fixedup gpt4o and gemini-flash examples with interruption changes

Gemini text input works. We translate from OpenAILLMContext format
on the fly in the GoogleLLMService implementation. This commit also
implements image input (vision) in both the GoogleLLMService and in
the OpenAILLMService. Image input is a hack and needs to be revisited.
OpenAI expects images to be uploaded as base64-encoded JPEGs. Google
does not require the base64 encoding. Other than for images, we use
the OpenAI format as our standard, but base64-encoding the images
and then unencoding them in the GoogleLLMService feels wasteful.

examples/foundational/12a-describe-video-gemini-flash.py

@@ -0,0 +1,110 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.google import GoogleLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class UserImageRequester(FrameProcessor):
+
+    def __init__(self, participant_id: str | None = None):
+        super().__init__()
+        self._participant_id = participant_id
+
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
+        await self.push_frame(frame, direction)
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Describe participant video",
+            DailyParams(
+                audio_in_enabled=True,  # This is so Silero VAD can get audio data
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer()
+            )
+        )
+
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        user_response = UserResponseAggregator()
+
+        image_requester = UserImageRequester()
+
+        vision_aggregator = VisionImageFrameAggregator()
+
+        google = GoogleLLMService(model="gemini-1.5-flash-latest")
+
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await tts.say("Hi there! Feel free to ask me what I see.")
+            transport.capture_participant_video(participant["id"], framerate=0)
+            transport.capture_participant_transcription(participant["id"])
+            image_requester.set_participant_id(participant["id"])
+
+        pipeline = Pipeline([
+            transport.input(),
+            user_response,
+            image_requester,
+            vision_aggregator,
+            google,
+            tts,
+            transport.output()
+        ])
+
+        task = PipelineTask(pipeline, allow_interruptions=True)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

examples/foundational/12b-describe-video-gpt-4o.py

@@ -0,0 +1,113 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.user_response import UserResponseAggregator
+from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class UserImageRequester(FrameProcessor):
+
+    def __init__(self, participant_id: str | None = None):
+        super().__init__()
+        self._participant_id = participant_id
+
+    def set_participant_id(self, participant_id: str):
+        self._participant_id = participant_id
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        if self._participant_id and isinstance(frame, TextFrame):
+            await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
+        await self.push_frame(frame, direction)
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Describe participant video",
+            DailyParams(
+                audio_in_enabled=True,  # This is so Silero VAD can get audio data
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer()
+            )
+        )
+
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        user_response = UserResponseAggregator()
+
+        image_requester = UserImageRequester()
+
+        vision_aggregator = VisionImageFrameAggregator()
+
+        gpt4o = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o"
+        )
+
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await tts.say("Hi there! Feel free to ask me what I see.")
+            transport.capture_participant_video(participant["id"], framerate=0)
+            transport.capture_participant_transcription(participant["id"])
+            image_requester.set_participant_id(participant["id"])
+
+        pipeline = Pipeline([
+            transport.input(),
+            user_response,
+            image_requester,
+            vision_aggregator,
+            gpt4o,
+            tts,
+            transport.output()
+        ])
+
+        task = PipelineTask(pipeline, allow_interruptions=True)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

macos-py3.10-requirements.txt

@@ -1,32 +1,33 @@
+WARNING: --strip-extras is becoming the default in version 8.0.0. To silence this warning, either use --strip-extras to opt into the new default or use --no-strip-extras to retain the existing behavior.
 #
-# This file is autogenerated by pip-compile with Python 3.10
+# This file is autogenerated by pip-compile with Python 3.11
 # by the following command:
 #
 #    pip-compile --all-extras pyproject.toml
 #
 aiohttp==3.9.5
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 aiosignal==1.3.1
     # via aiohttp
 annotated-types==0.6.0
     # via pydantic
-anthropic==0.25.8
+anthropic==0.25.9
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 anyio==4.3.0
     # via
     #   anthropic
     #   httpx
     #   openai
-async-timeout==4.0.3
-    # via aiohttp
 attrs==23.2.0
     # via aiohttp
 av==12.0.0
     # via faster-whisper
 azure-cognitiveservices-speech==1.37.0
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 blinker==1.8.2
     # via flask
+cachetools==5.3.3
+    # via google-auth
 certifi==2024.2.2
     # via
     #   httpcore
@@ -41,19 +42,17 @@ coloredlogs==15.0.1
 ctranslate2==4.2.1
     # via faster-whisper
 daily-python==0.7.4
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 distro==1.9.0
     # via
     #   anthropic
     #   openai
 einops==0.8.0
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
-exceptiongroup==1.2.1
-    # via anyio
 fal-client==0.4.0
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 faster-whisper==1.0.2
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 filelock==3.14.0
     # via
     #   huggingface-hub
@@ -63,25 +62,58 @@ filelock==3.14.0
 flask==3.0.3
     # via
     #   flask-cors
-    #   pipecat (pyproject.toml)
+    #   pipecat-ai (pyproject.toml)
 flask-cors==4.0.1
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 flatbuffers==24.3.25
     # via onnxruntime
 frozenlist==1.4.1
     # via
     #   aiohttp
     #   aiosignal
-fsspec==2024.3.1
+fsspec==2024.5.0
     # via
     #   huggingface-hub
     #   torch
+google-ai-generativelanguage==0.6.3
+    # via google-generativeai
+google-api-core[grpc]==2.19.0
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-python-client
+    #   google-generativeai
+google-api-python-client==2.129.0
+    # via google-generativeai
+google-auth==2.29.0
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-core
+    #   google-api-python-client
+    #   google-auth-httplib2
+    #   google-generativeai
+google-auth-httplib2==0.2.0
+    # via google-api-python-client
+google-generativeai==0.5.3
+    # via pipecat-ai (pyproject.toml)
+googleapis-common-protos==1.63.0
+    # via
+    #   google-api-core
+    #   grpcio-status
 grpcio==1.63.0
-    # via pyht
+    # via
+    #   google-api-core
+    #   grpcio-status
+    #   pyht
+grpcio-status==1.62.2
+    # via google-api-core
 h11==0.14.0
     # via httpcore
 httpcore==1.0.5
     # via httpx
+httplib2==0.22.0
+    # via
+    #   google-api-python-client
+    #   google-auth-httplib2
 httpx==0.27.0
     # via
     #   anthropic
@@ -110,7 +142,7 @@ jinja2==3.1.4
     #   flask
     #   torch
 loguru==0.7.2
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 markupsafe==2.1.5
     # via
     #   jinja2
@@ -127,13 +159,13 @@ numpy==1.26.4
     # via
     #   ctranslate2
     #   onnxruntime
-    #   pipecat (pyproject.toml)
+    #   pipecat-ai (pyproject.toml)
     #   torchvision
     #   transformers
 onnxruntime==1.17.3
     # via faster-whisper
 openai==1.26.0
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 packaging==24.0
     # via
     #   huggingface-hub
@@ -141,37 +173,59 @@ packaging==24.0
     #   transformers
 pillow==10.3.0
     # via
-    #   pipecat (pyproject.toml)
+    #   pipecat-ai (pyproject.toml)
     #   torchvision
+proto-plus==1.23.0
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-core
 protobuf==4.25.3
     # via
+    #   google-ai-generativelanguage
+    #   google-api-core
+    #   google-generativeai
+    #   googleapis-common-protos
+    #   grpcio-status
     #   onnxruntime
+    #   proto-plus
     #   pyht
+pyasn1==0.6.0
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.4.0
+    # via google-auth
 pyaudio==0.2.14
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 pydantic==2.7.1
     # via
     #   anthropic
+    #   google-generativeai
     #   openai
 pydantic-core==2.18.2
     # via pydantic
 pyht==0.0.28
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
+pyparsing==3.1.2
+    # via httplib2
 python-dotenv==1.0.1
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 pyyaml==6.0.1
     # via
     #   ctranslate2
     #   huggingface-hub
     #   timm
     #   transformers
-regex==2024.5.10
+regex==2024.5.15
     # via transformers
 requests==2.31.0
     # via
+    #   google-api-core
     #   huggingface-hub
     #   pyht
     #   transformers
+rsa==4.9
+    # via google-auth
 safetensors==0.4.3
     # via
     #   timm
@@ -187,7 +241,7 @@ sympy==1.12
     #   onnxruntime
     #   torch
 timm==0.9.16
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 tokenizers==0.19.1
     # via
     #   anthropic
@@ -195,35 +249,38 @@ tokenizers==0.19.1
     #   transformers
 torch==2.3.0
     # via
-    #   pipecat (pyproject.toml)
+    #   pipecat-ai (pyproject.toml)
     #   timm
     #   torchaudio
     #   torchvision
 torchaudio==2.3.0
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 torchvision==0.18.0
     # via timm
 tqdm==4.66.4
     # via
+    #   google-generativeai
     #   huggingface-hub
     #   openai
     #   transformers
 transformers==4.40.2
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 typing-extensions==4.11.0
     # via
     #   anthropic
-    #   anyio
+    #   google-generativeai
     #   huggingface-hub
     #   openai
-    #   pipecat (pyproject.toml)
+    #   pipecat-ai (pyproject.toml)
     #   pydantic
     #   pydantic-core
     #   torch
+uritemplate==4.1.1
+    # via google-api-python-client
 urllib3==2.2.1
     # via requests
 websockets==12.0
-    # via pipecat (pyproject.toml)
+    # via pipecat-ai (pyproject.toml)
 werkzeug==3.0.3
     # via flask
 yarl==1.9.4

pyproject.toml

@@ -37,6 +37,7 @@ azure = [ "azure-cognitiveservices-speech~=1.37.0" ]
 daily = [ "daily-python~=0.7.4" ]
 examples = [ "python-dotenv~=1.0.0", "flask~=3.0.3", "flask_cors~=4.0.1" ]
 fal = [ "fal-client~=0.4.0" ]
+google = [ "google-generativeai~=0.5.3" ]
 fireworks = [ "openai~=1.26.0" ]
 local = [ "pyaudio~=0.2.0" ]
 moondream = [ "einops~=0.8.0", "timm~=0.9.16", "transformers~=4.40.2" ]

src/pipecat/processors/aggregators/openai_llm_context.py

@@ -5,10 +5,13 @@
 #
 
 from dataclasses import dataclass
+import io
 
 from typing import List
 
-from pipecat.frames.frames import Frame
+from PIL import Image
+
+from pipecat.frames.frames import Frame, VisionImageRawFrame
 
 from openai._types import NOT_GIVEN, NotGiven
 
@@ -43,6 +46,31 @@ class OpenAILLMContext:
             })
         return context
 
+    @staticmethod
+    def from_image_frame(frame: VisionImageRawFrame) -> "OpenAILLMContext":
+        """
+        For images, we are deviating from the OpenAI messages shape. OpenAI
+        expects images to be base64 encoded, but other vision models may not.
+        So we'll store the image as bytes and do the base64 encoding as needed
+        in the LLM service.
+        """
+        context = OpenAILLMContext()
+        buffer = io.BytesIO()
+        Image.frombytes(
+            frame.format,
+            frame.size,
+            frame.image
+        ).save(
+            buffer,
+            format="JPEG")
+        context.add_message({
+            "content": frame.text,
+            "role": "user",
+            "data": buffer.getvalue(),
+            "mime_type": "image/jpeg"
+        })
+        return context
+
     def add_message(self, message: ChatCompletionMessageParam):
         self.messages.append(message)
 

src/pipecat/services/google.py

@@ -0,0 +1,96 @@
+import google.generativeai as gai
+import google.ai.generativelanguage as glm
+import os
+import asyncio
+
+from typing import List
+
+from pipecat.frames.frames import (
+    Frame,
+    TextFrame,
+    VisionImageRawFrame,
+    LLMMessagesFrame,
+    LLMResponseStartFrame,
+    LLMResponseEndFrame)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_services import LLMService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext, OpenAILLMContextFrame
+
+from loguru import logger
+
+
+class GoogleLLMService(LLMService):
+    """This class implements inference with Google's AI models
+
+    This service translates internally from OpenAILLMContext to the messages format
+    expected by the Google AI model. We are using the OpenAILLMContext as a lingua
+    franca for all LLM services, so that it is easy to switch between different LLMs.
+    """
+
+    def __init__(self, model="gemini-1.5-flash-latest", api_key=None, **kwargs):
+        super().__init__(**kwargs)
+        self.model = model
+        gai.configure(api_key=api_key or os.environ["GOOGLE_API_KEY"])
+        self.create_client()
+
+    def create_client(self):
+        self._client = gai.GenerativeModel(self.model)
+
+    def _get_messages_from_openai_context(
+            self, context: OpenAILLMContext) -> List[glm.Content]:
+        openai_messages = context.get_messages()
+        google_messages = []
+
+        for message in openai_messages:
+            role = message["role"]
+            content = message["content"]
+            if role == "system":
+                role = "user"
+            elif role == "assistant":
+                role = "model"
+
+            parts = [glm.Part(text=content)]
+            if "mime_type" in message:
+                parts.append(
+                    glm.Part(inline_data=glm.Blob(
+                        mime_type=message["mime_type"],
+                        data=message["data"]
+                    )))
+            google_messages.append({"role": role, "parts": parts})
+
+        return google_messages
+
+    async def _async_generator_wrapper(self, sync_generator):
+        for item in sync_generator:
+            yield item
+            await asyncio.sleep(0)
+
+    async def _process_context(self, context: OpenAILLMContext):
+        try:
+            messages = self._get_messages_from_openai_context(context)
+
+            await self.push_frame(LLMResponseStartFrame())
+            response = self._client.generate_content(messages, stream=True)
+
+            async for chunk in self._async_generator_wrapper(response):
+                logger.debug(f"Pushing inference text: {chunk.text}")
+                await self.push_frame(TextFrame(chunk.text))
+
+            await self.push_frame(LLMResponseEndFrame())
+        except Exception as e:
+            logger.error(f"Exception: {e}")
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        context = None
+
+        if isinstance(frame, OpenAILLMContextFrame):
+            context: OpenAILLMContext = frame.context
+        elif isinstance(frame, LLMMessagesFrame):
+            context = OpenAILLMContext.from_messages(frame.messages)
+        elif isinstance(frame, VisionImageRawFrame):
+            context = OpenAILLMContext.from_image_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
+
+        if context:
+            await self._process_context(context)

src/pipecat/services/openai.py

@@ -8,6 +8,7 @@ import io
 import json
 import time
 import aiohttp
+import base64
 
 from PIL import Image
 
@@ -22,7 +23,8 @@ from pipecat.frames.frames import (
     LLMResponseEndFrame,
     LLMResponseStartFrame,
     TextFrame,
-    URLImageRawFrame
+    URLImageRawFrame,
+    VisionImageRawFrame
 )
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext, OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection
@@ -67,8 +69,21 @@ class BaseOpenAILLMService(LLMService):
         self, context: OpenAILLMContext
     ) -> AsyncStream[ChatCompletionChunk]:
         messages: List[ChatCompletionMessageParam] = context.get_messages()
-        messages_for_log = json.dumps(messages)
+
-        logger.debug(f"Generating chat: {messages_for_log}")
+        # base64 encode any images
+        for message in messages:
+            if message.get("mime_type") == "image/jpeg":
+                encoded_image = base64.b64encode(message["data"]).decode("utf-8")
+                text = message["content"]
+                message["content"] = [
+                    {"type": "text", "text": text},
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}}
+                ]
+                del message["data"]
+                del message["mime_type"]
+
+        # messages_for_log = json.dumps(messages)
+        # logger.debug(f"Generating chat: {messages_for_log}")
 
         start_time = time.time()
         chunks: AsyncStream[ChatCompletionChunk] = (
@@ -151,6 +166,8 @@ class BaseOpenAILLMService(LLMService):
             context: OpenAILLMContext = frame.context
         elif isinstance(frame, LLMMessagesFrame):
             context = OpenAILLMContext.from_messages(frame.messages)
+        elif isinstance(frame, VisionImageRawFrame):
+            context = OpenAILLMContext.from_image_frame(frame)
         else:
             await self.push_frame(frame, direction)