try:
-        unknown_encodings = load_and_encode(image_path)
-        if not unknown_encodings:
-            return "No face found in the provided image."
-
         matches = []
-        for unknown_encoding in unknown_encodings:
+        for unknown_encodings in encodings_list:
             face_matches = []
             for name, data in ref.get().items():
-                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))
+                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))
 
             if face_matches:
                 matches.extend(face_matches)
@@ -261,10 +258,14 @@ def add_person_ui():
 # Streamlit interface for recognizing face
 def recognize_face_ui():
     st.title("🔍 Recognize Face")
-    image_path = st.file_uploader("Upload Image", help="Upload an image for face recognition")
+    image_paths = st.file_uploader("Upload Image(s)", accept_multiple_files=True, help="Upload image(s) for face recognition")
     if st.button("Recognize Face"):
-        result = recognize_face(image_path)
-        st.write(result, unsafe_allow_html=True)
+        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)
+
 
 def recognize_face_optimal_ui():
     st.title("🔍 Recognize Face (Optimal)")