193 lines
6.8 KiB
Python
193 lines
6.8 KiB
Python
#
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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 asyncio
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import io
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import os
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import re
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import shutil
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from mcp import StdioServerParameters
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from PIL import Image
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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Frame,
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FunctionCallResultFrame,
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URLImageRawFrame,
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)
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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.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.anthropic.llm import AnthropicLLMService
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.mcp_service import MCPClient
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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load_dotenv(override=True)
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class UrlToImageProcessor(FrameProcessor):
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def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
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super().__init__(**kwargs)
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self._aiohttp_session = aiohttp_session
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, FunctionCallResultFrame):
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await self.push_frame(frame, direction)
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image_url = self.extract_url(frame.result)
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if image_url:
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await self.run_image_process(image_url)
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# sometimes we get multiple image urls- process 1 at a time
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await asyncio.sleep(1)
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else:
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await self.push_frame(frame, direction)
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def extract_url(self, text: str):
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pattern = r"!\[[^\]]*\]\((https?://[^)]+\.(png|jpg|jpeg|PNG|JPG|JPEG))\)"
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match = re.search(pattern, text)
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if match:
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return match.group(1)
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return None
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async def run_image_process(self, image_url: str):
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try:
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logger.debug(f"handling image from url: '{image_url}'")
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async with self._aiohttp_session.get(image_url) as response:
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image_stream = io.BytesIO(await response.content.read())
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image = Image.open(image_stream)
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image = image.convert("RGB")
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frame = URLImageRawFrame(
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url=image_url, image=image.tobytes(), size=image.size, format="RGB"
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)
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await self.push_frame(frame)
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except Exception as e:
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error_msg = f"Error handling image url {image_url}: {str(e)}"
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logger.error(error_msg)
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async def run_bot(webrtc_connection: SmallWebRTCConnection):
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logger.info(f"Starting bot")
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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camera_out_enabled=True,
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camera_out_width=1024,
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camera_out_height=1024,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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# Create an HTTP session for API calls
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async with aiohttp.ClientSession() as session:
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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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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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
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)
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try:
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mcp = MCPClient(
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server_params=StdioServerParameters(
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command=shutil.which("npx"),
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args=["-y", "@programcomputer/nasa-mcp-server@latest"],
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# https://api.nasa.gov
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env={"NASA_API_KEY": os.getenv("NASA_API_KEY")},
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)
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)
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except Exception as e:
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logger.error(f"error setting up mcp")
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logger.exception("error trace:")
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mcp_image = UrlToImageProcessor(aiohttp_session=session)
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tools = await mcp.register_tools(llm)
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system = f"""
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You are a helpful LLM in a WebRTC call.
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Your goal is to demonstrate your capabilities in a succinct way.
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You have access to a number of tools provided by NASA MCP. Use any and all tools to help users.
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When asked for the astronomy picture of the day, PASS in NO date to the API.
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This ensures we get the latest picture available. If as specific date is asked for, you
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can pass in that date to the API.
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Your output will be converted to audio so don't include special characters in your answers.
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Respond to what the user said in a creative and helpful way.
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Don't overexplain what you are doing.
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Just respond with short sentences when you are carrying out tool calls.
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"""
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messages = [{"role": "system", "content": system}]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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context_aggregator.user(), # User spoken responses
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llm, # LLM
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tts, # TTS
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mcp_image, # URL image -> output
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses and tool context
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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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allow_interruptions=True,
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enable_metrics=True,
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),
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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: {client}")
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# Kick off the conversation.
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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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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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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from run import main
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main()
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