Merge branch 'fix/trigger-subgoal-error' into 'main'

Allow subgoals in triggers and empty plan

See merge request ics/sp/2025/n25b/pepperplus-cb!50

pyproject.toml

@@ -40,6 +40,7 @@ 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'",
@@ -54,6 +55,7 @@ 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

@@ -4,6 +4,7 @@ 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
@@ -30,7 +31,6 @@ from control_backend.schemas.program import (
     BasicNorm,
     ConditionalNorm,
     EmotionBelief,
-    FaceBelief,
     GestureAction,
     Goal,
     InferredBelief,
@@ -67,6 +67,7 @@ class AgentSpeakGenerator:
     """
 
     _asp: AstProgram
+    logger = logging.getLogger(__name__)
 
     def generate(self, program: Program) -> str:
         """
@@ -480,7 +481,8 @@ class AgentSpeakGenerator:
         :param main_goal: Whether this is a main goal (for UI notification purposes).
         """
         context: list[AstExpression] = [self._astify(phase)]
-        context.append(~self._astify(goal, achieved=True))
+        if goal.can_fail:
+            context.append(~self._astify(goal, achieved=True))
         if previous_goal and previous_goal.can_fail:
             context.append(self._astify(previous_goal, achieved=True))
         if not continues_response:
@@ -504,6 +506,10 @@ class AgentSpeakGenerator:
         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(
@@ -564,10 +570,10 @@ class AgentSpeakGenerator:
             )
         )
         for step in trigger.plan.steps:
-            body.append(self._step_to_statement(step))
             if isinstance(step, Goal):
-                step.can_fail = False  # triggers are continuous sequence
-                subgoals.append(step)
+                new_step = step.model_copy(update={"can_fail": False})  # triggers are sequence
+                subgoals.append(new_step)
+            body.append(self._step_to_statement(step))
 
         # Arbitrary wait for UI to display nicely
         body.append(
@@ -688,10 +694,6 @@ 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/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 FacePerceptionAgent, VADAgent
+from ..perception import VADAgent
 
 
 class RICommunicationAgent(BaseAgent):
@@ -181,7 +181,7 @@ class RICommunicationAgent(BaseAgent):
             bind = port_data["bind"]
 
             if not bind:
-                addr = f"tcp://localhost:{port}"
+                addr = f"tcp://{settings.ri_host}:{port}"
             else:
                 addr = f"tcp://*:{port}"
 
@@ -224,13 +224,6 @@ 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)
 
@@ -335,7 +328,7 @@ 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()
 
@@ -345,3 +338,4 @@ 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

@@ -185,6 +185,9 @@ 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

@@ -7,7 +7,6 @@ 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

@@ -1,144 +0,0 @@
-"""
-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

@@ -3,7 +3,6 @@ import json
 import time
 from collections import Counter, defaultdict
 
-import cv2
 import numpy as np
 import zmq
 import zmq.asyncio as azmq
@@ -64,6 +63,8 @@ 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:
@@ -71,12 +72,9 @@ 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.
@@ -97,21 +95,18 @@ class VisualEmotionRecognitionAgent(BaseAgent):
             try:
                 await self._paused.wait()
 
-                frame_bytes = await self.video_in_socket.recv()
+                width, height, image_bytes = await self.video_in_socket.recv_multipart()
+
+                width = int.from_bytes(width, 'little')
+                height = int.from_bytes(height, 'little')
 
                 # Convert bytes to a numpy buffer
-                nparr = np.frombuffer(frame_bytes, np.uint8)
+                image_array = np.frombuffer(image_bytes, np.uint8)
 
-                # 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
+                frame = image_array.reshape((height, width, 3))
 
                 # Get the dominant emotion from each face
-                current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame_image)
+                current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame)
                 # Update emotion counts for each detected face
                 for i, emotion in enumerate(current_emotions):
                     face_stats[i][emotion] += 1
@@ -135,6 +130,10 @@ class VisualEmotionRecognitionAgent(BaseAgent):
             except zmq.Again:
                 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,
@@ -180,7 +179,7 @@ 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.
@@ -205,3 +204,4 @@ class VisualEmotionRecognitionAgent(BaseAgent):
         """
         self.video_in_socket.close()
         await super().stop()
+        

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

@@ -18,37 +18,36 @@ 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)
-        print("Deepface Emotion Model loaded.")
+        DeepFace.analyze(dummy_img, actions=['emotion'], enforce_detection=False)
 
     def sorted_dominant_emotions(self, image) -> list[str]:
-        analysis = DeepFace.analyze(image, actions=["emotion"], enforce_detection=False)
-
+        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.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]
+        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

@@ -404,28 +404,22 @@ 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,
-                    settings.agent_settings.face_agent_name,
-                ],
+                to=[settings.agent_settings.vad_name, 
+                    settings.agent_settings.visual_emotion_recognition_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 perception agents.")
+            self.logger.info("Sent pause command to VAD and VED 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,
-                    settings.agent_settings.face_agent_name,
-                ],
+                to=[settings.agent_settings.vad_name, 
+                    settings.agent_settings.visual_emotion_recognition_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 perception agents.")
+            self.logger.info("Sent resume command to VAD and VED 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", "Face"])
+api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Commands"])
 
 api_router.include_router(logs.router, tags=["Logs"])
 

src/control_backend/core/config.py

@@ -64,7 +64,6 @@ 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):
@@ -83,12 +82,12 @@ 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 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
+    :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
@@ -112,13 +111,12 @@ class BehaviourSettings(BaseModel):
     # Text belief extractor settings
     conversation_history_length_limit: int = 10
 
-    # 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
+    # AgentSpeak related settings
+    trigger_time_to_wait: int = 2000
+    agentspeak_file: str = "src/control_backend/agents/bdi/agentspeak.asl"
 
 
 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
+from pydantic import UUID4, BaseModel, field_validator
 
 
 class ProgramElement(BaseModel):
@@ -24,6 +24,13 @@ 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):
     """
@@ -41,8 +48,8 @@ class LogicalOperator(Enum):
     OR = "OR"
 
 
-type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief | FaceBelief
-type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief | FaceBelief
+type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief
+type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief
 
 
 class KeywordBelief(ProgramElement):
@@ -117,16 +124,6 @@ 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

@@ -1,152 +0,0 @@
-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

@@ -0,0 +1,338 @@
+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

@@ -303,6 +303,33 @@ 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

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.13"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'darwin'",
@@ -1540,6 +1540,7 @@ dev = [
 ]
 test = [
     { name = "agentspeak" },
+    { name = "deepface" },
     { name = "fastapi" },
     { name = "httpx" },
     { name = "mlx-whisper", marker = "sys_platform == 'darwin'" },
@@ -1554,6 +1555,7 @@ test = [
     { name = "pyyaml" },
     { name = "pyzmq" },
     { name = "soundfile" },
+    { name = "tf-keras" },
 ]
 
 [package.metadata]
@@ -1593,6 +1595,7 @@ 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" },
@@ -1607,6 +1610,7 @@ 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]]