Fix final ruff linting issues
- Remove duplicate import in __init__.py - Clean up extra blank lines in gemini.py - Remove extra blank line in _create_single_response method
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@@ -1,3 +1,2 @@
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from .file_api import GeminiFileAPI
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from .gemini import GeminiMultimodalLiveLLMService
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from .file_api import GeminiFileAPI
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@@ -497,6 +497,7 @@ def parse_server_event(str):
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except Exception as e:
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print(f"Error parsing server event: {e}")
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class ContextWindowCompressionConfig(BaseModel):
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"""Configuration for context window compression.
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@@ -61,8 +61,6 @@ from pipecat.processors.aggregators.openai_llm_context import (
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.google.frames import LLMSearchOrigin, LLMSearchResponseFrame, LLMSearchResult
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from pipecat.services.llm_service import FunctionCallFromLLM, LLMService
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from pipecat.services.openai.llm import (
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OpenAIAssistantContextAggregator,
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OpenAIUserContextAggregator,
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@@ -74,11 +72,9 @@ from pipecat.utils.time import time_now_iso8601
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from pipecat.utils.tracing.service_decorators import traced_gemini_live, traced_stt
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from . import events
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from .audio_transcriber import AudioTranscriber
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from .file_api import GeminiFileAPI
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try:
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import websockets
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except ModuleNotFoundError as e:
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@@ -575,7 +571,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
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else {},
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"extra": params.extra if isinstance(params.extra, dict) else {},
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}
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# Initialize the File API client
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self.file_api = GeminiFileAPI(api_key=api_key, base_url=file_api_base_url)
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@@ -961,7 +957,7 @@ class GeminiMultimodalLiveLLMService(LLMService):
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await self._handle_evt_error(evt)
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# errors are fatal, so exit the receive loop
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return
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#
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#
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#
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@@ -1025,7 +1021,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
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async def _create_single_response(self, messages_list):
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"""Create a single response from a list of messages."""
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# Refactor to combine this logic with same logic in GeminiMultimodalLiveContext
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messages = []
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for item in messages_list:
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@@ -1202,7 +1197,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
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self._bot_text_buffer = ""
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self._llm_output_buffer = ""
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# Process grounding metadata if we have accumulated any
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if self._accumulated_grounding_metadata:
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await self._process_grounding_metadata(
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@@ -1295,7 +1289,6 @@ class GeminiMultimodalLiveLLMService(LLMService):
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# Collect text for tracing
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self._llm_output_buffer += text
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await self.push_frame(LLMTextFrame(text=text))
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await self.push_frame(TTSTextFrame(text=text))
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@@ -1335,13 +1328,13 @@ class GeminiMultimodalLiveLLMService(LLMService):
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)
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chunk_to_origin[index] = origin
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origins.append(origin)
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# Add grounding support results to the appropriate origins
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for support in grounding_metadata.groundingSupports:
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if support.segment and support.groundingChunkIndices:
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text = support.segment.text or ""
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confidence_scores = support.confidenceScores or []
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# Add this result to all origins referenced by this support
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for chunk_index in support.groundingChunkIndices:
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if chunk_index in chunk_to_origin:
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