examples(foundational): add 12d-describe-image-moondream
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@@ -240,8 +240,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Other
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### Other
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- Updated all vision 12-series foundational examples to load images from a file
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- Updated all vision 12-series foundational examples to load images from a file.
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and push `LLMMessagesAppendFrame` with the loaded image.
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- Added 14-series video examples for different services. These new examples
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- Added 14-series video examples for different services. These new examples
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request an image from the user camera through a function call.
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request an image from the user camera through a function call.
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122
examples/foundational/12d-describe-image-moondream.py
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122
examples/foundational/12d-describe-image-moondream.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from PIL import Image
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import UserImageRawFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.moondream.vision import MoondreamService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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load_dotenv(override=True)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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"webrtc": lambda: TransportParams(
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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vision = MoondreamService()
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pipeline = Pipeline(
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[
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vision, # Vision
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tts, # TTS
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transport.output(), # Transport bot output
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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if not runner_args.body:
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script_dir = os.path.dirname(__file__)
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runner_args.body = {
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"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
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"question": "Describe this image",
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}
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image_path = runner_args.body["image_path"]
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question = runner_args.body["question"]
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# Describe the image.
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image = Image.open(image_path)
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await task.queue_frames(
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[
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UserImageRawFrame(
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image=image.tobytes(),
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format="RGB",
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size=image.size,
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text=question,
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)
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]
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)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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@@ -49,15 +49,18 @@ EVAL_VISION_CAMERA = EvalConfig(
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eval="The user provides a cat description.",
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eval="The user provides a cat description.",
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)
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)
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EVAL_VISION_IMAGE = EvalConfig(
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prompt="Briefly describe this image.",
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def EVAL_VISION_IMAGE(*, eval_speaks_first: bool = False):
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eval="The user provides a cat description.",
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return EvalConfig(
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eval_speaks_first=True,
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prompt="Briefly describe this image.",
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runner_args_body={
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eval="The user provides a cat description.",
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"image_path": ASSETS_DIR / "cat.jpg",
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eval_speaks_first=eval_speaks_first,
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"question": "Briefly describe this image.",
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runner_args_body={
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},
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"image_path": ASSETS_DIR / "cat.jpg",
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)
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"question": "Briefly describe this image.",
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},
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)
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EVAL_VOICEMAIL = EvalConfig(
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EVAL_VOICEMAIL = EvalConfig(
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prompt="Please leave a message.",
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prompt="Please leave a message.",
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@@ -117,10 +120,11 @@ TESTS_07 = [
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]
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]
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TESTS_12 = [
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TESTS_12 = [
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("12-describe-image-openai.py", EVAL_VISION_IMAGE),
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("12-describe-image-openai.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
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("12a-describe-image-anthropic.py", EVAL_VISION_IMAGE),
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("12a-describe-image-anthropic.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
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("12b-describe-image-aws.py", EVAL_VISION_IMAGE),
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("12b-describe-image-aws.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
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("12c-describe-image-gemini-flash.py", EVAL_VISION_IMAGE),
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("12c-describe-image-gemini-flash.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
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("12d-describe-image-moondream.py", EVAL_VISION_IMAGE()),
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]
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]
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TESTS_14 = [
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TESTS_14 = [
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