About to restructure voicebot to support dynamic agent loading

Dockerfile.python-3.12

@@ -0,0 +1,44 @@
+FROM ubuntu:plucky
+
+# Install build dependencies and tools
+RUN apt-get update \
+    && DEBIAN_FRONTEND=noninteractive apt-get install -y \
+    build-essential \
+    checkinstall \
+    zlib1g-dev \
+    libncurses5-dev \
+    libgdbm-dev \
+    libnss3-dev \
+    libssl-dev \
+    libreadline-dev \
+    libffi-dev \
+    libsqlite3-dev \
+    libbz2-dev \
+    tk-dev \
+    liblzma-dev \
+    wget \
+    tar \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/{apt,dpkg,cache,log}
+
+# Download Python 3.12.11 source
+RUN wget https://www.python.org/ftp/python/3.12.11/Python-3.12.11.tgz -O /tmp/Python-3.12.11.tgz \
+    && tar -xvf /tmp/Python-3.12.11.tgz -C /tmp \
+    && rm /tmp/Python-3.12.11.tgz
+
+# Change to source directory
+WORKDIR /tmp/Python-3.12.11
+
+# Configure the build
+RUN ./configure --enable-optimizations --enable-shared --prefix=/usr
+
+# Compile
+RUN make -j $(nproc)
+
+RUN make altinstall DESTDIR=/tmp/python-build \
+    && mkdir -p /tmp/python-build/DEBIAN \
+    && echo "Package: python3.12-custom\nVersion: 3.12.11-1\nSection: custom\nPriority: optional\nArchitecture: amd64\nMaintainer: you@example.com\nDescription: Custom Python 3.12.11 build" > /tmp/python-build/DEBIAN/control \
+    && dpkg-deb --build /tmp/python-build /python3.12-custom_3.12.11-1_amd64.deb \
+    && rm -rf /tmp/python-build
+    
+CMD ["/bin/bash", "-c", "cp /python3.12-custom_3.12.11-1_*.deb /build/"]

README.md

@@ -298,6 +298,23 @@ The backend acts as the signaling server, routing WebRTC negotiation messages be
 
 ## Development Tools
 
+### Python version
+
+Many of the packages required for ML are not available or with correct versions 
+with Python 3.13 (default for Ubuntu:Plucky.) If your host is running 
+python>3.12 and there aren't any python-3.12 packages available to install, you 
+can use the 'python-3.12' container to build a python3.12 package:
+
+```
+# Build the .deb
+docker compose build python-3.12 
+# Copy the .deb from the container image to ./build
+docker compose run python-3.12
+# Install the pkg:
+sudo dpkg -i build/python*.deb
+```
+
+
 ### TypeScript Type Generation
 The project includes automatic TypeScript type generation from the FastAPI OpenAPI schema:
 

docker-compose.yml

@@ -18,6 +18,17 @@ services:
     networks:
       - ai-voicebot-net
 
+  python-3.12:
+    container_name: python-3.12
+    hostname: python-3.12
+    build:
+      context: .
+      dockerfile: Dockerfile.python-3.12
+    restart: no
+    volumes:
+      - ./build:/build:rw
+
+
   server:
     container_name: server
     hostname: server

voicebot/main.py

@@ -3,7 +3,6 @@ WebRTC Media Agent for Python
 
 This module provides WebRTC signaling server communication and peer connection management.
 Synthetic audio/video track creation is handled by the synthetic_media module.
-Test change to trigger reload - TESTING RELOAD NOW
 """
 
 from __future__ import annotations

voicebot/pyproject.toml

@@ -6,7 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "aiortc>=1.13.0",
-    "numpy>=2.3.2",
+    "numpy<2.3",
     "opencv-python>=4.11.0.86",
     "pydantic>=2.11.7",
     "websockets>=15.0.1",

voicebot/requirements.txt

@@ -13,15 +13,15 @@ audioread==3.0.1
 autograd==1.8.0
 av==14.4.0
 brotli==1.1.0
-certifi==2022.12.7
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==2.1.1
+charset-normalizer==3.4.3
 click==8.2.1
 cma==4.3.0
 contourpy==1.3.3
 cryptography==45.0.7
 cycler==0.12.1
-datasets==2.19.1
+datasets==4.0.0
 decorator==5.2.1
 deprecated==1.2.18
 dill==0.3.8
@@ -31,7 +31,7 @@ ffmpy==0.6.1
 filelock==3.13.1
 fonttools==4.59.2
 frozenlist==1.7.0
-fsspec==2024.2.0
+fsspec==2024.6.1
 google-crc32c==1.7.1
 gradio==5.44.1
 gradio-client==1.12.1
@@ -42,7 +42,7 @@ hf-xet==1.1.9
 httpcore==1.0.9
 httpx==0.28.1
 huggingface-hub==0.34.4
-idna==3.4
+idna==3.10
 ifaddr==0.2.0
 jinja2==3.1.4
 jiwer==4.0.0
@@ -70,12 +70,12 @@ numba==0.61.2
 numpy==2.2.6
 onnx==1.19.0
 opencv-python==4.11.0.86
-openvino==2025.2.0
+openvino==2025.3.0
 openvino-telemetry==2025.2.0
 optimum==1.27.0
-optimum-intel @ git+https://github.com/huggingface/optimum-intel.git@cd0028599d65e841d6fe16d4d9ccd5528f89f3cb
+optimum-intel @ git+https://github.com/huggingface/optimum-intel.git@c35534d077dddf9382c6d8699f13412d28b19853
 orjson==3.11.3
-packaging==24.1
+packaging==25.0
 pandas==2.2.3
 pillow==11.3.0
 platformdirs==4.4.0
@@ -84,7 +84,6 @@ propcache==0.3.2
 protobuf==6.32.0
 psutil==7.0.0
 pyarrow==21.0.0
-pyarrow-hotfix==0.7
 pycparser==2.22
 pydantic==2.11.7
 pydantic-core==2.33.2
@@ -103,7 +102,7 @@ pyyaml==6.0.2
 rapidfuzz==3.14.0
 referencing==0.36.2
 regex==2025.9.1
-requests==2.28.1
+requests==2.32.5
 rich==14.1.0
 rpds-py==0.27.1
 ruff==0.12.11
@@ -131,7 +130,7 @@ typer==0.17.3
 typing-extensions==4.15.0
 typing-inspection==0.4.1
 tzdata==2025.2
-urllib3==1.26.13
+urllib3==2.5.0
 uvicorn==0.35.0
 websockets==15.0.1
 wrapt==1.17.3

voicebot/synthetic_media.py

@@ -13,7 +13,8 @@ import time
 import random
 from av.audio.frame import AudioFrame
 from asyncio import Queue, create_task, sleep
-from typing import TypedDict, TYPE_CHECKING
+from typing import TypedDict, TYPE_CHECKING, Tuple, List, Optional, Any
+import numpy.typing as npt
 from aiortc import MediaStreamTrack
 from av import VideoFrame
 from logger import logger
@@ -42,13 +43,7 @@ class BounceEvent(TypedDict):
 
 
 class AnimatedVideoTrack(MediaStreamTrack):
-    """
+    """Animated synthetic video track (fixed, properly-indented implementation)."""
-    Synthetic video track that generates animated content with a bouncing ball.
-
-    Can also composite remote video tracks with edge detection overlay.
-    Remote video tracks are processed through Canny edge detection and blended
-    with the synthetic ball animation.
-    """
 
     kind = "video"
 
@@ -58,117 +53,98 @@ class AnimatedVideoTrack(MediaStreamTrack):
         width: int = 320,
         height: int = 240,
         name: str = "",
-        audio_track: "SyntheticAudioTrack | None" = None,
+        audio_track: Optional["SyntheticAudioTrack"] = None,
-    ):
+    ) -> None:
         super().__init__()
         self.width = width
         self.height = height
         self.name = name
         self.clock = clock
-        self.fps = 15
         self._next_frame_index = 0
 
-        self.audio_track = audio_track  # Reference to the audio track
+        self.audio_track = audio_track
-        self.remote_video_tracks: list[
+        self.remote_video_tracks: List[MediaStreamTrack] = []
-            MediaStreamTrack
-        ] = []  # Store remote video tracks
 
-        # Generate color from name hash (similar to JavaScript nameToColor)
+        self.ball_color = self._name_to_color(name) if name else (0, 255, 136)
-        self.ball_color = (
-            self._name_to_color(name) if name else (0, 255, 136)
-        )  # Default green
 
-        # Ball properties
         ball_radius = min(width, height) * 0.06
         self.ball = {
             "x": random.uniform(ball_radius, width - ball_radius),
             "y": random.uniform(ball_radius, height - ball_radius),
             "radius": ball_radius,
-            "speed_mps": 0.5,  # Speed in meters per second (frame width = 1 meter)
+            "speed_mps": 0.5,
-            "direction_x": random.uniform(
+            "direction_x": random.uniform(-1.0, 1.0),
-                -1.0, 1.0
+            "direction_y": random.uniform(-1.0, 1.0),
-            ),  # Random direction x component (-1 to 1)
-            "direction_y": random.uniform(
-                -1.0, 1.0
-            ),  # Random direction y component (-1 to 1)
         }
 
         self.frame_count = 0
         self._start_time = time.time()
         self._last_frame_time = time.time()
-        self.fps = 15  # Target frames per second
+        self.fps = 15
-        self._remote_latest = {}  # track -> np.ndarray
-        self._remote_tasks: list[
-            tuple[MediaStreamTrack, object, Queue[np.ndarray]]
-        ] = []
 
-    def set_ball_speed(self, speed_mps: float):
+        self._remote_latest: dict[MediaStreamTrack, npt.NDArray[Any]] = {}
-        """Set the ball speed in meters per second"""
+        self._remote_tasks: List[Tuple[MediaStreamTrack, object, Queue[npt.NDArray[Any]]]] = []
+
+    def set_ball_speed(self, speed_mps: float) -> None:
         self.ball["speed_mps"] = speed_mps
 
-    def add_remote_video_track(self, track: MediaStreamTrack):
+    def add_remote_video_track(self, track: MediaStreamTrack) -> None:
-        """Add a remote video track to be composited with edge detection"""
+        if track.kind != "video":
-        if track.kind == "video":
+            return
-            self.remote_video_tracks.append(track)
+        self.remote_video_tracks.append(track)
-            logger.info(f"Added remote video track: {track}")
+        logger.info("Added remote video track: %s", track)
-            q: Queue[np.ndarray] = Queue(maxsize=1)
+        q: Queue[npt.NDArray[Any]] = Queue(maxsize=1)
 
-            async def pump():
+        async def pump() -> None:
-                while True:
+            while True:
-                    frame = await track.recv()
+                frame = await track.recv()
-                    if isinstance(frame, VideoFrame):
+                if isinstance(frame, VideoFrame):
-                        img: np.ndarray = frame.to_ndarray(format="bgr24")
+                    img = frame.to_ndarray(format="bgr24")
-                        if q.full():
+                    if q.full():
+                        try:
                             _ = q.get_nowait()
-                        await q.put(img)
+                        except Exception:
+                            pass
+                    await q.put(img)
 
-            t = create_task(pump())
+        t = create_task(pump())
-            self._remote_tasks.append((track, t, q))
+        self._remote_tasks.append((track, t, q))
 
-    def remove_remote_video_track(self, track: MediaStreamTrack):
+    def remove_remote_video_track(self, track: MediaStreamTrack) -> None:
-        """Remove a remote video track"""
         if track in self.remote_video_tracks:
             self.remote_video_tracks.remove(track)
-            logger.info(f"Removed remote video track: {track}")
+            logger.info("Removed remote video track: %s", track)
 
-    def _calculate_velocity_components(self, dt: float) -> tuple[float, float]:
+    def _calculate_velocity_components(self, dt: float) -> Tuple[float, float]:
-        dir_x, dir_y = self.ball["direction_x"], self.ball["direction_y"]
+        dir_x = float(self.ball["direction_x"])
-        mag = np.hypot(dir_x, dir_y)
+        dir_y = float(self.ball["direction_y"])
+        mag = math.hypot(dir_x, dir_y)
         if mag == 0:
             dir_x_norm, dir_y_norm = 1.0, 0.0
         else:
             dir_x_norm, dir_y_norm = dir_x / mag, dir_y / mag
-        pixels_per_second = self.width * self.ball["speed_mps"]
+        pixels_per_second = self.width * float(self.ball["speed_mps"])
         pixels_this_frame = pixels_per_second * dt
         return pixels_this_frame * dir_x_norm, pixels_this_frame * dir_y_norm
 
-    async def next_timestamp(self):
+    async def next_timestamp(self) -> Tuple[int, float]:
-        """Returns (pts, time_base) for 15 FPS video"""
+        pts = int(self.frame_count * (1 / self.fps) * 90000)
-        pts = int(self.frame_count * (1 / 15) * 90000)
         time_base = 1 / 90000
         return pts, time_base
 
-    def _name_to_color(self, name: str) -> tuple[int, int, int]:
+    def _name_to_color(self, name: str) -> Tuple[int, int, int]:
-        """Convert name to HSL color, then to RGB tuple"""
-        # Simple hash function (djb2)
         hash_value = 5381
-        for char in name:
+        for ch in name:
-            hash_value = ((hash_value << 5) + hash_value + ord(char)) & 0xFFFFFFFF
+            hash_value = ((hash_value << 5) + hash_value + ord(ch)) & 0xFFFFFFFF
-
-        # Generate HSL color from hash
         hue = abs(hash_value) % 360
-        sat = 60 + (abs(hash_value) % 30)  # 60-89%
+        sat = 60 + (abs(hash_value) % 30)
-        light = 45 + (abs(hash_value) % 30)  # 45-74%
+        light = 45 + (abs(hash_value) % 30)
-
-        # Convert HSL to RGB
         h = hue / 360.0
         s = sat / 100.0
         lightness = light / 100.0
-
         c = (1 - abs(2 * lightness - 1)) * s
         x = c * (1 - abs((h * 6) % 2 - 1))
         m = lightness - c / 2
-
         if h < 1 / 6:
             r, g, b = c, x, 0
         elif h < 2 / 6:
@@ -181,54 +157,42 @@ class AnimatedVideoTrack(MediaStreamTrack):
             r, g, b = x, 0, c
         else:
             r, g, b = c, 0, x
+        return int((b + m) * 255), int((g + m) * 255), int((r + m) * 255)
 
-        return (
+    async def recv(self) -> VideoFrame:
-            int((b + m) * 255),
-            int((g + m) * 255),
-            int((r + m) * 255),
-        )  # BGR for OpenCV
-
-    async def recv(self):
-        """Generate video frames at 15 FPS"""
         pts, time_base = await self.next_timestamp()
 
-        # Target timestamp for this frame (seconds since t0)
+        # schedule frame according to clock
         target_t = self._next_frame_index / self.fps
         now = self.clock.now()
         if target_t > now:
             await sleep(target_t - now)
 
-        # Use constant dt tied to fps (prevents physics jitter)
         dt = 1.0 / self.fps
         dx, dy = self._calculate_velocity_components(dt)
 
-        # PTS derived from frame index, not wall clock
         pts = int(self._next_frame_index * (90000 / self.fps))
         time_base = 1 / 90000
-
         self._next_frame_index += 1
 
-        # Create black background
+        frame_array: npt.NDArray[np.uint8] = np.zeros((self.height, self.width, 3), dtype=np.uint8)
-        frame_array = np.zeros((self.height, self.width, 3), dtype=np.uint8)
 
-        # Process remote video tracks with edge detection
+        # Blend in edge-detected remote frames if available
         for _track, _task, q in self._remote_tasks:
             try:
-                img: np.ndarray = q.get_nowait()
+                img = q.get_nowait()
             except Exception:
                 continue
             edges = cv2.Canny(img, 100, 200)
             img_edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
             if img_edges.shape[:2] != (self.height, self.width):
                 img_edges = cv2.resize(img_edges, (self.width, self.height))
-            frame_array = cv2.addWeighted(frame_array, 0.7, img_edges, 0.3, 0.0)
+            frame_array = cv2.addWeighted(frame_array, 0.7, img_edges, 0.3, 0.0).astype(np.uint8)
 
-        # Update ball position
+        # Update ball position and handle bouncing
         ball = self.ball
         ball["x"] += dx
         ball["y"] += dy
-
-        # Bounce off walls and trigger audio events
         bounce_occurred = False
         if ball["x"] + ball["radius"] >= self.width or ball["x"] - ball["radius"] <= 0:
             ball["direction_x"] = -ball["direction_x"]
@@ -237,48 +201,21 @@ class AnimatedVideoTrack(MediaStreamTrack):
             ball["direction_y"] = -ball["direction_y"]
             bounce_occurred = True
 
-        # Trigger bounce sound if a bounce occurred
+        if bounce_occurred and self.audio_track is not None:
-        if bounce_occurred and self.audio_track:
             logger.info("Video: Bounce detected, triggering audio event")
             self.audio_track.add_bounce_event_at(self.clock.now())
 
-        # Keep ball in bounds
         ball["x"] = max(ball["radius"], min(self.width - ball["radius"], ball["x"]))
         ball["y"] = max(ball["radius"], min(self.height - ball["radius"], ball["y"]))
 
-        # Draw ball
+        cv2.circle(frame_array, (int(ball["x"]), int(ball["y"])), int(ball["radius"]), self.ball_color, -1)
-        cv2.circle(
-            frame_array,
-            (int(ball["x"]), int(ball["y"])),
-            int(ball["radius"]),
-            self.ball_color,
-            -1,
-        )
 
-        # Add frame counter and speed text
         frame_text = f"Frame: {int(time.time() * 1000) % 10000}"
         speed_text = f"Speed: {ball['speed_mps']:.2f} m/s"
-        cv2.putText(
+        cv2.putText(frame_array, frame_text, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
-            frame_array,
+        cv2.putText(frame_array, speed_text, (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
-            frame_text,
-            (10, 20),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
-            (255, 255, 255),
-            1,
-        )
-        cv2.putText(
-            frame_array,
-            speed_text,
-            (10, 40),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
-            (255, 255, 255),
-            1,
-        )
 
-        # Convert to VideoFrame
+        frame = VideoFrame.from_ndarray(frame_array, format="bgr24")
-        frame = VideoFrame.from_ndarray(frame_array.astype(np.uint8), format="bgr24")
         frame.pts = pts
         frame.time_base = fractions.Fraction(time_base).limit_denominator(1000000)
 

voicebot/uv.lock

@@ -22,7 +22,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "aiortc", specifier = ">=1.13.0" },
-    { name = "numpy", specifier = ">=2.3.2" },
+    { name = "numpy", specifier = "<2.3" },
     { name = "opencv-python", specifier = ">=4.11.0.86" },
     { name = "pydantic", specifier = ">=2.11.7" },
     { name = "websockets", specifier = ">=15.0.1" },
@@ -203,65 +203,40 @@ wheels = [
 
 [[package]]
 name = "numpy"
-version = "2.3.2"
+version = "2.2.6"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/37/7d/3fec4199c5ffb892bed55cff901e4f39a58c81df9c44c280499e92cad264/numpy-2.3.2.tar.gz", hash = "sha256:e0486a11ec30cdecb53f184d496d1c6a20786c81e55e41640270130056f8ee48", size = 20489306, upload-time = "2025-07-24T21:32:07.553Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/6d/745dd1c1c5c284d17725e5c802ca4d45cfc6803519d777f087b71c9f4069/numpy-2.3.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bc3186bea41fae9d8e90c2b4fb5f0a1f5a690682da79b92574d63f56b529080b", size = 20956420, upload-time = "2025-07-24T20:28:18.002Z" },
+    { url = "https://files.pythonhosted.org/packages/82/5d/c00588b6cf18e1da539b45d3598d3557084990dcc4331960c15ee776ee41/numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff", size = 20875348, upload-time = "2025-05-17T21:34:39.648Z" },
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 ]
 
 [[package]]

voicebot/whisper.py

@@ -1,6 +1,5 @@
 from typing import Any
 import librosa
-import numpy as np
 from logger import logger
 from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
 
@@ -37,7 +36,7 @@ pt_model: Any = AutoModelForSpeechSeq2Seq.from_pretrained(pretrained_model_name_
 pt_model.eval()  # type: ignore
 
 
-def extract_input_features(audio_array: np.ndarray, sampling_rate: int):
+def extract_input_features(audio_array: Any, sampling_rate: int) -> Any:
     """Extract input features from audio array and sampling rate."""
     input_features = processor(
         audio_array,
@@ -47,13 +46,22 @@ def extract_input_features(audio_array: np.ndarray, sampling_rate: int):
     return input_features
 
 
-def load_audio_file(file_path: str) -> tuple[np.ndarray, int]:
+def load_audio_file(file_path: str) -> tuple[Any, int]:
     """Load audio file from disk and return audio array and sampling rate."""
+    # Whisper models expect 16kHz sample rate
+    target_sample_rate = 16000
+    
     try:
-        # Load audio file using librosa
+        # Load audio file using librosa and resample to target rate
-        audio_array, sampling_rate = librosa.load(file_path, sr=None)
+        audio_array, original_sampling_rate = librosa.load(file_path, sr=None)  # type: ignore
-        logger.info(f"Loaded audio file: {file_path}, duration: {len(audio_array)/sampling_rate:.2f}s, sample rate: {sampling_rate}Hz")
+        logger.info(f"Loaded audio file: {file_path}, duration: {len(audio_array)/original_sampling_rate:.2f}s, original sample rate: {original_sampling_rate}Hz")  # type: ignore
-        return audio_array, int(sampling_rate)  # Ensure sampling_rate is int
+        
+        # Resample if necessary
+        if original_sampling_rate != target_sample_rate:
+            audio_array = librosa.resample(audio_array, orig_sr=original_sampling_rate, target_sr=target_sample_rate)  # type: ignore
+            logger.info(f"Resampled audio from {original_sampling_rate}Hz to {target_sample_rate}Hz")
+        
+        return audio_array, target_sample_rate  # type: ignore
     except Exception as e:
         logger.error(f"Error loading audio file {file_path}: {e}")
         raise