Merge branch 'main' into feat/face-recognition

ref: N25B-397

pyproject.toml

@@ -24,7 +24,6 @@ dependencies = [
   "sphinx-rtd-theme>=3.0.2",
   "tf-keras>=2.20.1",
   "torch>=2.8.0",
-  "tornado ; sys_platform == 'win32'",
   "uvicorn>=0.37.0",
 ]
 
@@ -41,7 +40,6 @@ dev = [
 ]
 test = [
   "agentspeak>=0.2.2",
-  "deepface>=0.0.97",
   "fastapi>=0.115.6",
   "httpx>=0.28.1",
   "mlx-whisper>=0.4.3 ; sys_platform == 'darwin'",
@@ -56,7 +54,6 @@ test = [
   "pyyaml>=6.0.3",
   "pyzmq>=27.1.0",
   "soundfile>=0.13.1",
-  "tf-keras>=2.20.1",
 ]
 
 [tool.pytest.ini_options]

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -4,7 +4,6 @@ University within the Software Project course.
 © Copyright Utrecht University (Department of Information and Computing Sciences)
 """
 
-import logging
 from functools import singledispatchmethod
 
 from slugify import slugify
@@ -31,6 +30,7 @@ from control_backend.schemas.program import (
     BasicNorm,
     ConditionalNorm,
     EmotionBelief,
+    FaceBelief,
     GestureAction,
     Goal,
     InferredBelief,
@@ -67,7 +67,6 @@ class AgentSpeakGenerator:
     """
 
     _asp: AstProgram
-    logger = logging.getLogger(__name__)
 
     def generate(self, program: Program) -> str:
         """
@@ -107,7 +106,7 @@ class AgentSpeakGenerator:
         check if a keyword is a substring of the user's message.
 
         The generated rule has the form:
-        keyword_said(Keyword) :- user_said(Message) & .substring_case_insensitive(Keyword, Message, Pos) & Pos >= 0
+        keyword_said(Keyword) :- user_said(Message) & .substring(Keyword, Message, Pos) & Pos >= 0
 
         This enables the system to trigger behaviors based on keyword detection.
         """
@@ -119,7 +118,7 @@ class AgentSpeakGenerator:
             AstRule(
                 AstLiteral("keyword_said", [keyword]),
                 AstLiteral("user_said", [message])
-                & AstLiteral(".substring_case_insensitive", [keyword, message, position])
+                & AstLiteral(".substring", [keyword, message, position])
                 & (position >= 0),
             )
         )
@@ -135,6 +134,7 @@ class AgentSpeakGenerator:
         """
         self._add_reply_with_goal_plan()
         self._add_say_plan()
+        self._add_reply_plan()
         self._add_notify_cycle_plan()
 
     def _add_reply_with_goal_plan(self):
@@ -198,6 +198,40 @@ class AgentSpeakGenerator:
             )
         )
 
+    def _add_reply_plan(self):
+        """
+        Adds a plan for general reply actions.
+
+        This plan handles general reply actions where the agent needs to respond
+        to user input without a specific conversational goal. It:
+        1. Marks that the agent has responded this turn
+        2. Gathers all active norms
+        3. Generates a reply based on the user message and norms
+
+        Trigger: +!reply
+        Context: user_said(Message)
+        """
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("reply"),
+                [AstLiteral("user_said", [AstVar("Message")])],
+                [
+                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral(
+                            "findall",
+                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
+                        ),
+                    ),
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral("reply", [AstVar("Message"), AstVar("Norms")]),
+                    ),
+                ],
+            )
+        )
 
     def _add_notify_cycle_plan(self):
         """
@@ -235,39 +269,6 @@ class AgentSpeakGenerator:
             )
         )
 
-    def _add_stop_plan(self, phase: Phase):
-        """
-        Adds a plan to stop the program. This just skips to the end phase,
-        where there is no behavior defined.
-        """
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("stop"),
-                [AstLiteral("phase", [AstString(phase.id)])],
-                [
-                    AstStatement(
-                        StatementType.DO_ACTION,
-                        AstLiteral(
-                            "notify_transition_phase",
-                            [
-                                AstString(phase.id),
-                                AstString("end")
-                            ]
-                        )
-                    ),
-                    AstStatement(
-                        StatementType.REMOVE_BELIEF,
-                        AstLiteral("phase", [AstVar("Phase")]),
-                    ),
-                    AstStatement(
-                        StatementType.ADD_BELIEF,
-                        AstLiteral("phase", [AstString("end")])
-                    )
-                ]
-            )
-        )
-
     def _process_phases(self, phases: list[Phase]) -> None:
         """
         Processes all phases in the program and their transitions.
@@ -284,6 +285,21 @@ class AgentSpeakGenerator:
                 self._process_phase(curr_phase)
             self._add_phase_transition(curr_phase, next_phase)
 
+        # End phase behavior
+        # When deleting this, the entire `reply` plan and action can be deleted
+        self._asp.plans.append(
+            AstPlan(
+                type=TriggerType.ADDED_BELIEF,
+                trigger_literal=AstLiteral("user_said", [AstVar("Message")]),
+                context=[AstLiteral("phase", [AstString("end")])],
+                body=[
+                    AstStatement(
+                        StatementType.DO_ACTION, AstLiteral("notify_user_said", [AstVar("Message")])
+                    ),
+                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply")),
+                ],
+            )
+        )
 
     def _process_phase(self, phase: Phase) -> None:
         """
@@ -310,9 +326,6 @@ class AgentSpeakGenerator:
         for trigger in phase.triggers:
             self._process_trigger(trigger, phase)
 
-        # Add force transition to end phase
-        self._add_stop_plan(phase)
-
     def _add_phase_transition(self, from_phase: Phase | None, to_phase: Phase | None) -> None:
         """
         Adds plans for transitioning between phases.
@@ -488,13 +501,9 @@ class AgentSpeakGenerator:
             if isinstance(step, Goal):
                 subgoals.append(step)
 
-        if not goal.can_fail:
+        if not goal.can_fail and not continues_response:
             body.append(AstStatement(StatementType.ADD_BELIEF, self._astify(goal, achieved=True)))
 
-        if len(body) == 0:
-            self.logger.warning("Goal with no plan detected: %s", goal.name)
-            body.append(AstStatement(StatementType.EMPTY, AstLiteral("true")))
-
         self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(goal), context, body))
 
         self._asp.plans.append(
@@ -555,10 +564,10 @@ class AgentSpeakGenerator:
             )
         )
         for step in trigger.plan.steps:
-            if isinstance(step, Goal):
-                new_step = step.model_copy(update={"can_fail": False})  # triggers are sequence
-                subgoals.append(new_step)
             body.append(self._step_to_statement(step))
+            if isinstance(step, Goal):
+                step.can_fail = False  # triggers are continuous sequence
+                subgoals.append(step)
 
         # Arbitrary wait for UI to display nicely
         body.append(
@@ -602,7 +611,6 @@ class AgentSpeakGenerator:
         - check_triggers: When no triggers are applicable
         - transition_phase: When phase transition conditions aren't met
         - force_transition_phase: When forced transitions aren't possible
-        - stop: When we are already in the end phase
         """
         # Trigger fallback
         self._asp.plans.append(
@@ -634,16 +642,6 @@ class AgentSpeakGenerator:
             )
         )
 
-        # Stop fallback
-        self._asp.plans.append(
-            AstPlan(
-                TriggerType.ADDED_GOAL,
-                AstLiteral("stop"),
-                [],
-                [AstStatement(StatementType.EMPTY, AstLiteral("true"))],
-            )
-        )
-
     @singledispatchmethod
     def _astify(self, element: ProgramElement) -> AstExpression:
         """
@@ -690,6 +688,10 @@ class AgentSpeakGenerator:
     def _(self, eb: EmotionBelief) -> AstExpression:
         return AstLiteral("emotion_detected", [AstAtom(eb.emotion)])
 
+    @_astify.register
+    def _(self, eb: FaceBelief) -> AstExpression:
+        return AstLiteral("face_present")
+
     @_astify.register
     def _(self, ib: InferredBelief) -> AstExpression:
         """

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -176,8 +176,6 @@ class BDICoreAgent(BaseAgent):
                         self._force_norm(msg.body)
                     case "force_next_phase":
                         self._force_next_phase()
-                    case "stop":
-                        self._stop()
                     case _:
                         self.logger.warning("Received unknown user interruption: %s", msg)
 
@@ -337,11 +335,6 @@ class BDICoreAgent(BaseAgent):
 
         self.logger.info("Manually forced phase transition.")
 
-    def _stop(self):
-        self._set_goal("stop")
-
-        self.logger.info("Stopped the program (skipped to end phase).")
-
     def _add_custom_actions(self) -> None:
         """
         Add any custom actions here. Inside `@self.actions.add()`, the first argument is
@@ -349,28 +342,6 @@ class BDICoreAgent(BaseAgent):
         the function expects (which will be located in `term.args`).
         """
 
-        @self.actions.add(".substring_case_insensitive", 3)
-        @agentspeak.optimizer.function_like
-        def _substring(agent, term, intention):
-            """
-            Find out if a string is a substring of another (case insensitive). Copied mostly from
-            the agentspeak library method .substring.
-            """
-            needle = agentspeak.asl_str(agentspeak.grounded(term.args[0], intention.scope)).lower()
-            haystack = agentspeak.asl_str(agentspeak.grounded(term.args[1], intention.scope)).lower()
-
-            choicepoint = object()
-
-            pos = haystack.find(needle)
-            while pos != -1:
-                intention.stack.append(choicepoint)
-
-                if agentspeak.unify(term.args[2], pos, intention.scope, intention.stack):
-                    yield
-
-                agentspeak.reroll(intention.scope, intention.stack, choicepoint)
-                pos = haystack.find(needle, pos + 1)
-
         @self.actions.add(".reply", 2)
         def _reply(agent, term, intention):
             """
@@ -496,6 +467,7 @@ class BDICoreAgent(BaseAgent):
                 body=str(trigger_name),
             )
 
+            # TODO: check with Pim
             self.add_behavior(self.send(msg))
 
             yield

src/control_backend/agents/communication/ri_communication_agent.py

@@ -19,7 +19,7 @@ from control_backend.agents.perception.visual_emotion_recognition_agent.visual_e
 from control_backend.core.config import settings
 
 from ..actuation.robot_speech_agent import RobotSpeechAgent
-from ..perception import VADAgent
+from ..perception import FacePerceptionAgent, VADAgent
 
 
 class RICommunicationAgent(BaseAgent):
@@ -181,7 +181,7 @@ class RICommunicationAgent(BaseAgent):
             bind = port_data["bind"]
 
             if not bind:
-                addr = f"tcp://{settings.ri_host}:{port}"
+                addr = f"tcp://localhost:{port}"
             else:
                 addr = f"tcp://*:{port}"
 
@@ -224,6 +224,13 @@ class RICommunicationAgent(BaseAgent):
                     )
                     self.visual_emotion_recognition_agent = visual_emotion_agent
                     await visual_emotion_agent.start()
+                case "face":
+                    face_agent = FacePerceptionAgent(
+                        settings.agent_settings.face_agent_name,
+                        zmq_address=addr,
+                        zmq_bind=bind,
+                    )
+                    await face_agent.start()
                 case _:
                     self.logger.warning("Unhandled negotiation id: %s", id)
 
@@ -328,7 +335,7 @@ class RICommunicationAgent(BaseAgent):
 
         if self.speech_agent is not None:
             await self.speech_agent.stop()
-        
+
         if self.visual_emotion_recognition_agent is not None:
             await self.visual_emotion_recognition_agent.stop()
 
@@ -338,4 +345,3 @@ class RICommunicationAgent(BaseAgent):
         self.logger.debug("Restarting communication negotiation.")
         if await self._negotiate_connection(max_retries=2):
             self.connected = True
-            

src/control_backend/agents/llm/llm_agent.py

@@ -185,9 +185,6 @@ class LLMAgent(BaseAgent):
             full_message = ""
             current_chunk = ""
             async for token in self._stream_query_llm(messages):
-                if self._interrupted:
-                    return
-
                 full_message += token
                 current_chunk += token
 

src/control_backend/agents/perception/__init__.py

@@ -7,6 +7,7 @@ Agents responsible for processing sensory input, such as audio transcription and
 detection.
 """
 
+from .face_rec_agent import FacePerceptionAgent as FacePerceptionAgent
 from .transcription_agent.transcription_agent import (
     TranscriptionAgent as TranscriptionAgent,
 )

src/control_backend/agents/perception/face_rec_agent.py

@@ -0,0 +1,144 @@
+"""
+This program has been developed by students from the bachelor Computer Science at Utrecht
+University within the Software Project course.
+© Copyright Utrecht University (Department of Information and Computing Sciences)
+"""
+
+import asyncio
+
+import zmq
+import zmq.asyncio as azmq
+
+from control_backend.agents import BaseAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+from control_backend.schemas.belief_message import Belief, BeliefMessage
+
+
+class FacePerceptionAgent(BaseAgent):
+    """
+    Receives face presence updates from the RICommunicationAgent
+    via the internal PUB/SUB bus.
+    """
+
+    def __init__(self, name: str, zmq_address: str, zmq_bind: bool):
+        """
+        :param name: The name of the agent.
+        :param zmq_address: The ZMQ address to subscribe to, an endpoint which sends face presence
+            updates.
+        :param zmq_bind: Whether to connect to the ZMQ endpoint, or to bind.
+        """
+        super().__init__(name)
+        self._zmq_address = zmq_address
+        self._zmq_bind = zmq_bind
+        self._socket: azmq.Socket | None = None
+
+        self._last_face_state: bool | None = None
+
+        # Pause functionality
+        # NOTE: flag is set when running, cleared when paused
+        self._paused = asyncio.Event()
+        self._paused.set()
+
+    async def setup(self):
+        self.logger.info("Starting FacePerceptionAgent")
+
+        if self._socket is None:
+            self._connect_socket()
+
+        self.add_behavior(self._poll_loop())
+        self.logger.info("Finished setting up %s", self.name)
+
+    def _connect_socket(self):
+        if self._socket is not None:
+            self.logger.warning("ZMQ socket already initialized. Did you call setup() twice?")
+            return
+
+        self._socket = azmq.Context.instance().socket(zmq.SUB)
+        self._socket.setsockopt_string(zmq.SUBSCRIBE, "")
+        if self._zmq_bind:
+            self._socket.bind(self._zmq_address)
+        else:
+            self._socket.connect(self._zmq_address)
+
+    async def _poll_loop(self):
+        if self._socket is None:
+            self.logger.warning("Connection not initialized before poll loop. Call setup() first.")
+            return
+
+        while self._running:
+            try:
+                await self._paused.wait()
+                response = await asyncio.wait_for(
+                    self._socket.recv_json(), timeout=settings.behaviour_settings.sleep_s
+                )
+
+                face_present = response.get("face_detected", False)
+
+                if self._last_face_state is None:
+                    self._last_face_state = face_present
+                    continue
+
+                if face_present != self._last_face_state:
+                    self._last_face_state = face_present
+                    self.logger.debug("Face detected" if face_present else "Face lost")
+                    await self._update_face_belief(face_present)
+            except TimeoutError:
+                pass
+            except Exception as e:
+                self.logger.error("Face polling failed", exc_info=e)
+
+    async def _post_face_belief(self, present: bool):
+        """
+        Send a face_present belief update to the BDI Core Agent.
+        """
+        if present:
+            belief_msg = BeliefMessage(create=[{"name": "face_present", "arguments": []}])
+        else:
+            belief_msg = BeliefMessage(delete=[{"name": "face_present", "arguments": []}])
+
+        msg = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            thread="beliefs",
+            body=belief_msg.model_dump_json(),
+        )
+
+        await self.send(msg)
+
+    async def _update_face_belief(self, present: bool):
+        """
+        Add or remove the `face_present` belief in the BDI Core Agent.
+        """
+        if present:
+            payload = BeliefMessage(create=[Belief(name="face_present").model_dump()])
+        else:
+            payload = BeliefMessage(delete=[Belief(name="face_present").model_dump()])
+
+        message = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            thread="beliefs",
+            body=payload.model_dump_json(),
+        )
+
+        await self.send(message)
+
+    async def handle_message(self, msg: InternalMessage):
+        """
+        Handle incoming pause/resume commands from User Interrupt Agent.
+        """
+        sender = msg.sender
+
+        if sender == settings.agent_settings.user_interrupt_name:
+            if msg.body == "PAUSE":
+                self.logger.info("Pausing Face Perception processing.")
+                self._paused.clear()
+                self._last_face_state = None
+            elif msg.body == "RESUME":
+                self.logger.info("Resuming Face Perception processing.")
+                self._paused.set()
+            else:
+                self.logger.warning("Unknown command from User Interrupt Agent: %s", msg.body)
+        else:
+            self.logger.debug("Ignoring message from unknown sender: %s", sender)

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognition_agent.py

@@ -3,6 +3,7 @@ import json
 import time
 from collections import Counter, defaultdict
 
+import cv2
 import numpy as np
 import zmq
 import zmq.asyncio as azmq
@@ -63,8 +64,6 @@ class VisualEmotionRecognitionAgent(BaseAgent):
 
         self.video_in_socket = azmq.Context.instance().socket(zmq.SUB)
 
-        self.video_in_socket.setsockopt(zmq.RCVHWM, 3)
-
         if self.socket_bind:
             self.video_in_socket.bind(self.socket_address)
         else:
@@ -72,9 +71,12 @@ class VisualEmotionRecognitionAgent(BaseAgent):
 
         self.video_in_socket.setsockopt_string(zmq.SUBSCRIBE, "")
         self.video_in_socket.setsockopt(zmq.RCVTIMEO, self.timeout_ms)
+        self.video_in_socket.setsockopt(zmq.CONFLATE, 1)
 
         self.add_behavior(self.emotion_update_loop())
 
+        self.logger.info("Finished setting up %s", self.name)
+
     async def emotion_update_loop(self):
         """
         Background loop to receive video frames, recognize emotions, and update beliefs.
@@ -95,18 +97,21 @@ class VisualEmotionRecognitionAgent(BaseAgent):
             try:
                 await self._paused.wait()
 
-                width, height, image_bytes = await self.video_in_socket.recv_multipart()
-
-                width = int.from_bytes(width, 'little')
-                height = int.from_bytes(height, 'little')
+                frame_bytes = await self.video_in_socket.recv()
 
                 # Convert bytes to a numpy buffer
-                image_array = np.frombuffer(image_bytes, np.uint8)
+                nparr = np.frombuffer(frame_bytes, np.uint8)
 
-                frame = image_array.reshape((height, width, 3))
+                # Decode image into the generic Numpy Array DeepFace expects
+                frame_image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                if frame_image is None:
+                    # Could not decode image, skip this frame
+                    self.logger.warning("Received invalid video frame, skipping.")
+                    continue
 
                 # Get the dominant emotion from each face
-                current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame)
+                current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame_image)
                 # Update emotion counts for each detected face
                 for i, emotion in enumerate(current_emotions):
                     face_stats[i][emotion] += 1
@@ -128,12 +133,8 @@ class VisualEmotionRecognitionAgent(BaseAgent):
                     next_window_time = time.time() + self.window_duration
 
             except zmq.Again:
-                pass
+                self.logger.warning("No video frame received within timeout.")
 
-            except Exception as e:
-                self.logger.error(f"Error in emotion recognition loop: {e}")
-
-    
     async def update_emotions(self, prev_emotions: set[str], emotions: set[str]):
         """
         Compare emotions from previous window and current emotions,
@@ -179,7 +180,7 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         """
         Handle incoming messages.
 
-        Expects messages to pause or resume the Visual Emotion Recognition 
+        Expects messages to pause or resume the Visual Emotion Recognition
         processing from User Interrupt Agent.
 
         :param msg: The received internal message.
@@ -204,4 +205,3 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         """
         self.video_in_socket.close()
         await super().stop()
-        

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognizer.py

@@ -18,36 +18,37 @@ class VisualEmotionRecognizer(abc.ABC):
         To minimize false positives, consider filtering faces with low confidence.
 
         :param image: The input image for emotion recognition.
-        :return: List of dominant emotion detected for each face in the image, 
+        :return: List of dominant emotion detected for each face in the image,
         sorted per face.
         """
         pass
 
+
 class DeepFaceEmotionRecognizer(VisualEmotionRecognizer):
     """
     DeepFace-based implementation of VisualEmotionRecognizer.
-    DeepFape has proven to be quite a pessimistic model, so expect sad, fear and neutral 
+    DeepFape has proven to be quite a pessimistic model, so expect sad, fear and neutral
     emotions to be over-represented.
     """
+
     def __init__(self):
         self.load_model()
-    
+
     def load_model(self):
+        print("Loading Deepface Emotion Model...")
         dummy_img = np.zeros((224, 224, 3), dtype=np.uint8)
-        # analyze does not take a model as an argument, calling it once on a dummy image to load 
+        # analyze does not take a model as an argument, calling it once on a dummy image to load
         # the model
-        DeepFace.analyze(dummy_img, actions=['emotion'], enforce_detection=False)
+        DeepFace.analyze(dummy_img, actions=["emotion"], enforce_detection=False)
+        print("Deepface Emotion Model loaded.")
 
     def sorted_dominant_emotions(self, image) -> list[str]:
-        analysis = DeepFace.analyze(image, 
-                                    actions=['emotion'], 
-                                    enforce_detection=False
-                                    )
-        
-        # Sort faces by x coordinate to maintain left-to-right order
-        analysis.sort(key=lambda face: face['region']['x'])
+        analysis = DeepFace.analyze(image, actions=["emotion"], enforce_detection=False)
 
-        analysis = [face for face in analysis if face['face_confidence'] >= 0.90]
-        
-        dominant_emotions = [face['dominant_emotion'] for face in analysis]
+        # Sort faces by x coordinate to maintain left-to-right order
+        analysis.sort(key=lambda face: face["region"]["x"])
+
+        analysis = [face for face in analysis if face["face_confidence"] >= 0.90]
+
+        dominant_emotions = [face["dominant_emotion"] for face in analysis]
         return dominant_emotions

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -164,12 +164,6 @@ class UserInterruptAgent(BaseAgent):
                     else:
                         self.logger.info("Sent resume command.")
 
-                case "stop":
-                    self.logger.debug(
-                        "Received stop command."
-                    )
-                    await self._send_stop_command()
-
                 case "next_phase" | "reset_phase":
                     await self._send_experiment_control_to_bdi_core(event_type)
                 case _:
@@ -410,34 +404,28 @@ class UserInterruptAgent(BaseAgent):
         if pause == "true":
             # Send pause to VAD and VED agent
             vad_message = InternalMessage(
-                to=[settings.agent_settings.vad_name, 
-                    settings.agent_settings.visual_emotion_recognition_name],
+                to=[
+                    settings.agent_settings.vad_name,
+                    settings.agent_settings.visual_emotion_recognition_name,
+                    settings.agent_settings.face_agent_name,
+                ],
                 sender=self.name,
                 body="PAUSE",
             )
             await self.send(vad_message)
             # Voice Activity Detection and Visual Emotion Recognition agents
-            self.logger.info("Sent pause command to VAD and VED agents.")
+            self.logger.info("Sent pause command to perception agents.")
         else:
             # Send resume to VAD and VED agents
             vad_message = InternalMessage(
-                to=[settings.agent_settings.vad_name, 
-                    settings.agent_settings.visual_emotion_recognition_name],
+                to=[
+                    settings.agent_settings.vad_name,
+                    settings.agent_settings.visual_emotion_recognition_name,
+                    settings.agent_settings.face_agent_name,
+                ],
                 sender=self.name,
                 body="RESUME",
             )
             await self.send(vad_message)
             # Voice Activity Detection and Visual Emotion Recognition agents
-            self.logger.info("Sent resume command to VAD and VED agents.")
-
-    async def _send_stop_command(self):
-        """
-        Send a command to the BDI to stop the program (i.e., skip to end phase).
-        """
-        msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            body="",
-            thread="stop"
-        )
-
-        await self.send(msg)
+            self.logger.info("Sent resume command to perception agents.")

src/control_backend/api/v1/router.py

@@ -12,7 +12,7 @@ api_router = APIRouter()
 
 api_router.include_router(message.router, tags=["Messages"])
 
-api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Commands"])
+api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Commands", "Face"])
 
 api_router.include_router(logs.router, tags=["Logs"])
 

src/control_backend/core/config.py

@@ -64,6 +64,7 @@ class AgentSettings(BaseModel):
     robot_speech_name: str = "robot_speech_agent"
     robot_gesture_name: str = "robot_gesture_agent"
     user_interrupt_name: str = "user_interrupt_agent"
+    face_agent_name: str = "face_detection_agent"
 
 
 class BehaviourSettings(BaseModel):
@@ -82,12 +83,12 @@ class BehaviourSettings(BaseModel):
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
     :ivar transcription_token_buffer: Buffer for transcription tokens.
     :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
-    :ivar visual_emotion_recognition_window_duration_s: Duration in seconds over which to aggregate 
+    :ivar trigger_time_to_wait: Amount of milliseconds to wait before informing the UI about trigger
+     completion.
+    :ivar visual_emotion_recognition_window_duration_s: Duration in seconds over which to aggregate
     emotions and update emotion beliefs.
     :ivar visual_emotion_recognition_min_frames_per_face: Minimum number of frames per face required
     to consider a face valid.
-    :ivar trigger_time_to_wait: Amount of milliseconds to wait before informing the UI about trigger
-     completion.
     """
 
     # ATTENTION: When adding/removing settings, make sure to update the .env.example file
@@ -111,13 +112,14 @@ class BehaviourSettings(BaseModel):
     # Text belief extractor settings
     conversation_history_length_limit: int = 10
 
-    # Visual Emotion Recognition settings
-    visual_emotion_recognition_window_duration_s: int = 5
-    visual_emotion_recognition_min_frames_per_face: int = 3
     # AgentSpeak related settings
     trigger_time_to_wait: int = 2000
     agentspeak_file: str = "src/control_backend/agents/bdi/agentspeak.asl"
 
+    # Visual Emotion Recognition settings
+    visual_emotion_recognition_window_duration_s: int = 5
+    visual_emotion_recognition_min_frames_per_face: int = 3
+
 
 class LLMSettings(BaseModel):
     """

src/control_backend/schemas/program.py

@@ -7,7 +7,7 @@ University within the Software Project course.
 from enum import Enum
 from typing import Literal
 
-from pydantic import UUID4, BaseModel, field_validator
+from pydantic import UUID4, BaseModel
 
 
 class ProgramElement(BaseModel):
@@ -24,13 +24,6 @@ class ProgramElement(BaseModel):
     # To make program elements hashable
     model_config = {"frozen": True}
 
-    @field_validator("name")
-    @classmethod
-    def name_must_not_start_with_number(cls, v: str) -> str:
-        if v and v[0].isdigit():
-            raise ValueError('Field "name" must not start with a number.')
-        return v
-
 
 class LogicalOperator(Enum):
     """
@@ -48,8 +41,8 @@ class LogicalOperator(Enum):
     OR = "OR"
 
 
-type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief
-type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief
+type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief | FaceBelief
+type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief | FaceBelief
 
 
 class KeywordBelief(ProgramElement):
@@ -124,6 +117,16 @@ class EmotionBelief(ProgramElement):
     emotion: str
 
 
+class FaceBelief(ProgramElement):
+    """
+    Represents the belief that at least one face is currently detected.
+    This belief is maintained by a perception agent (not inferred).
+    """
+
+    face_present: bool
+    name: str = ""
+
+
 class Norm(ProgramElement):
     """
     Base class for behavioral norms that guide the robot's interactions.

test/unit/agents/perception/test_face_detection_agent.py

@@ -0,0 +1,152 @@
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+import zmq
+
+import control_backend.agents.perception.face_rec_agent as face_module
+from control_backend.agents.perception.face_rec_agent import FacePerceptionAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.schemas.belief_message import BeliefMessage
+
+
+@pytest.fixture
+def agent():
+    """Return a FacePerceptionAgent instance for testing."""
+    return FacePerceptionAgent(
+        name="face_agent",
+        zmq_address="inproc://test",
+        zmq_bind=False,
+    )
+
+
+@pytest.fixture
+def socket():
+    """Return a mocked ZMQ socket."""
+    sock = AsyncMock()
+    sock.setsockopt_string = MagicMock()
+    sock.connect = MagicMock()
+    sock.bind = MagicMock()
+    return sock
+
+
+def test_connect_socket_connect(agent, socket, monkeypatch):
+    """Test that _connect_socket properly connects when zmq_bind=False."""
+    ctx = MagicMock()
+    ctx.socket.return_value = socket
+    monkeypatch.setattr(face_module.azmq, "Context", MagicMock(instance=lambda: ctx))
+
+    agent._connect_socket()
+    socket.setsockopt_string.assert_called_once_with(zmq.SUBSCRIBE, "")
+    socket.connect.assert_called_once_with(agent._zmq_address)
+    socket.bind.assert_not_called()
+
+
+def test_connect_socket_bind(agent, socket, monkeypatch):
+    """Test that _connect_socket properly binds when zmq_bind=True."""
+    agent._zmq_bind = True
+    ctx = MagicMock()
+    ctx.socket.return_value = socket
+    monkeypatch.setattr(face_module.azmq, "Context", MagicMock(instance=lambda: ctx))
+
+    agent._connect_socket()
+    socket.bind.assert_called_once_with(agent._zmq_address)
+    socket.connect.assert_not_called()
+
+
+def test_connect_socket_twice_is_noop(agent, socket):
+    """Test that calling _connect_socket twice does not overwrite an existing socket."""
+    agent._socket = socket
+    agent._connect_socket()
+    assert agent._socket is socket
+
+
+@pytest.mark.asyncio
+async def test_update_face_belief_present(agent):
+    """Test that _update_face_belief(True) creates the 'face_present' belief."""
+    agent.send = AsyncMock()
+    await agent._update_face_belief(True)
+    msg = agent.send.await_args.args[0]
+    payload = BeliefMessage.model_validate_json(msg.body)
+    assert payload.create[0].name == "face_present"
+
+
+@pytest.mark.asyncio
+async def test_update_face_belief_absent(agent):
+    """Test that _update_face_belief(False) deletes the 'face_present' belief."""
+    agent.send = AsyncMock()
+    await agent._update_face_belief(False)
+    msg = agent.send.await_args.args[0]
+    payload = BeliefMessage.model_validate_json(msg.body)
+    assert payload.delete[0].name == "face_present"
+
+
+@pytest.mark.asyncio
+async def test_post_face_belief_present(agent):
+    """Test that _post_face_belief(True) sends a belief creation message."""
+    agent.send = AsyncMock()
+    await agent._post_face_belief(True)
+    msg = agent.send.await_args.args[0]
+    assert '"create"' in msg.body and '"face_present"' in msg.body
+
+
+@pytest.mark.asyncio
+async def test_post_face_belief_absent(agent):
+    """Test that _post_face_belief(False) sends a belief deletion message."""
+    agent.send = AsyncMock()
+    await agent._post_face_belief(False)
+    msg = agent.send.await_args.args[0]
+    assert '"delete"' in msg.body and '"face_present"' in msg.body
+
+
+@pytest.mark.asyncio
+async def test_handle_pause(agent):
+    """Test that a 'PAUSE' message clears _paused and resets _last_face_state."""
+    agent._paused.set()
+    agent._last_face_state = True
+    msg = InternalMessage(
+        to=agent.name,
+        sender=face_module.settings.agent_settings.user_interrupt_name,
+        thread="cmd",
+        body="PAUSE",
+    )
+    await agent.handle_message(msg)
+    assert not agent._paused.is_set()
+    assert agent._last_face_state is None
+
+
+@pytest.mark.asyncio
+async def test_handle_resume(agent):
+    """Test that a 'RESUME' message sets _paused."""
+    agent._paused.clear()
+    msg = InternalMessage(
+        to=agent.name,
+        sender=face_module.settings.agent_settings.user_interrupt_name,
+        thread="cmd",
+        body="RESUME",
+    )
+    await agent.handle_message(msg)
+    assert agent._paused.is_set()
+
+
+@pytest.mark.asyncio
+async def test_handle_unknown_command(agent):
+    """Test that an unknown command from UserInterruptAgent is ignored (logs a warning)."""
+    msg = InternalMessage(
+        to=agent.name,
+        sender=face_module.settings.agent_settings.user_interrupt_name,
+        thread="cmd",
+        body="???",
+    )
+    await agent.handle_message(msg)
+
+
+@pytest.mark.asyncio
+async def test_handle_unknown_sender(agent):
+    """Test that messages from unknown senders are ignored."""
+    msg = InternalMessage(
+        to=agent.name,
+        sender="someone_else",
+        thread="cmd",
+        body="PAUSE",
+    )
+    await agent.handle_message(msg)

test/unit/agents/perception/visual_emotion_detection/test_visual_emotion_detection.py

@@ -1,338 +0,0 @@
-import asyncio
-import json
-import time
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import numpy as np
-import pytest
-import zmq
-from pydantic_core import ValidationError
-
-# Adjust the import path to match your project structure
-from control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognition_agent import (  # noqa
-    VisualEmotionRecognitionAgent,
-)
-from control_backend.core.agent_system import InternalMessage
-
-# -----------------------------------------------------------------------------
-# Fixtures
-# -----------------------------------------------------------------------------
-
-@pytest.fixture
-def mock_settings():
-    with patch("control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognition_agent.settings") as mock: # noqa
-        # Set default values required by the agent
-        mock.behaviour_settings.visual_emotion_recognition_window_duration_s = 5
-        mock.behaviour_settings.visual_emotion_recognition_min_frames_per_face = 3
-        mock.agent_settings.bdi_core_name = "bdi_core_agent"
-        mock.agent_settings.user_interrupt_name = "user_interrupt_agent"
-        yield mock
-
-@pytest.fixture
-def mock_deepface():
-    with patch("control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognition_agent.DeepFaceEmotionRecognizer") as mock: # noqa
-        instance = mock.return_value
-        instance.sorted_dominant_emotions.return_value = []
-        yield instance
-
-@pytest.fixture
-def mock_zmq_context():
-    with patch("zmq.asyncio.Context.instance") as mock_ctx:
-        mock_socket = MagicMock()
-        # Mock socket methods to return None or AsyncMock for async methods
-        mock_socket.bind = MagicMock()
-        mock_socket.connect = MagicMock()
-        mock_socket.setsockopt = MagicMock()
-        mock_socket.setsockopt_string = MagicMock()
-        mock_socket.recv_multipart = AsyncMock()
-        mock_socket.close = MagicMock()
-        
-        mock_ctx.return_value.socket.return_value = mock_socket
-        yield mock_ctx
-
-@pytest.fixture
-def agent(mock_settings, mock_deepface, mock_zmq_context):
-    # Initialize agent with specific params to control testing
-    agent = VisualEmotionRecognitionAgent(
-        name="test_agent",
-        socket_address="tcp://localhost:5555",
-        bind=False,
-        timeout_ms=100,
-        window_duration=2,
-        min_frames_required=2
-    )
-    # Mock the internal send method from BaseAgent
-    agent.send = AsyncMock()
-    # Mock the add_behavior method from BaseAgent
-    agent.add_behavior = MagicMock()
-    # Mock the logger
-    agent.logger = MagicMock()
-    
-    return agent
-
-# -----------------------------------------------------------------------------
-# Tests
-# -----------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-async def test_initialization(agent):
-    """Test that the agent initializes with correct attributes."""
-    assert agent.name == "test_agent"
-    assert agent.socket_address == "tcp://localhost:5555"
-    assert agent.socket_bind is False
-    assert agent.timeout_ms == 100
-    assert agent._paused.is_set()
-
-@pytest.mark.asyncio
-async def test_setup_connect(agent, mock_zmq_context, mock_deepface):
-    """Test setup routine when binding is False (connect)."""
-    agent.socket_bind = False
-    await agent.setup()
-
-    socket = agent.video_in_socket
-    socket.connect.assert_called_with("tcp://localhost:5555")
-    socket.bind.assert_not_called()
-    socket.setsockopt.assert_any_call(zmq.RCVHWM, 3)
-    socket.setsockopt.assert_any_call(zmq.RCVTIMEO, 100)
-    agent.add_behavior.assert_called_once()
-    assert agent.emotion_recognizer == mock_deepface
-
-@pytest.mark.asyncio
-async def test_setup_bind(agent, mock_zmq_context):
-    """Test setup routine when binding is True."""
-    agent.socket_bind = True
-    await agent.setup()
-
-    socket = agent.video_in_socket
-    socket.bind.assert_called_with("tcp://localhost:5555")
-    socket.connect.assert_not_called()
-
-@pytest.mark.asyncio
-async def test_emotion_update_loop_normal_flow(agent, mock_deepface):
-    """
-    Test the main loop logic:
-    1. Receive frames
-    2. Aggregate stats
-    3. Trigger window update
-    4. Call update_emotions
-    """
-    # Setup dependencies
-    await agent.setup()
-    agent._running = True
-    
-    # Create fake image data (10x10 pixels)
-    width, height = 10, 10
-    image_bytes = np.zeros((10, 10, 3), dtype=np.uint8).tobytes()
-    w_bytes = width.to_bytes(4, 'little')
-    h_bytes = height.to_bytes(4, 'little')
-    
-    # Mock ZMQ receive to return data 3 times, then stop the loop
-    # We use a side_effect on recv_multipart to simulate frames and then stop the loop
-    async def recv_side_effect():
-        if agent._running:
-            return w_bytes, h_bytes, image_bytes
-        raise asyncio.CancelledError()
-
-    agent.video_in_socket.recv_multipart.side_effect = recv_side_effect
-
-    # Mock DeepFace to return emotions
-    # Frame 1: Happy
-    # Frame 2: Happy
-    # Frame 3: Happy (Trigger window)
-    mock_deepface.sorted_dominant_emotions.side_effect = [
-        ["happy"], 
-        ["happy"], 
-        ["happy"]
-    ]
-    
-    # Mock update_emotions to verify it's called
-    agent.update_emotions = AsyncMock()
-
-    # Mock time.time to simulate window passage
-    # We need time to advance significantly after the frames are collected
-    start_time = time.time()
-    
-    with patch("time.time") as mock_time:
-        # Sequence of time calls:
-        # 1. Init next_window_time calculation
-        # 2. Loop 1 check
-        # 3. Loop 2 check
-        # 4. Loop 3 check (Make this one pass the window threshold)
-        mock_time.side_effect = [
-            start_time,          # init
-            start_time + 0.1,    # frame 1 check
-            start_time + 0.2,    # frame 2 check
-            start_time + 10.0,   # frame 3 check (triggers window reset)
-            start_time + 10.1,   # next init
-            start_time + 10.2    # break loop
-        ]
-        
-        # We need to manually break the infinite loop after the update
-        # We can do this by wrapping update_emotions to set _running = False
-        async def stop_loop(*args, **kwargs):
-            agent._running = False
-        
-        agent.update_emotions.side_effect = stop_loop
-
-        # Run the loop
-        await agent.emotion_update_loop()
-
-    # Verifications
-    assert agent.update_emotions.called
-    # Check that it detected 'happy' as dominant (2 required, 3 found)
-    call_args = agent.update_emotions.call_args
-    assert call_args is not None
-    # args: (prev_emotions, window_dominant_emotions)
-    assert call_args[0][1] == {"happy"} 
-
-@pytest.mark.asyncio
-async def test_emotion_update_loop_insufficient_frames(agent, mock_deepface):
-    """Test that emotions are NOT updated if min_frames_required is not met."""
-    await agent.setup()
-    agent._running = True
-    agent.min_frames_required = 5  # Set high requirement
-
-    width, height = 10, 10
-    image_bytes = np.zeros((10, 10, 3), dtype=np.uint8).tobytes()
-    w_bytes = width.to_bytes(4, 'little')
-    h_bytes = height.to_bytes(4, 'little')
-
-    agent.video_in_socket.recv_multipart.return_value = (w_bytes, h_bytes, image_bytes)
-    mock_deepface.sorted_dominant_emotions.return_value = ["sad"]
-    
-    agent.update_emotions = AsyncMock()
-
-    with patch("time.time") as mock_time:
-        # Time setup to trigger window processing immediately
-        mock_time.side_effect = [0, 100, 101] 
-        
-        # Stop loop after first pass
-        async def stop_loop(*args, **kwargs):
-            agent._running = False
-        agent.update_emotions.side_effect = stop_loop
-
-        await agent.emotion_update_loop()
-
-    # It should call update_emotions with EMPTY set because min frames (5) > detected (1)
-    call_args = agent.update_emotions.call_args
-    assert call_args[0][1] == set()
-
-@pytest.mark.asyncio
-async def test_emotion_update_loop_zmq_again_and_exception(agent):
-    """Test that the loop handles ZMQ timeouts (Again) and generic exceptions."""
-    await agent.setup()
-    agent._running = True
-
-    # Side effect: 
-    # 1. Raise ZMQ Again (Timeout) -> should log warning
-    # 2. Raise Generic Exception -> should log error
-    # 3. Raise CancelledError -> stop loop (simulating stop)
-    agent.video_in_socket.recv_multipart.side_effect = [
-        zmq.Again(),
-        RuntimeError("Random Failure"),
-        asyncio.CancelledError() # To break loop cleanly
-    ]
-
-    # We need to ensure the loop doesn't block on _paused
-    agent._paused.set()
-
-    # Run loop
-    try:
-        await agent.emotion_update_loop()
-    except asyncio.CancelledError:
-        pass
-
-@pytest.mark.asyncio
-async def test_update_emotions_logic(agent, mock_settings):
-    """Test the logic for calculating diffs and sending messages."""
-    agent.name = "viz_agent"
-    
-    # Case 1: No change
-    await agent.update_emotions({"happy"}, {"happy"})
-    agent.send.assert_not_called()
-
-    # Case 2: Remove 'happy', Add 'sad'
-    await agent.update_emotions({"happy"}, {"sad"})
-    
-    assert agent.send.called
-    call_args = agent.send.call_args
-    msg = call_args[0][0] # InternalMessage object
-    
-    assert msg.to == mock_settings.agent_settings.bdi_core_name
-    assert msg.sender == "viz_agent"
-    assert msg.thread == "beliefs"
-    
-    payload = json.loads(msg.body)
-    
-    # Check Created Beliefs
-    assert len(payload["create"]) == 1
-    assert payload["create"][0]["name"] == "emotion_detected"
-    assert payload["create"][0]["arguments"] == ["sad"]
-    
-    # Check Deleted Beliefs
-    assert len(payload["delete"]) == 1
-    assert payload["delete"][0]["name"] == "emotion_detected"
-    assert payload["delete"][0]["arguments"] == ["happy"]
-
-@pytest.mark.asyncio
-async def test_update_emotions_validation_error(agent):
-    """Test that ValidationErrors during Belief creation are caught."""
-    
-    # We patch Belief to raise ValidationError
-    with patch("control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognition_agent.Belief") as MockBelief: # noqa
-        MockBelief.side_effect = ValidationError.from_exception_data("Simulated Error", [])
-        
-        # Try to update emotions
-        await agent.update_emotions(prev_emotions={"happy"}, emotions={"sad"})
-        
-        # Verify empty payload is sent (or payload with valid ones if mixed)
-        # In this case both failed, so payload lists should be empty
-        assert agent.send.called
-        msg = agent.send.call_args[0][0]
-        payload = json.loads(msg.body)
-        assert payload["create"] == []
-        assert payload["delete"] == []
-
-@pytest.mark.asyncio
-async def test_handle_message(agent, mock_settings):
-    """Test message handling for Pause/Resume."""
-    
-    # Setup
-    ui_name = mock_settings.agent_settings.user_interrupt_name
-    
-    # 1. PAUSE message
-    msg_pause = InternalMessage(to="me", sender=ui_name, body="PAUSE")
-    await agent.handle_message(msg_pause)
-    assert not agent._paused.is_set() # Should be cleared (paused)
-    agent.logger.info.assert_called_with("Pausing Visual Emotion Recognition processing.")
-
-    # 2. RESUME message
-    msg_resume = InternalMessage(to="me", sender=ui_name, body="RESUME")
-    await agent.handle_message(msg_resume)
-    assert agent._paused.is_set() # Should be set (running)
-
-    # 3. Unknown command
-    msg_unknown = InternalMessage(to="me", sender=ui_name, body="DANCE")
-    await agent.handle_message(msg_unknown)
-
-    # 4. Unknown sender
-    msg_random = InternalMessage(to="me", sender="random_guy", body="PAUSE")
-    await agent.handle_message(msg_random)
-
-@pytest.mark.asyncio
-async def test_stop(agent, mock_zmq_context):
-    """Test the stop method cleans up resources."""
-    # We need to mock super().stop(). Since we can't easily patch super(),
-    # and the provided BaseAgent code shows stop() just sets _running and cancels tasks,
-    # we can rely on the fact that VisualEmotionRecognitionAgent calls it.
-    
-    # However, since we provided a 'agent' fixture that mocks things, we should verify specific cleanups. # noqa
-    await agent.setup()
-    
-    with patch("control_backend.agents.BaseAgent.stop", new_callable=AsyncMock) as mock_super_stop:
-        await agent.stop()
-        
-        # Verify socket closed
-        agent.video_in_socket.close.assert_called_once()
-        # Verify parent stop called
-        mock_super_stop.assert_called_once()

test/unit/agents/user_interrupt/test_user_interrupt.py

@@ -303,33 +303,6 @@ async def test_send_experiment_control(agent):
     assert msg.thread == "reset_experiment"
 
 
-@pytest.mark.asyncio
-async def test_send_pause_command(agent):
-    # --- Test PAUSE ---
-    await agent._send_pause_command("true")
-
-    # Should send exactly 1 message
-    assert agent.send.await_count == 1
-    
-    # Extract the message object from the mock call
-    # call_args[0] are positional args, and [0] is the first arg (the message)
-    msg = agent.send.call_args[0][0]
-
-    # Verify Body
-    assert msg.body == "PAUSE"
-
-    # --- Test RESUME ---
-    agent.send.reset_mock()
-    await agent._send_pause_command("false")
-
-    # Should send exactly 1 message
-    assert agent.send.await_count == 1
-    
-    msg = agent.send.call_args[0][0]
-
-    # Verify Body
-    assert msg.body == "RESUME"
-
 @pytest.mark.asyncio
 async def test_setup(agent):
     """Test the setup method initializes sockets correctly."""

uv.lock

@@ -1524,7 +1524,6 @@ dependencies = [
     { name = "sphinx-rtd-theme" },
     { name = "tf-keras" },
     { name = "torch" },
-    { name = "tornado", marker = "sys_platform == 'win32'" },
     { name = "uvicorn" },
 ]
 
@@ -1541,7 +1540,6 @@ dev = [
 ]
 test = [
     { name = "agentspeak" },
-    { name = "deepface" },
     { name = "fastapi" },
     { name = "httpx" },
     { name = "mlx-whisper", marker = "sys_platform == 'darwin'" },
@@ -1556,7 +1554,6 @@ test = [
     { name = "pyyaml" },
     { name = "pyzmq" },
     { name = "soundfile" },
-    { name = "tf-keras" },
 ]
 
 [package.metadata]
@@ -1580,7 +1577,6 @@ requires-dist = [
     { name = "sphinx-rtd-theme", specifier = ">=3.0.2" },
     { name = "tf-keras", specifier = ">=2.20.1" },
     { name = "torch", specifier = ">=2.8.0" },
-    { name = "tornado", marker = "sys_platform == 'win32'" },
     { name = "uvicorn", specifier = ">=0.37.0" },
 ]
 
@@ -1597,7 +1593,6 @@ dev = [
 ]
 test = [
     { name = "agentspeak", specifier = ">=0.2.2" },
-    { name = "deepface", specifier = ">=0.0.97" },
     { name = "fastapi", specifier = ">=0.115.6" },
     { name = "httpx", specifier = ">=0.28.1" },
     { name = "mlx-whisper", marker = "sys_platform == 'darwin'", specifier = ">=0.4.3" },
@@ -1612,7 +1607,6 @@ test = [
     { name = "pyyaml", specifier = ">=6.0.3" },
     { name = "pyzmq", specifier = ">=27.1.0" },
     { name = "soundfile", specifier = ">=0.13.1" },
-    { name = "tf-keras", specifier = ">=2.20.1" },
 ]
 
 [[package]]
@@ -2726,17 +2720,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/52/27/7fc2d7435af044ffbe0b9b8e98d99eac096d43f128a5cde23c04825d5dcf/torchaudio-2.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:d4a715d09ac28c920d031ee1e60ecbc91e8a5079ad8c61c0277e658436c821a6", size = 2549553, upload-time = "2025-08-06T14:59:00.019Z" },
 ]
 
-[[package]]
-name = "tornado"
-version = "6.5.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/37/1d/0a336abf618272d53f62ebe274f712e213f5a03c0b2339575430b8362ef2/tornado-6.5.4.tar.gz", hash = "sha256:a22fa9047405d03260b483980635f0b041989d8bcc9a313f8fe18b411d84b1d7", size = 513632, upload-time = "2025-12-15T19:21:03.836Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0c/1a/d7592328d037d36f2d2462f4bc1fbb383eec9278bc786c1b111cbbd44cfa/tornado-6.5.4-cp39-abi3-win32.whl", hash = "sha256:1768110f2411d5cd281bac0a090f707223ce77fd110424361092859e089b38d1", size = 446481, upload-time = "2025-12-15T19:21:00.008Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/6d/c69be695a0a64fd37a97db12355a035a6d90f79067a3cf936ec2b1dc38cd/tornado-6.5.4-cp39-abi3-win_amd64.whl", hash = "sha256:fa07d31e0cd85c60713f2b995da613588aa03e1303d75705dca6af8babc18ddc", size = 446886, upload-time = "2025-12-15T19:21:01.287Z" },
-    { url = "https://files.pythonhosted.org/packages/50/49/8dc3fd90902f70084bd2cd059d576ddb4f8bb44c2c7c0e33a11422acb17e/tornado-6.5.4-cp39-abi3-win_arm64.whl", hash = "sha256:053e6e16701eb6cbe641f308f4c1a9541f91b6261991160391bfc342e8a551a1", size = 445910, upload-time = "2025-12-15T19:21:02.571Z" },
-]
-
 [[package]]
 name = "tqdm"
 version = "4.67.1"