changes so far

grpc_server.py

@@ -1,13 +1,14 @@
+import tempfile
 import grpc
+import sys
 from concurrent import futures
-import time
 import os
 from deepface import DeepFace
-import pandas as pd
 
-import face_recognition_pb2
-import face_recognition_pb2_grpc
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "proto"))
 
+from proto import face_recognition_pb2
+from proto import face_recognition_pb2_grpc
 
 EMPLOYEE_DB_PATH = "data/employees"
 
@@ -15,24 +16,22 @@ EMPLOYEE_DB_PATH = "data/employees"
 class FaceRecognitionServicer(face_recognition_pb2_grpc.FaceRecognitionServiceServicer):
 
     def Recognize(self, request, context):
-        print("[INFO] Received request...")
-
-        # Save temp image
-        temp_path = "temp_upload.jpg"
-        with open(temp_path, "wb") as f:
-            f.write(request.image)
-
+        print("[INFO] Received recognition request...")
+        temp_file = None
         try:
-            print("[INFO] Running DeepFace search...")
+            # Save incoming image to a temporary file
+            with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
+                temp_file = tmp.name
+                tmp.write(request.image)
 
+            print("[INFO] Running DeepFace search...")
             result = DeepFace.find(
-                img_path=temp_path,
+                img_path=temp_file,
                 db_path=EMPLOYEE_DB_PATH,
+                detector_backend="opencv",
                 enforce_detection=False
             )
 
-            # result is a list of pandas DataFrames for each model;
-            # we only use first result
             if isinstance(result, list):
                 result = result[0]
 
@@ -40,35 +39,90 @@ class FaceRecognitionServicer(face_recognition_pb2_grpc.FaceRecognitionServiceSe
                 print("[INFO] No match found.")
                 return face_recognition_pb2.FaceResponse(
                     name="Unknown",
-                    confidence=0.0
+                    confidence=0.0,
+                    image=b""
                 )
 
-            # Pick best match
             best_row = result.iloc[0]
             matched_image_path = best_row["identity"]
             name = os.path.splitext(os.path.basename(matched_image_path))[0]
 
-            # DeepFace gives "distance" values, convert roughly to confidence
+            # Convert distance to rough confidence
             distance = best_row.get("VGG-Face_cosine", 0.3)
             confidence = float(max(0.0, 1.0 - distance))
 
-            print(f"[INFO] Match: {name}, confidence: {confidence:.2f}")
+            # Read matched image bytes
+            with open(matched_image_path, "rb") as f:
+                matched_image_bytes = f.read()
 
+            print(f"[INFO] Match: {name}, confidence: {confidence:.2f}")
             return face_recognition_pb2.FaceResponse(
                 name=name,
-                confidence=confidence
+                confidence=confidence,
+                image=matched_image_bytes
             )
 
         except Exception as e:
             print("[ERROR] DeepFace failed:", e)
             return face_recognition_pb2.FaceResponse(
                 name="Error",
-                confidence=0.0
+                confidence=0.0,
+                image=b""
             )
 
         finally:
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
+            if temp_file and os.path.exists(temp_file):
+                try:
+                    os.remove(temp_file)
+                except Exception as cleanup_error:
+                    print(f"[WARN] Could not delete temp file {temp_file}: {cleanup_error}")
+
+    def GetAllEmployees(self, request, context):
+        try:
+            employee_files = [
+                f for f in os.listdir(EMPLOYEE_DB_PATH)
+                if f.lower().endswith((".jpg", ".png", ".jpeg"))
+            ]
+
+            employees = []
+            for file in employee_files:
+                path = os.path.join(EMPLOYEE_DB_PATH, file)
+                name = os.path.splitext(file)[0]
+
+                with open(path, "rb") as f:
+                    image_bytes = f.read()
+
+                employees.append(face_recognition_pb2.Employee(
+                    name=name,
+                    image=image_bytes
+                ))
+
+            return face_recognition_pb2.EmployeeListResponse(
+                employees=employees
+            )
+        except Exception as e:
+            print("[ERROR] Failed to list employees:", e)
+            return face_recognition_pb2.EmployeeListResponse(employees=[])
+
+    def DeleteEmployee(self, request, context):
+        try:
+            found = False
+            for file in os.listdir(EMPLOYEE_DB_PATH):
+                if os.path.splitext(file)[0] == request.name:
+                    os.remove(os.path.join(EMPLOYEE_DB_PATH, file))
+                    found = True
+                    break
+            message = "Deleted successfully" if found else "Employee not found"
+            return face_recognition_pb2.DeleteEmployeeResponse(
+                success=found,
+                message=message
+            )
+        except Exception as e:
+            print("[ERROR] Failed to delete employee:", e)
+            return face_recognition_pb2.DeleteEmployeeResponse(
+                success=False,
+                message=str(e)
+            )
 
 
 def serve():
@@ -84,4 +138,5 @@ def serve():
 
 
 if __name__ == "__main__":
+    os.makedirs(EMPLOYEE_DB_PATH, exist_ok=True)
     serve()

proto/face_recognition.proto

@@ -7,13 +7,51 @@ message FaceRequest {
   bytes image = 1;
 }
 
-// The response message containing recognition results
+// The response message containing recognition results, now includes image
 message FaceResponse {
   string name = 1;
   float confidence = 2;
+  bytes image = 3; // newly added: return matched employee image
+}
+
+// New messages for enrollment
+message EnrollFaceRequest {
+  bytes image = 1;
+  string name = 2;
+}
+
+message EnrollFaceResponse {
+  bool success = 1;
+  string message = 2;
+}
+
+// Messages for getting all employees
+message Employee {
+  string name = 1;
+  bytes image = 2; // enrolled photo
+}
+
+message EmployeeListRequest {}
+
+message EmployeeListResponse {
+  repeated Employee employees = 1;
+}
+
+// Messages for deleting an employee
+message DeleteRequest {
+  string name = 1;
+}
+
+message DeleteResponse {
+  bool success = 1;
 }
 
 // The Face Recognition Service
 service FaceRecognitionService {
   rpc Recognize(FaceRequest) returns (FaceResponse);
+  rpc EnrollFace(EnrollFaceRequest) returns (EnrollFaceResponse);
+
+  // Updated RPC to return employee name + photo
+  rpc GetAllEmployees(EmployeeListRequest) returns (EmployeeListResponse);
+  rpc DeleteEmployee(DeleteRequest) returns (DeleteResponse);
 }