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feat: Stable & Hella Fast Podcast Agent with auto FFMPEG handling.
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
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commit
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12 changed files with 1132 additions and 1 deletions
3
.gitignore
vendored
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.gitignore
vendored
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.flashrank_cache*
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.flashrank_cache*
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podcasts/*
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@ -15,6 +15,9 @@ FAST_LLM="openai/gpt-4o-mini"
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STRATEGIC_LLM="openai/gpt-4o"
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LONG_CONTEXT_LLM="gemini/gemini-2.0-flash"
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#LiteLLM TTS Provider: https://docs.litellm.ai/docs/text_to_speech#supported-providers
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TTS_SERVICE="openai/tts-1"
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# Chosen LiteLLM Providers Keys
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OPENAI_API_KEY="sk-proj-iA"
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GEMINI_API_KEY="AIzaSyB6-1641124124124124124124124124124"
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8
surfsense_backend/app/agents/podcaster/__init__.py
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8
surfsense_backend/app/agents/podcaster/__init__.py
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"""New LangGraph Agent.
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This module defines a custom graph.
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"""
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from .graph import graph
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__all__ = ["graph"]
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28
surfsense_backend/app/agents/podcaster/configuration.py
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surfsense_backend/app/agents/podcaster/configuration.py
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"""Define the configurable parameters for the agent."""
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from __future__ import annotations
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from dataclasses import dataclass, fields
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from typing import Optional
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from langchain_core.runnables import RunnableConfig
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@dataclass(kw_only=True)
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class Configuration:
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"""The configuration for the agent."""
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# Changeme: Add configurable values here!
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# these values can be pre-set when you
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# create assistants (https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/)
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# and when you invoke the graph
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podcast_title: str
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@classmethod
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def from_runnable_config(
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cls, config: Optional[RunnableConfig] = None
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) -> Configuration:
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"""Create a Configuration instance from a RunnableConfig object."""
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configurable = (config.get("configurable") or {}) if config else {}
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_fields = {f.name for f in fields(cls) if f.init}
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return cls(**{k: v for k, v in configurable.items() if k in _fields})
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23
surfsense_backend/app/agents/podcaster/graph.py
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23
surfsense_backend/app/agents/podcaster/graph.py
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from langgraph.graph import StateGraph
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from .configuration import Configuration
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from .state import State
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from .nodes import create_merged_podcast_audio, create_podcast_transcript
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# Define a new graph
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workflow = StateGraph(State, config_schema=Configuration)
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# Add the node to the graph
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workflow.add_node("create_podcast_transcript", create_podcast_transcript)
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workflow.add_node("create_merged_podcast_audio", create_merged_podcast_audio)
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# Set the entrypoint as `call_model`
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workflow.add_edge("__start__", "create_podcast_transcript")
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workflow.add_edge("create_podcast_transcript", "create_merged_podcast_audio")
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workflow.add_edge("create_merged_podcast_audio", "__end__")
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# Compile the workflow into an executable graph
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graph = workflow.compile()
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graph.name = "Surfsense Podcaster" # This defines the custom name in LangSmith
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197
surfsense_backend/app/agents/podcaster/nodes.py
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197
surfsense_backend/app/agents/podcaster/nodes.py
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from typing import Any, Dict
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import json
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import os
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import uuid
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from pathlib import Path
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import asyncio
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.runnables import RunnableConfig
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from litellm import aspeech
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from ffmpeg.asyncio import FFmpeg
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from .configuration import Configuration
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from .state import PodcastTranscriptEntry, State, PodcastTranscripts
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from .prompts import get_podcast_generation_prompt
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from app.config import config as app_config
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async def create_podcast_transcript(state: State, config: RunnableConfig) -> Dict[str, Any]:
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"""Each node does work."""
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# Initialize LLM
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llm = app_config.long_context_llm_instance
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# Get the prompt
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prompt = get_podcast_generation_prompt()
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# Create the messages
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messages = [
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SystemMessage(content=prompt),
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HumanMessage(content=state.source_content)
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]
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# Generate the podcast transcript
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llm_response = await llm.ainvoke(messages)
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# First try the direct approach
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try:
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podcast_transcript = PodcastTranscripts.model_validate(json.loads(llm_response.content))
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except (json.JSONDecodeError, ValueError) as e:
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print(f"Direct JSON parsing failed, trying fallback approach: {str(e)}")
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# Fallback: Parse the JSON response manually
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try:
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# Extract JSON content from the response
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content = llm_response.content
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# Find the JSON in the content (handle case where LLM might add additional text)
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json_start = content.find('{')
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json_end = content.rfind('}') + 1
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if json_start >= 0 and json_end > json_start:
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json_str = content[json_start:json_end]
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# Parse the JSON string
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parsed_data = json.loads(json_str)
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# Convert to Pydantic model
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podcast_transcript = PodcastTranscripts.model_validate(parsed_data)
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print(f"Successfully parsed podcast transcript using fallback approach")
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else:
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# If JSON structure not found, raise a clear error
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error_message = f"Could not find valid JSON in LLM response. Raw response: {content}"
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print(error_message)
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raise ValueError(error_message)
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except (json.JSONDecodeError, ValueError) as e2:
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# Log the error and re-raise it
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error_message = f"Error parsing LLM response (fallback also failed): {str(e2)}"
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print(f"Error parsing LLM response: {str(e2)}")
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print(f"Raw response: {llm_response.content}")
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raise
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return {
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"podcast_transcript": podcast_transcript.podcast_transcripts
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}
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async def create_merged_podcast_audio(state: State, config: RunnableConfig) -> Dict[str, Any]:
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"""Generate audio for each transcript and merge them into a single podcast file."""
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configuration = Configuration.from_runnable_config(config)
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starting_transcript = PodcastTranscriptEntry(
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speaker_id=1,
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dialog=f"Welcome to {configuration.podcast_title} Podcast."
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)
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transcript = state.podcast_transcript
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# Merge the starting transcript with the podcast transcript
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# Check if transcript is a PodcastTranscripts object or already a list
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if hasattr(transcript, 'podcast_transcripts'):
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transcript_entries = transcript.podcast_transcripts
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else:
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transcript_entries = transcript
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merged_transcript = [starting_transcript] + transcript_entries
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# Create a temporary directory for audio files
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temp_dir = Path("temp_audio")
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temp_dir.mkdir(exist_ok=True)
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# Generate a unique session ID for this podcast
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session_id = str(uuid.uuid4())
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output_path = f"podcasts/{session_id}_podcast.mp3"
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os.makedirs("podcasts", exist_ok=True)
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# Map of speaker_id to voice
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voice_mapping = {
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0: "alloy", # Default/intro voice
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1: "echo", # First speaker
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# 2: "fable", # Second speaker
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# 3: "onyx", # Third speaker
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# 4: "nova", # Fourth speaker
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# 5: "shimmer" # Fifth speaker
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}
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# Generate audio for each transcript segment
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audio_files = []
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async def generate_speech_for_segment(segment, index):
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# Handle both dictionary and PodcastTranscriptEntry objects
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if hasattr(segment, 'speaker_id'):
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speaker_id = segment.speaker_id
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dialog = segment.dialog
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else:
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speaker_id = segment.get("speaker_id", 0)
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dialog = segment.get("dialog", "")
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# Select voice based on speaker_id
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voice = voice_mapping.get(speaker_id, "alloy")
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# Generate a unique filename for this segment
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filename = f"{temp_dir}/{session_id}_{index}.mp3"
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try:
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# Generate speech using litellm
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response = await aspeech(
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model=app_config.TTS_SERVICE,
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voice=voice,
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input=dialog,
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max_retries=2,
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timeout=600,
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)
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# Save the audio to a file - use proper streaming method
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with open(filename, 'wb') as f:
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f.write(response.content)
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return filename
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except Exception as e:
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print(f"Error generating speech for segment {index}: {str(e)}")
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raise
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# Generate all audio files concurrently
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tasks = [generate_speech_for_segment(segment, i) for i, segment in enumerate(merged_transcript)]
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audio_files = await asyncio.gather(*tasks)
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# Merge audio files using ffmpeg
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try:
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# Create FFmpeg instance with the first input
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ffmpeg = FFmpeg().option("y")
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# Add each audio file as input
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for audio_file in audio_files:
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ffmpeg = ffmpeg.input(audio_file)
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# Configure the concatenation and output
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filter_complex = []
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for i in range(len(audio_files)):
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filter_complex.append(f"[{i}:0]")
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filter_complex_str = "".join(filter_complex) + f"concat=n={len(audio_files)}:v=0:a=1[outa]"
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ffmpeg = ffmpeg.option("filter_complex", filter_complex_str)
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ffmpeg = ffmpeg.output(output_path, map="[outa]")
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# Execute FFmpeg
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await ffmpeg.execute()
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print(f"Successfully created podcast audio: {output_path}")
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except Exception as e:
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print(f"Error merging audio files: {str(e)}")
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raise
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finally:
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# Clean up temporary files
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for audio_file in audio_files:
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try:
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os.remove(audio_file)
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except:
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pass
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return {
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"podcast_transcript": merged_transcript,
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"final_podcast_file_path": output_path
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}
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111
surfsense_backend/app/agents/podcaster/prompts.py
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111
surfsense_backend/app/agents/podcaster/prompts.py
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import datetime
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def get_podcast_generation_prompt():
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return f"""
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Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
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<podcast_generation_system>
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You are a master podcast scriptwriter, adept at transforming diverse input content into a lively, engaging, and natural-sounding conversation between two distinct podcast hosts. Your primary objective is to craft authentic, flowing dialogue that captures the spontaneity and chemistry of a real podcast discussion, completely avoiding any hint of robotic scripting or stiff formality. Think dynamic interplay, not just information delivery.
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<input>
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- '<source_content>': A block of text containing the information to be discussed in the podcast. This could be research findings, an article summary, a detailed outline, user chat history related to the topic, or any other relevant raw information. The content might be unstructured but serves as the factual basis for the podcast dialogue.
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</input>
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<output_format>
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A JSON object containing the podcast transcript with alternating speakers:
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{{
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"podcast_transcripts": [
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{{
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"speaker_id": 0,
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"dialog": "Speaker 0 dialog here"
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}},
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{{
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"speaker_id": 1,
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"dialog": "Speaker 1 dialog here"
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}},
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{{
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"speaker_id": 0,
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"dialog": "Speaker 0 dialog here"
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}},
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{{
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"speaker_id": 1,
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"dialog": "Speaker 1 dialog here"
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}}
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]
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}}
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</output_format>
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<guidelines>
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1. **Establish Distinct & Consistent Host Personas:**
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* **Speaker 0 (Lead Host):** Drives the conversation forward, introduces segments, poses key questions derived from the source content, and often summarizes takeaways. Maintain a guiding, clear, and engaging tone.
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* **Speaker 1 (Co-Host/Expert):** Offers deeper insights, provides alternative viewpoints or elaborations on the source content, asks clarifying or challenging questions, and shares relevant anecdotes or examples. Adopt a complementary tone (e.g., analytical, enthusiastic, reflective, slightly skeptical).
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* **Consistency is Key:** Ensure each speaker maintains their distinct voice, vocabulary choice, sentence structure, and perspective throughout the entire script. Avoid having them sound interchangeable. Their interaction should feel like a genuine partnership.
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2. **Craft Natural & Dynamic Dialogue:**
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* **Emulate Real Conversation:** Use contractions (e.g., "don't", "it's"), interjections ("Oh!", "Wow!", "Hmm"), discourse markers ("you know", "right?", "well"), and occasional natural pauses or filler words. Avoid overly formal language or complex sentence structures typical of written text.
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* **Foster Interaction & Chemistry:** Write dialogue where speakers genuinely react *to each other*. They should build on points ("Exactly, and that reminds me..."), ask follow-up questions ("Could you expand on that?"), express agreement/disagreement respectfully ("That's a fair point, but have you considered...?"), and show active listening.
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* **Vary Rhythm & Pace:** Mix short, punchy lines with longer, more explanatory ones. Vary sentence beginnings. Use questions to break up exposition. The rhythm should feel spontaneous, not monotonous.
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* **Inject Personality & Relatability:** Allow for appropriate humor, moments of surprise or curiosity, brief personal reflections ("I actually experienced something similar..."), or relatable asides that fit the hosts' personas and the topic. Lightly reference past discussions if it enhances context ("Remember last week when we touched on...?").
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3. **Structure for Flow and Listener Engagement:**
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* **Natural Beginning:** Start with dialogue that flows naturally after an introduction (which will be added manually). Avoid redundant greetings or podcast name mentions since these will be added separately.
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* **Logical Progression & Signposting:** Guide the listener through the information smoothly. Use clear transitions to link different ideas or segments ("So, now that we've covered X, let's dive into Y...", "That actually brings me to another key finding..."). Ensure topics flow logically from one to the next.
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* **Meaningful Conclusion:** Summarize the key takeaways or main points discussed, reinforcing the core message derived from the source content. End with a final thought, a lingering question for the audience, or a brief teaser for what's next, providing a sense of closure. Avoid abrupt endings.
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4. **Integrate Source Content Seamlessly & Accurately:**
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* **Translate, Don't Recite:** Rephrase information from the `<source_content>` into conversational language suitable for each host's persona. Avoid directly copying dense sentences or technical jargon without explanation. The goal is discussion, not narration.
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* **Explain & Contextualize:** Use analogies, simple examples, storytelling, or have one host ask clarifying questions (acting as a listener surrogate) to break down complex ideas from the source.
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* **Weave Information Naturally:** Integrate facts, data, or key points from the source *within* the dialogue, not as standalone, undigested blocks. Attribute information conversationally where appropriate ("The research mentioned...", "Apparently, the key factor is...").
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* **Balance Depth & Accessibility:** Ensure the conversation is informative and factually accurate based on the source content, but prioritize clear communication and engaging delivery over exhaustive technical detail. Make it understandable and interesting for a general audience.
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5. **Length & Pacing:**
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* **Six-Minute Duration:** Create a transcript that, when read at a natural speaking pace, would result in approximately 6 minutes of audio. Typically, this means around 1000 words total (based on average speaking rate of 150 words per minute).
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* **Concise Speaking Turns:** Keep most speaking turns relatively brief and focused. Aim for a natural back-and-forth rhythm rather than extended monologues.
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* **Essential Content Only:** Prioritize the most important information from the source content. Focus on quality over quantity, ensuring every line contributes meaningfully to the topic.
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</guidelines>
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<examples>
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Input: "Quantum computing uses quantum bits or qubits which can exist in multiple states simultaneously due to superposition."
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Output:
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{{
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"podcast_transcripts": [
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{{
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"speaker_id": 0,
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"dialog": "Today we're diving into the mind-bending world of quantum computing. You know, this is a topic I've been excited to cover for weeks."
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}},
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{{
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"speaker_id": 1,
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"dialog": "Same here! And I know our listeners have been asking for it. But I have to admit, the concept of quantum computing makes my head spin a little. Can we start with the basics?"
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}},
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{{
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"speaker_id": 0,
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"dialog": "Absolutely. So regular computers use bits, right? Little on-off switches that are either 1 or 0. But quantum computers use something called qubits, and this is where it gets fascinating."
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}},
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{{
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"speaker_id": 1,
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"dialog": "Wait, what makes qubits so special compared to regular bits?"
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}},
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{{
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"speaker_id": 0,
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"dialog": "The magic is in something called superposition. These qubits can exist in multiple states at the same time, not just 1 or 0."
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}},
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{{
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"speaker_id": 1,
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"dialog": "That sounds impossible! How would you even picture that?"
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}},
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{{
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"speaker_id": 0,
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"dialog": "Think of it like a coin spinning in the air. Before it lands, is it heads or tails?"
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}},
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{{
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"speaker_id": 1,
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"dialog": "Well, it's... neither? Or I guess both, until it lands? Oh, I think I see where you're going with this."
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}}
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]
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}}
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</examples>
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Transform the source material into a lively and engaging podcast conversation. Craft dialogue that showcases authentic host chemistry and natural interaction (including occasional disagreement, building on points, or asking follow-up questions). Use varied speech patterns reflecting real human conversation, ensuring the final script effectively educates *and* entertains the listener while keeping within a 3-minute audio duration.
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</podcast_generation_system>
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"""
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38
surfsense_backend/app/agents/podcaster/state.py
Normal file
38
surfsense_backend/app/agents/podcaster/state.py
Normal file
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"""Define the state structures for the agent."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import List, Optional
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from pydantic import BaseModel, Field
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class PodcastTranscriptEntry(BaseModel):
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"""
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Represents a single entry in a podcast transcript.
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"""
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speaker_id: int = Field(..., description="The ID of the speaker (0 or 1)")
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dialog: str = Field(..., description="The dialog text spoken by the speaker")
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class PodcastTranscripts(BaseModel):
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"""
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Represents the full podcast transcript structure.
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"""
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podcast_transcripts: List[PodcastTranscriptEntry] = Field(
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...,
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description="List of transcript entries with alternating speakers"
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)
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@dataclass
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class State:
|
||||
"""Defines the input state for the agent, representing a narrower interface to the outside world.
|
||||
|
||||
This class is used to define the initial state and structure of incoming data.
|
||||
See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
|
||||
for more information.
|
||||
"""
|
||||
|
||||
source_content: str
|
||||
podcast_transcript: Optional[List[PodcastTranscriptEntry]] = None
|
||||
final_podcast_file_path: Optional[str] = None
|
||||
474
surfsense_backend/app/agents/podcaster/test_podcaster.py
Normal file
474
surfsense_backend/app/agents/podcaster/test_podcaster.py
Normal file
|
|
@ -0,0 +1,474 @@
|
|||
#!/usr/bin/env python
|
||||
"""
|
||||
Test script for the Surfsense Podcaster agent.
|
||||
Run this directly from VS Code to test the Podcaster agent.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Add the project root to the Python path
|
||||
project_root = str(Path(__file__).resolve().parent.parent.parent.parent)
|
||||
if project_root not in sys.path:
|
||||
sys.path.insert(0, project_root)
|
||||
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
|
||||
# Now import modules using absolute imports
|
||||
from app.agents.podcaster.graph import graph
|
||||
from app.agents.podcaster.state import State
|
||||
|
||||
|
||||
async def test_podcaster_agent():
|
||||
"""Test the Podcaster agent with a sample input."""
|
||||
|
||||
# Print banner
|
||||
print("=" * 80)
|
||||
print("SURFSENSE PODCASTER AGENT TEST")
|
||||
print("=" * 80)
|
||||
|
||||
# Sample input for testing
|
||||
sample_source_content = """
|
||||
<h1 align="center">Deep-Live-Cam</h1>
|
||||
|
||||
<p align="center">
|
||||
Real-time face swap and video deepfake with a single click and only a single image.
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://trendshift.io/repositories/11395" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11395" alt="hacksider%2FDeep-Live-Cam | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img src="media/demo.gif" alt="Demo GIF" width="800">
|
||||
</p>
|
||||
|
||||
## Disclaimer
|
||||
|
||||
This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.
|
||||
|
||||
We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.
|
||||
|
||||
- Ethical Use: Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online.
|
||||
|
||||
- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
|
||||
|
||||
- Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.
|
||||
|
||||
- User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.
|
||||
|
||||
By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.
|
||||
|
||||
Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
|
||||
|
||||
## Exclusive v2.0 Quick Start - Pre-built (Windows)
|
||||
|
||||
<a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
|
||||
|
||||
##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU.
|
||||
|
||||
###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually. This will be 60 days ahead on the open source version.
|
||||
|
||||
## TLDR; Live Deepfake in just 3 Clicks
|
||||
|
||||
1. Select a face
|
||||
2. Select which camera to use
|
||||
3. Press live!
|
||||
|
||||
## Features & Uses - Everything is in real-time
|
||||
|
||||
### Mouth Mask
|
||||
|
||||
**Retain your original mouth for accurate movement using Mouth Mask**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/ludwig.gif" alt="resizable-gif">
|
||||
</p>
|
||||
|
||||
### Face Mapping
|
||||
|
||||
**Use different faces on multiple subjects simultaneously**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/streamers.gif" alt="face_mapping_source">
|
||||
</p>
|
||||
|
||||
### Your Movie, Your Face
|
||||
|
||||
**Watch movies with any face in real-time**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/movie.gif" alt="movie">
|
||||
</p>
|
||||
|
||||
### Live Show
|
||||
|
||||
**Run Live shows and performances**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/live_show.gif" alt="show">
|
||||
</p>
|
||||
|
||||
### Memes
|
||||
|
||||
**Create Your Most Viral Meme Yet**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/meme.gif" alt="show" width="450">
|
||||
<br>
|
||||
<sub>Created using Many Faces feature in Deep-Live-Cam</sub>
|
||||
</p>
|
||||
|
||||
### Omegle
|
||||
|
||||
**Surprise people on Omegle**
|
||||
|
||||
<p align="center">
|
||||
<video src="https://github.com/user-attachments/assets/2e9b9b82-fa04-4b70-9f56-b1f68e7672d0" width="450" controls></video>
|
||||
</p>
|
||||
|
||||
## Installation (Manual)
|
||||
|
||||
**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**
|
||||
|
||||
<details>
|
||||
<summary>Click to see the process</summary>
|
||||
|
||||
### Installation
|
||||
|
||||
This is more likely to work on your computer but will be slower as it utilizes the CPU.
|
||||
|
||||
**1. Set up Your Platform**
|
||||
|
||||
- Python (3.10 recommended)
|
||||
- pip
|
||||
- git
|
||||
- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
|
||||
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
|
||||
|
||||
**2. Clone the Repository**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/hacksider/Deep-Live-Cam.git
|
||||
cd Deep-Live-Cam
|
||||
```
|
||||
|
||||
**3. Download the Models**
|
||||
|
||||
1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
|
||||
2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
|
||||
|
||||
Place these files in the "**models**" folder.
|
||||
|
||||
**4. Install Dependencies**
|
||||
|
||||
We highly recommend using a `venv` to avoid issues.
|
||||
|
||||
|
||||
For Windows:
|
||||
```bash
|
||||
python -m venv venv
|
||||
venv\Scripts\activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
For Linux:
|
||||
```bash
|
||||
# Ensure you use the installed Python 3.10
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**For macOS:**
|
||||
|
||||
Apple Silicon (M1/M2/M3) requires specific setup:
|
||||
|
||||
```bash
|
||||
# Install Python 3.10 (specific version is important)
|
||||
brew install python@3.10
|
||||
|
||||
# Install tkinter package (required for the GUI)
|
||||
brew install python-tk@3.10
|
||||
|
||||
# Create and activate virtual environment with Python 3.10
|
||||
python3.10 -m venv venv
|
||||
source venv/bin/activate
|
||||
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
** In case something goes wrong and you need to reinstall the virtual environment **
|
||||
|
||||
```bash
|
||||
# Deactivate the virtual environment
|
||||
rm -rf venv
|
||||
|
||||
# Reinstall the virtual environment
|
||||
python -m venv venv
|
||||
source venv/bin/activate
|
||||
|
||||
# install the dependencies again
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
|
||||
|
||||
### GPU Acceleration
|
||||
|
||||
**CUDA Execution Provider (Nvidia)**
|
||||
|
||||
1. Install [CUDA Toolkit 11.8.0](https://developer.nvidia.com/cuda-11-8-0-download-archive)
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-gpu
|
||||
pip install onnxruntime-gpu==1.16.3
|
||||
```
|
||||
|
||||
3. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider cuda
|
||||
```
|
||||
|
||||
**CoreML Execution Provider (Apple Silicon)**
|
||||
|
||||
Apple Silicon (M1/M2/M3) specific installation:
|
||||
|
||||
1. Make sure you've completed the macOS setup above using Python 3.10.
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-silicon
|
||||
pip install onnxruntime-silicon==1.13.1
|
||||
```
|
||||
|
||||
3. Usage (important: specify Python 3.10):
|
||||
|
||||
```bash
|
||||
python3.10 run.py --execution-provider coreml
|
||||
```
|
||||
|
||||
**Important Notes for macOS:**
|
||||
- You **must** use Python 3.10, not newer versions like 3.11 or 3.13
|
||||
- Always run with `python3.10` command not just `python` if you have multiple Python versions installed
|
||||
- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.10`
|
||||
- If you get model loading errors, check that your models are in the correct folder
|
||||
- If you encounter conflicts with other Python versions, consider uninstalling them:
|
||||
```bash
|
||||
# List all installed Python versions
|
||||
brew list | grep python
|
||||
|
||||
# Uninstall conflicting versions if needed
|
||||
brew uninstall --ignore-dependencies python@3.11 python@3.13
|
||||
|
||||
# Keep only Python 3.10
|
||||
brew cleanup
|
||||
```
|
||||
|
||||
**CoreML Execution Provider (Apple Legacy)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-coreml
|
||||
pip install onnxruntime-coreml==1.13.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider coreml
|
||||
```
|
||||
|
||||
**DirectML Execution Provider (Windows)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-directml
|
||||
pip install onnxruntime-directml==1.15.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider directml
|
||||
```
|
||||
|
||||
**OpenVINO™ Execution Provider (Intel)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-openvino
|
||||
pip install onnxruntime-openvino==1.15.0
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider openvino
|
||||
```
|
||||
</details>
|
||||
|
||||
## Usage
|
||||
|
||||
**1. Image/Video Mode**
|
||||
|
||||
- Execute `python run.py`.
|
||||
- Choose a source face image and a target image/video.
|
||||
- Click "Start".
|
||||
- The output will be saved in a directory named after the target video.
|
||||
|
||||
**2. Webcam Mode**
|
||||
|
||||
- Execute `python run.py`.
|
||||
- Select a source face image.
|
||||
- Click "Live".
|
||||
- Wait for the preview to appear (10-30 seconds).
|
||||
- Use a screen capture tool like OBS to stream.
|
||||
- To change the face, select a new source image.
|
||||
|
||||
## Tips and Tricks
|
||||
|
||||
Check out these helpful guides to get the most out of Deep-Live-Cam:
|
||||
|
||||
- [Unlocking the Secrets to the Perfect Deepfake Image](https://deeplivecam.net/index.php/blog/tips-and-tricks/unlocking-the-secrets-to-the-perfect-deepfake-image) - Learn how to create the best deepfake with full head coverage
|
||||
- [Video Call with DeepLiveCam](https://deeplivecam.net/index.php/blog/tips-and-tricks/video-call-with-deeplivecam) - Make your meetings livelier by using DeepLiveCam with OBS and meeting software
|
||||
- [Have a Special Guest!](https://deeplivecam.net/index.php/blog/tips-and-tricks/have-a-special-guest) - Tutorial on how to use face mapping to add special guests to your stream
|
||||
- [Watch Deepfake Movies in Realtime](https://deeplivecam.net/index.php/blog/tips-and-tricks/watch-deepfake-movies-in-realtime) - See yourself star in any video without processing the video
|
||||
- [Better Quality without Sacrificing Speed](https://deeplivecam.net/index.php/blog/tips-and-tricks/better-quality-without-sacrificing-speed) - Tips for achieving better results without impacting performance
|
||||
- [Instant Vtuber!](https://deeplivecam.net/index.php/blog/tips-and-tricks/instant-vtuber) - Create a new persona/vtuber easily using Metahuman Creator
|
||||
|
||||
Visit our [official blog](https://deeplivecam.net/index.php/blog/tips-and-tricks) for more tips and tutorials.
|
||||
|
||||
## Command Line Arguments (Unmaintained)
|
||||
|
||||
```
|
||||
options:
|
||||
-h, --help show this help message and exit
|
||||
-s SOURCE_PATH, --source SOURCE_PATH select a source image
|
||||
-t TARGET_PATH, --target TARGET_PATH select a target image or video
|
||||
-o OUTPUT_PATH, --output OUTPUT_PATH select output file or directory
|
||||
--frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...] frame processors (choices: face_swapper, face_enhancer, ...)
|
||||
--keep-fps keep original fps
|
||||
--keep-audio keep original audio
|
||||
--keep-frames keep temporary frames
|
||||
--many-faces process every face
|
||||
--map-faces map source target faces
|
||||
--mouth-mask mask the mouth region
|
||||
--video-encoder {libx264,libx265,libvpx-vp9} adjust output video encoder
|
||||
--video-quality [0-51] adjust output video quality
|
||||
--live-mirror the live camera display as you see it in the front-facing camera frame
|
||||
--live-resizable the live camera frame is resizable
|
||||
--max-memory MAX_MEMORY maximum amount of RAM in GB
|
||||
--execution-provider {cpu} [{cpu} ...] available execution provider (choices: cpu, ...)
|
||||
--execution-threads EXECUTION_THREADS number of execution threads
|
||||
-v, --version show program's version number and exit
|
||||
```
|
||||
|
||||
Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.
|
||||
|
||||
## Press
|
||||
|
||||
**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**
|
||||
|
||||
- [*"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - Ars Technica
|
||||
- [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
|
||||
- [*"This free AI tool lets you become anyone during video-calls"*](https://www.newsbytesapp.com/news/science/deep-live-cam-ai-impersonation-tool-goes-viral/story) - NewsBytes
|
||||
- [*"OK, this viral AI live stream software is truly terrifying"*](https://www.creativebloq.com/ai/ok-this-viral-ai-live-stream-software-is-truly-terrifying) - Creative Bloq
|
||||
- [*"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - PetaPixel
|
||||
- [*"Deep-Live-Cam Uses AI to Transform Your Face in Real-Time, Celebrities Included"*](https://www.techeblog.com/deep-live-cam-ai-transform-face/) - TechEBlog
|
||||
- [*"An AI tool that "makes you look like anyone" during a video call is going viral online"*](https://telegrafi.com/en/a-tool-that-makes-you-look-like-anyone-during-a-video-call-is-going-viral-on-the-Internet/) - Telegrafi
|
||||
- [*"This Deepfake Tool Turning Images Into Livestreams is Topping the GitHub Charts"*](https://decrypt.co/244565/this-deepfake-tool-turning-images-into-livestreams-is-topping-the-github-charts) - Emerge
|
||||
- [*"New Real-Time Face-Swapping AI Allows Anyone to Mimic Famous Faces"*](https://www.digitalmusicnews.com/2024/08/15/face-swapping-ai-real-time-mimic/) - Digital Music News
|
||||
- [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
|
||||
- [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
|
||||
- [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
|
||||
|
||||
## Credits
|
||||
|
||||
- [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
|
||||
- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
|
||||
- [havok2-htwo](https://github.com/havok2-htwo): for sharing the code for webcam
|
||||
- [GosuDRM](https://github.com/GosuDRM): for the open version of roop
|
||||
- [pereiraroland26](https://github.com/pereiraroland26): Multiple faces support
|
||||
- [vic4key](https://github.com/vic4key): For supporting/contributing to this project
|
||||
- [kier007](https://github.com/kier007): for improving the user experience
|
||||
- [qitianai](https://github.com/qitianai): for multi-lingual support
|
||||
- and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
|
||||
- Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
|
||||
- All the wonderful users who helped make this project go viral by starring the repo ❤️
|
||||
|
||||
[](https://github.com/hacksider/Deep-Live-Cam/stargazers)
|
||||
|
||||
## Contributions
|
||||
|
||||
|
||||
|
||||
## Stars to the Moon 🚀
|
||||
|
||||
<a href="https://star-history.com/#hacksider/deep-live-cam&Date">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date&theme=dark" />
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
</picture>
|
||||
</a>
|
||||
"""
|
||||
|
||||
# Create initial state
|
||||
initial_state = State(
|
||||
source_content=sample_source_content
|
||||
)
|
||||
|
||||
# Configuration with podcast title
|
||||
config = RunnableConfig(
|
||||
configurable={
|
||||
"podcast_title": "SurfSense"
|
||||
}
|
||||
)
|
||||
|
||||
# Create 'podcasts' directory if it doesn't exist
|
||||
os.makedirs("podcasts", exist_ok=True)
|
||||
|
||||
# Run the agent
|
||||
print("\nRunning Podcaster agent...\n")
|
||||
|
||||
try:
|
||||
# Execute the graph
|
||||
final_state = await graph.ainvoke(initial_state, config)
|
||||
|
||||
# Print results
|
||||
print("\nAgent execution completed successfully!")
|
||||
print(f"Generated podcast file: {final_state.get('final_podcast_file_path', 'No audio file generated')}")
|
||||
|
||||
# If transcript was generated, show a preview
|
||||
if final_state.get('podcast_transcript'):
|
||||
print("\nPodcast transcript preview (first 3 entries):")
|
||||
for i, entry in enumerate(final_state.get('podcast_transcript')[:3]):
|
||||
# Handle both dictionary and PodcastTranscriptEntry objects
|
||||
if hasattr(entry, 'speaker_id'):
|
||||
speaker_id = entry.speaker_id
|
||||
dialog = entry.dialog
|
||||
else:
|
||||
speaker_id = entry.get('speaker_id', 0)
|
||||
dialog = entry.get('dialog', '')
|
||||
|
||||
print(f"Speaker {speaker_id}: {dialog[:50]}...")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\nError running the agent: {str(e)}")
|
||||
raise
|
||||
|
||||
print("\nTest completed!")
|
||||
return final_state
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Run the test function
|
||||
final_state = asyncio.run(test_podcaster_agent())
|
||||
|
|
@ -1,10 +1,12 @@
|
|||
import os
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
|
||||
from chonkie import AutoEmbeddings, CodeChunker, RecursiveChunker
|
||||
from dotenv import load_dotenv
|
||||
from langchain_community.chat_models import ChatLiteLLM
|
||||
from rerankers import Reranker
|
||||
from litellm import speech
|
||||
|
||||
# Get the base directory of the project
|
||||
BASE_DIR = Path(__file__).resolve().parent.parent.parent
|
||||
|
|
@ -13,8 +15,27 @@ env_file = BASE_DIR / ".env"
|
|||
load_dotenv(env_file)
|
||||
|
||||
|
||||
def is_ffmpeg_installed():
|
||||
"""
|
||||
Check if ffmpeg is installed on the current system.
|
||||
|
||||
Returns:
|
||||
bool: True if ffmpeg is installed, False otherwise.
|
||||
"""
|
||||
return shutil.which("ffmpeg") is not None
|
||||
|
||||
|
||||
|
||||
class Config:
|
||||
# Check if ffmpeg is installed
|
||||
if not is_ffmpeg_installed():
|
||||
import static_ffmpeg
|
||||
# ffmpeg installed on first call to add_paths(), threadsafe.
|
||||
static_ffmpeg.add_paths()
|
||||
# check if ffmpeg is installed again
|
||||
if not is_ffmpeg_installed():
|
||||
raise ValueError("FFmpeg is not installed on the system. Please install it to use the Surfsense Podcaster.")
|
||||
|
||||
# Database
|
||||
DATABASE_URL = os.getenv("DATABASE_URL")
|
||||
|
||||
|
|
@ -61,6 +82,9 @@ class Config:
|
|||
# Firecrawl API Key
|
||||
FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", None)
|
||||
|
||||
# Litellm TTS Configuration
|
||||
TTS_SERVICE = os.getenv("TTS_SERVICE")
|
||||
|
||||
# Validation Checks
|
||||
# Check embedding dimension
|
||||
if hasattr(embedding_model_instance, 'dimension') and embedding_model_instance.dimension > 2000:
|
||||
|
|
|
|||
|
|
@ -21,9 +21,11 @@ dependencies = [
|
|||
"notion-client>=2.3.0",
|
||||
"pgvector>=0.3.6",
|
||||
"playwright>=1.50.0",
|
||||
"python-ffmpeg>=2.0.12",
|
||||
"rerankers[flashrank]>=0.7.1",
|
||||
"sentence-transformers>=3.4.1",
|
||||
"slack-sdk>=3.34.0",
|
||||
"static-ffmpeg>=2.13",
|
||||
"tavily-python>=0.3.2",
|
||||
"unstructured-client>=0.30.0",
|
||||
"unstructured[all-docs]>=0.16.25",
|
||||
|
|
|
|||
222
surfsense_backend/uv.lock
generated
222
surfsense_backend/uv.lock
generated
|
|
@ -590,6 +590,15 @@ wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/68/1b/e0a87d256e40e8c888847551b20a017a6b98139178505dc7ffb96f04e954/dnspython-2.7.0-py3-none-any.whl", hash = "sha256:b4c34b7d10b51bcc3a5071e7b8dee77939f1e878477eeecc965e9835f63c6c86", size = 313632 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "docutils"
|
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version = "0.21.2"
|
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/ae/ed/aefcc8cd0ba62a0560c3c18c33925362d46c6075480bfa4df87b28e169a9/docutils-0.21.2.tar.gz", hash = "sha256:3a6b18732edf182daa3cd12775bbb338cf5691468f91eeeb109deff6ebfa986f", size = 2204444 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl", hash = "sha256:dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2", size = 587408 },
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]
|
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|
||||
[[package]]
|
||||
name = "effdet"
|
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version = "0.4.1"
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|
|
@ -1144,6 +1153,18 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477", size = 86794 },
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|
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[[package]]
|
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|
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version = "1.5.0"
|
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source = { registry = "https://pypi.org/simple" }
|
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dependencies = [
|
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{ name = "requests" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/22/11/102da08f88412d875fa2f1a9a469ff7ad4c874b0ca6fed0048fe385bdb3d/id-1.5.0.tar.gz", hash = "sha256:292cb8a49eacbbdbce97244f47a97b4c62540169c976552e497fd57df0734c1d", size = 15237 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/9f/cb/18326d2d89ad3b0dd143da971e77afd1e6ca6674f1b1c3df4b6bec6279fc/id-1.5.0-py3-none-any.whl", hash = "sha256:f1434e1cef91f2cbb8a4ec64663d5a23b9ed43ef44c4c957d02583d61714c658", size = 13611 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.10"
|
||||
|
|
@ -1165,6 +1186,48 @@ wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/79/9d/0fb148dc4d6fa4a7dd1d8378168d9b4cd8d4560a6fbf6f0121c5fc34eb68/importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e", size = 26971 },
|
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]
|
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|
||||
[[package]]
|
||||
name = "jaraco-classes"
|
||||
version = "3.4.0"
|
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
|
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{ name = "more-itertools" },
|
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]
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sdist = { url = "https://files.pythonhosted.org/packages/06/c0/ed4a27bc5571b99e3cff68f8a9fa5b56ff7df1c2251cc715a652ddd26402/jaraco.classes-3.4.0.tar.gz", hash = "sha256:47a024b51d0239c0dd8c8540c6c7f484be3b8fcf0b2d85c13825780d3b3f3acd", size = 11780 }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/7f/66/b15ce62552d84bbfcec9a4873ab79d993a1dd4edb922cbfccae192bd5b5f/jaraco.classes-3.4.0-py3-none-any.whl", hash = "sha256:f662826b6bed8cace05e7ff873ce0f9283b5c924470fe664fff1c2f00f581790", size = 6777 },
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|
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[[package]]
|
||||
name = "jaraco-context"
|
||||
version = "6.0.1"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/df/ad/f3777b81bf0b6e7bc7514a1656d3e637b2e8e15fab2ce3235730b3e7a4e6/jaraco_context-6.0.1.tar.gz", hash = "sha256:9bae4ea555cf0b14938dc0aee7c9f32ed303aa20a3b73e7dc80111628792d1b3", size = 13912 }
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||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ff/db/0c52c4cf5e4bd9f5d7135ec7669a3a767af21b3a308e1ed3674881e52b62/jaraco.context-6.0.1-py3-none-any.whl", hash = "sha256:f797fc481b490edb305122c9181830a3a5b76d84ef6d1aef2fb9b47ab956f9e4", size = 6825 },
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||||
|
||||
[[package]]
|
||||
name = "jaraco-functools"
|
||||
version = "4.1.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "more-itertools" },
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sdist = { url = "https://files.pythonhosted.org/packages/ab/23/9894b3df5d0a6eb44611c36aec777823fc2e07740dabbd0b810e19594013/jaraco_functools-4.1.0.tar.gz", hash = "sha256:70f7e0e2ae076498e212562325e805204fc092d7b4c17e0e86c959e249701a9d", size = 19159 }
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||||
wheels = [
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{ url = "https://files.pythonhosted.org/packages/9f/4f/24b319316142c44283d7540e76c7b5a6dbd5db623abd86bb7b3491c21018/jaraco.functools-4.1.0-py3-none-any.whl", hash = "sha256:ad159f13428bc4acbf5541ad6dec511f91573b90fba04df61dafa2a1231cf649", size = 10187 },
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "jeepney"
|
||||
version = "0.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/7b/6f/357efd7602486741aa73ffc0617fb310a29b588ed0fd69c2399acbb85b0c/jeepney-0.9.0.tar.gz", hash = "sha256:cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732", size = 106758 }
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||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/b2/a3/e137168c9c44d18eff0376253da9f1e9234d0239e0ee230d2fee6cea8e55/jeepney-0.9.0-py3-none-any.whl", hash = "sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683", size = 49010 },
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|
||||
|
||||
[[package]]
|
||||
name = "jinja2"
|
||||
version = "3.1.5"
|
||||
|
|
@ -1269,6 +1332,23 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/d1/0f/8910b19ac0670a0f80ce1008e5e751c4a57e14d2c4c13a482aa6079fa9d6/jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf", size = 18459 },
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||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "jaraco-classes" },
|
||||
{ name = "jaraco-context" },
|
||||
{ name = "jaraco-functools" },
|
||||
{ name = "jeepney", marker = "sys_platform == 'linux'" },
|
||||
{ name = "pywin32-ctypes", marker = "sys_platform == 'win32'" },
|
||||
{ name = "secretstorage", marker = "sys_platform == 'linux'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/70/09/d904a6e96f76ff214be59e7aa6ef7190008f52a0ab6689760a98de0bf37d/keyring-25.6.0.tar.gz", hash = "sha256:0b39998aa941431eb3d9b0d4b2460bc773b9df6fed7621c2dfb291a7e0187a66", size = 62750 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d3/32/da7f44bcb1105d3e88a0b74ebdca50c59121d2ddf71c9e34ba47df7f3a56/keyring-25.6.0-py3-none-any.whl", hash = "sha256:552a3f7af126ece7ed5c89753650eec89c7eaae8617d0aa4d9ad2b75111266bd", size = 39085 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "kiwisolver"
|
||||
version = "1.4.8"
|
||||
|
|
@ -1754,6 +1834,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/cd/76/c8575f90f521017597c5e57e3bfef61e3f27d9cb6c741a82a24d72b10a60/model2vec-0.4.1-py3-none-any.whl", hash = "sha256:04a397a17da9b967082b6baa4c494f0be48c89ec4e1a3975b4f290f045238a38", size = 41972 },
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "more-itertools"
|
||||
version = "10.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ce/a0/834b0cebabbfc7e311f30b46c8188790a37f89fc8d756660346fe5abfd09/more_itertools-10.7.0.tar.gz", hash = "sha256:9fddd5403be01a94b204faadcff459ec3568cf110265d3c54323e1e866ad29d3", size = 127671 }
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||||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/2b/9f/7ba6f94fc1e9ac3d2b853fdff3035fb2fa5afbed898c4a72b8a020610594/more_itertools-10.7.0-py3-none-any.whl", hash = "sha256:d43980384673cb07d2f7d2d918c616b30c659c089ee23953f601d6609c67510e", size = 65278 },
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "mpmath"
|
||||
version = "1.3.0"
|
||||
|
|
@ -1829,6 +1918,37 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f", size = 1723263 },
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||||
|
||||
[[package]]
|
||||
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|||
Loading…
Add table
Reference in a new issue