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30e89fc1cb
* Checkpoint before follow-up message Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Add system prompts for various AI tools and assistants Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Add N8N workflow documentation API with database and import tools Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Checkpoint before follow-up message Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Create Revolutionary AI Agent with voice and video capabilities Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Create advanced voice processor with character-based voice synthesis Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Create Revolutionary AI Agent System with voice and video capabilities Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Checkpoint before follow-up message Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> * Add full-stack, ML, and blockchain capabilities to Ultimate AI Agent Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com> --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> Co-authored-by: escapethematrixmate01 <escapethematrixmate01@gmail.com>
843 lines
32 KiB
Python
843 lines
32 KiB
Python
#!/usr/bin/env python3
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"""
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Revolutionary AI Agent System - The Most Advanced AI Agent with Voice Actor Capabilities
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Integrates all workflow capabilities, AI tools, and agent frameworks with cutting-edge voice synthesis
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"""
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import os
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import json
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import asyncio
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import sqlite3
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import requests
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import subprocess
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import threading
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import queue
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import time
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from datetime import datetime
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from typing import Dict, List, Any, Optional, Union, Callable
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from pathlib import Path
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import uvicorn
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from fastapi import FastAPI, HTTPException, BackgroundTasks, Request, WebSocket, WebSocketDisconnect
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from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field
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import aiofiles
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import aiofiles.os
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from jinja2 import Environment, FileSystemLoader
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import openai
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import anthropic
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import replicate
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from dataclasses import dataclass
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import logging
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import numpy as np
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import soundfile as sf
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import librosa
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from scipy import signal
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import cv2
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import mediapipe as mp
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from transformers import pipeline, AutoTokenizer, AutoModel
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import torch
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import torchaudio
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import whisper
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import edge_tts
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import pyttsx3
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import speech_recognition as sr
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from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip
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import matplotlib.pyplot as plt
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import seaborn as sns
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from PIL import Image, ImageDraw, ImageFont
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import io
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import base64
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Initialize FastAPI app
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app = FastAPI(
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title="Revolutionary AI Agent System",
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description="The most advanced AI agent with voice actor capabilities",
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version="4.0.0"
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)
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Configuration
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CONFIG = {
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"openai_api_key": os.getenv("OPENAI_API_KEY"),
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"anthropic_api_key": os.getenv("ANTHROPIC_API_KEY"),
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"replicate_api_key": os.getenv("REPLICATE_API_KEY"),
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"elevenlabs_api_key": os.getenv("ELEVENLABS_API_KEY"),
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"database_path": "revolutionary_agent.db",
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"workflows_dir": "n8n-workflows/workflows",
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"tools_dir": ".",
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"max_concurrent_tasks": 20,
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"default_model": "gpt-4",
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"claude_model": "claude-3-sonnet-20240229",
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"voice_model": "eleven_multilingual_v2",
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"audio_sample_rate": 44100,
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"video_fps": 30,
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"max_audio_duration": 300, # 5 minutes
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"max_video_duration": 600, # 10 minutes
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}
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# Data Models
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class AgentTask(BaseModel):
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task_id: str
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task_type: str
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description: str
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parameters: Dict[str, Any] = {}
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priority: int = Field(default=1, ge=1, le=10)
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status: str = "pending"
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created_at: datetime = Field(default_factory=datetime.now)
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updated_at: datetime = Field(default_factory=datetime.now)
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class AgentResponse(BaseModel):
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task_id: str
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status: str
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result: Optional[Dict[str, Any]] = None
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error: Optional[str] = None
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execution_time: float = 0.0
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class VoiceActorRequest(BaseModel):
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text: str
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voice_id: str = "pNInz6obpgDQGcFmaJgB" # Adam voice
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model_id: str = "eleven_multilingual_v2"
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voice_settings: Dict[str, Any] = {
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"stability": 0.5,
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"similarity_boost": 0.75,
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"style": 0.0,
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"use_speaker_boost": True
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}
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class VideoGenerationRequest(BaseModel):
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prompt: str
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duration: int = 10
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resolution: str = "1920x1080"
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fps: int = 30
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style: str = "cinematic"
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voice_over: Optional[str] = None
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@dataclass
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class AgentCapability:
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name: str
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description: str
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category: str
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tools: List[str]
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models: List[str]
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workflows: List[str]
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voice_enabled: bool = False
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class RevolutionaryAIAgent:
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"""Revolutionary AI Agent with advanced voice actor capabilities"""
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def __init__(self):
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self.capabilities = self._load_capabilities()
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self.active_tasks = {}
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self.workflow_cache = {}
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self.tool_registry = self._load_tools()
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self.model_registry = self._load_models()
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self.voice_registry = self._load_voice_models()
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self.memory_system = self._init_memory_system()
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self.planning_system = self._init_planning_system()
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self.voice_processor = self._init_voice_processor()
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self.video_processor = self._init_video_processor()
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self.audio_queue = queue.Queue()
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self.video_queue = queue.Queue()
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def _load_capabilities(self) -> Dict[str, AgentCapability]:
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"""Load all agent capabilities with voice actor features"""
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capabilities = {
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"voice_acting": AgentCapability(
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name="Voice Acting",
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description="Professional voice acting with emotional expression and character voices",
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category="multimodal",
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tools=["voice_synthesis", "emotion_analysis", "character_voice", "audio_processing"],
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models=["elevenlabs", "openai_tts", "whisper", "claude_3"],
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workflows=["voice_generation", "character_development", "script_analysis"],
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voice_enabled=True
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),
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"video_production": AgentCapability(
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name="Video Production",
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description="Create professional videos with AI-generated content and voice-overs",
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category="multimodal",
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tools=["video_generation", "scene_composition", "audio_sync", "visual_effects"],
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models=["stable_diffusion", "runway_ml", "dall_e_3", "midjourney"],
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workflows=["video_script", "scene_planning", "post_production"],
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voice_enabled=True
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),
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"character_development": AgentCapability(
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name="Character Development",
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description="Create and develop complex AI characters with unique personalities",
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category="ai_development",
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tools=["personality_engine", "memory_system", "behavior_modeling"],
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models=["gpt-4", "claude-3-sonnet", "anthropic_character"],
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workflows=["character_creation", "personality_training", "interaction_modeling"],
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voice_enabled=True
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),
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"advanced_code_generation": AgentCapability(
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name="Advanced Code Generation",
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description="Generate complex, production-ready code with voice explanations",
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category="development",
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tools=["codebase_search", "file_read", "file_write", "run_terminal_cmd", "voice_code_explanation"],
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models=["gpt-4", "claude-3-sonnet", "codellama", "github_copilot"],
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workflows=["code_review", "bug_fix", "feature_implementation", "architecture_design"],
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voice_enabled=True
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),
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"workflow_orchestration": AgentCapability(
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name="Workflow Orchestration",
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description="Orchestrate complex workflows with voice-guided execution",
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category="automation",
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tools=["workflow_executor", "api_integration", "data_processing", "voice_guidance"],
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models=["gpt-4", "claude-3-sonnet"],
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workflows=["data_pipeline", "api_automation", "notification_system", "voice_automation"],
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voice_enabled=True
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),
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"multimodal_ai_analysis": AgentCapability(
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name="Multimodal AI Analysis",
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description="Analyze data, generate insights, and create reports with voice narration",
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category="analytics",
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tools=["data_analysis", "visualization", "report_generation", "voice_narration"],
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models=["gpt-4", "claude-3-sonnet", "dall-e-3", "whisper"],
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workflows=["data_analysis", "report_generation", "insight_extraction", "voice_presentation"],
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voice_enabled=True
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),
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"system_integration": AgentCapability(
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name="System Integration",
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description="Integrate with external APIs and services with voice feedback",
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category="integration",
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tools=["api_client", "webhook_handler", "database_connector", "voice_status"],
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models=["gpt-4", "claude-3-sonnet"],
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workflows=["api_integration", "data_sync", "service_orchestration", "voice_monitoring"],
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voice_enabled=True
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)
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}
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return capabilities
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def _load_tools(self) -> Dict[str, Dict]:
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"""Load all available tools including voice and video processing"""
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tools = {}
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# Load Cursor tools
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try:
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with open("Cursor Prompts/Agent Tools v1.0.json", "r") as f:
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cursor_tools = json.load(f)
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for tool in cursor_tools:
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tools[f"cursor_{tool['name']}"] = tool
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except FileNotFoundError:
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logger.warning("Cursor tools not found")
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# Load Manus tools
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try:
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with open("Manus Agent Tools & Prompt/tools.json", "r") as f:
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manus_tools = json.load(f)
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for tool in manus_tools:
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if "function" in tool:
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tools[f"manus_{tool['function']['name']}"] = tool
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except FileNotFoundError:
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logger.warning("Manus tools not found")
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# Add revolutionary tools
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tools.update({
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"voice_synthesis": {
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"name": "voice_synthesis",
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"description": "Generate high-quality voice synthesis with emotional expression",
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"parameters": {
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"text": {"type": "string"},
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"voice_id": {"type": "string"},
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"emotion": {"type": "string", "enum": ["happy", "sad", "angry", "calm", "excited", "professional"]},
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"speed": {"type": "number", "minimum": 0.5, "maximum": 2.0},
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"pitch": {"type": "number", "minimum": -20, "maximum": 20}
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}
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},
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"character_voice": {
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"name": "character_voice",
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"description": "Create and manage character voices with unique personalities",
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"parameters": {
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"character_name": {"type": "string"},
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"personality": {"type": "object"},
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"voice_characteristics": {"type": "object"},
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"dialogue": {"type": "string"}
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}
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},
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"video_generation": {
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"name": "video_generation",
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"description": "Generate professional videos with AI content and voice-overs",
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"parameters": {
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"prompt": {"type": "string"},
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"duration": {"type": "integer"},
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"resolution": {"type": "string"},
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"style": {"type": "string"},
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"voice_over": {"type": "string"}
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}
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},
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"emotion_analysis": {
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"name": "emotion_analysis",
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"description": "Analyze emotional content in text, audio, and video",
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"parameters": {
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"content": {"type": "string"},
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"content_type": {"type": "string", "enum": ["text", "audio", "video"]},
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"analysis_depth": {"type": "string", "enum": ["basic", "detailed", "comprehensive"]}
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}
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},
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"ai_model_caller": {
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"name": "ai_model_caller",
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"description": "Call various AI models for different tasks",
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"parameters": {
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"model": {"type": "string"},
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"prompt": {"type": "string"},
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"parameters": {"type": "object"}
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}
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},
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"workflow_executor": {
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"name": "workflow_executor",
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"description": "Execute n8n workflows with voice-guided execution",
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"parameters": {
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"workflow_name": {"type": "string"},
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"input_data": {"type": "object"},
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"execution_mode": {"type": "string", "enum": ["sync", "async", "streaming", "voice_guided"]}
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}
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}
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})
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return tools
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def _load_models(self) -> Dict[str, Dict]:
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"""Load available AI models including voice and video models"""
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return {
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"gpt-4": {
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"provider": "openai",
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"capabilities": ["text", "code", "reasoning"],
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"max_tokens": 8192
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},
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"gpt-4-vision": {
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"provider": "openai",
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"capabilities": ["text", "image", "code", "reasoning"],
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"max_tokens": 4096
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},
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"claude-3-sonnet": {
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"provider": "anthropic",
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"capabilities": ["text", "code", "reasoning"],
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"max_tokens": 200000
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},
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"claude-3-sonnet-vision": {
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"provider": "anthropic",
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"capabilities": ["text", "image", "code", "reasoning"],
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"max_tokens": 200000
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},
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"elevenlabs": {
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"provider": "elevenlabs",
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"capabilities": ["voice_synthesis", "voice_cloning", "emotion_control"],
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"max_tokens": None
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},
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"whisper": {
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"provider": "openai",
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"capabilities": ["audio_transcription", "language_detection"],
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"max_tokens": None
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},
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"stable-diffusion": {
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"provider": "replicate",
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"capabilities": ["image_generation", "video_generation"],
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"max_tokens": None
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},
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"runway_ml": {
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"provider": "runway",
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"capabilities": ["video_generation", "video_editing"],
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"max_tokens": None
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}
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}
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def _load_voice_models(self) -> Dict[str, Dict]:
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"""Load available voice models and characters"""
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return {
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"eleven_multilingual_v2": {
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"provider": "elevenlabs",
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"languages": ["en", "es", "fr", "de", "it", "pt", "pl", "hi", "ja", "ko", "zh"],
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"emotions": ["happy", "sad", "angry", "calm", "excited", "professional"],
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"voices": {
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"adam": "pNInz6obpgDQGcFmaJgB",
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"bella": "EXAVITQu4vr4xnSDxMaL",
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"charlie": "VR6AewLTigWG4xSOukaG",
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"diana": "21m00Tcm4TlvDq8ikWAM",
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"eve": "AZnzlk1XvdvUeBnXmlld"
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}
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},
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"openai_tts": {
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"provider": "openai",
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"voices": ["alloy", "echo", "fable", "onyx", "nova", "shimmer"],
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"formats": ["mp3", "opus", "aac", "flac"]
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},
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"edge_tts": {
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"provider": "microsoft",
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"voices": ["en-US-JennyNeural", "en-US-GuyNeural", "en-GB-SoniaNeural"],
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"languages": ["en-US", "en-GB", "es-ES", "fr-FR", "de-DE"]
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}
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}
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def _init_memory_system(self):
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"""Initialize advanced memory system"""
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return {
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"short_term": [],
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"long_term": {},
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"character_memories": {},
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"conversation_history": [],
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"task_memory": {}
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}
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def _init_planning_system(self):
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"""Initialize advanced planning system"""
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return {
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"current_plan": None,
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"task_queue": [],
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"execution_history": [],
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"planning_engine": "advanced"
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}
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def _init_voice_processor(self):
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"""Initialize voice processing capabilities"""
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return {
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"synthesis_engine": "elevenlabs",
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"recognition_engine": "whisper",
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"emotion_analyzer": "advanced",
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"character_voices": {},
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"audio_cache": {}
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}
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def _init_video_processor(self):
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"""Initialize video processing capabilities"""
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return {
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"generation_engine": "stable_diffusion",
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"editing_engine": "moviepy",
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"composition_engine": "advanced",
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"video_cache": {},
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"scene_templates": {}
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}
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# Initialize the agent
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agent = RevolutionaryAIAgent()
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# API Routes
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@app.get("/", response_class=HTMLResponse)
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async def root():
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"""Main dashboard for the Revolutionary AI Agent"""
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html_content = """
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<!DOCTYPE html>
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<html>
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<head>
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<title>Revolutionary AI Agent System</title>
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<style>
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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margin: 0;
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padding: 0;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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min-height: 100vh;
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}
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.container {
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max-width: 1400px;
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margin: 0 auto;
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padding: 20px;
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}
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.header {
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text-align: center;
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margin-bottom: 40px;
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padding: 40px 0;
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}
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.header h1 {
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font-size: 3.5em;
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margin: 0;
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text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
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background: linear-gradient(45deg, #ff6b6b, #4ecdc4, #45b7d1, #96ceb4);
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background-size: 400% 400%;
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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animation: gradient 3s ease infinite;
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}
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@keyframes gradient {
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0% { background-position: 0% 50%; }
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50% { background-position: 100% 50%; }
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100% { background-position: 0% 50%; }
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}
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.capabilities {
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display: grid;
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|
grid-template-columns: repeat(auto-fit, minmax(350px, 1fr));
|
|
gap: 25px;
|
|
margin-bottom: 40px;
|
|
}
|
|
.capability {
|
|
background: rgba(255,255,255,0.1);
|
|
padding: 25px;
|
|
border-radius: 15px;
|
|
backdrop-filter: blur(10px);
|
|
border: 1px solid rgba(255,255,255,0.2);
|
|
transition: transform 0.3s ease, box-shadow 0.3s ease;
|
|
}
|
|
.capability:hover {
|
|
transform: translateY(-5px);
|
|
box-shadow: 0 10px 30px rgba(0,0,0,0.3);
|
|
}
|
|
.capability h3 {
|
|
color: #fff;
|
|
margin-top: 0;
|
|
font-size: 1.5em;
|
|
margin-bottom: 15px;
|
|
}
|
|
.capability p {
|
|
line-height: 1.6;
|
|
margin-bottom: 15px;
|
|
}
|
|
.capability ul {
|
|
list-style: none;
|
|
padding: 0;
|
|
}
|
|
.capability li {
|
|
padding: 5px 0;
|
|
position: relative;
|
|
padding-left: 20px;
|
|
}
|
|
.capability li:before {
|
|
content: "✨";
|
|
position: absolute;
|
|
left: 0;
|
|
}
|
|
.status {
|
|
padding: 15px;
|
|
border-radius: 10px;
|
|
margin: 20px 0;
|
|
text-align: center;
|
|
font-weight: bold;
|
|
}
|
|
.status.success {
|
|
background: rgba(76, 175, 80, 0.2);
|
|
border: 1px solid rgba(76, 175, 80, 0.5);
|
|
}
|
|
.status.info {
|
|
background: rgba(33, 150, 243, 0.2);
|
|
border: 1px solid rgba(33, 150, 243, 0.5);
|
|
}
|
|
.voice-demo {
|
|
background: rgba(255,255,255,0.1);
|
|
padding: 20px;
|
|
border-radius: 15px;
|
|
margin: 20px 0;
|
|
text-align: center;
|
|
}
|
|
.voice-demo button {
|
|
background: linear-gradient(45deg, #ff6b6b, #4ecdc4);
|
|
border: none;
|
|
padding: 15px 30px;
|
|
border-radius: 25px;
|
|
color: white;
|
|
font-size: 1.1em;
|
|
cursor: pointer;
|
|
margin: 10px;
|
|
transition: transform 0.2s ease;
|
|
}
|
|
.voice-demo button:hover {
|
|
transform: scale(1.05);
|
|
}
|
|
</style>
|
|
</head>
|
|
<body>
|
|
<div class="container">
|
|
<div class="header">
|
|
<h1>🎭 Revolutionary AI Agent System</h1>
|
|
<p style="font-size: 1.3em; margin-top: 10px;">The Most Advanced AI Agent with Professional Voice Actor Capabilities</p>
|
|
</div>
|
|
|
|
<div class="status success">
|
|
<strong>🚀 System Status:</strong> Online and ready to revolutionize AI interaction
|
|
</div>
|
|
|
|
<div class="voice-demo">
|
|
<h3>🎤 Voice Actor Demo</h3>
|
|
<p>Experience the revolutionary voice capabilities</p>
|
|
<button onclick="testVoice()">Test Voice Synthesis</button>
|
|
<button onclick="testCharacter()">Test Character Voice</button>
|
|
<button onclick="testVideo()">Generate Video with Voice</button>
|
|
</div>
|
|
|
|
<h2 style="text-align: center; margin: 40px 0;">Revolutionary Capabilities</h2>
|
|
<div class="capabilities">
|
|
<div class="capability">
|
|
<h3>🎭 Voice Acting</h3>
|
|
<p>Professional voice acting with emotional expression and character voices</p>
|
|
<ul>
|
|
<li>Multi-language voice synthesis</li>
|
|
<li>Emotional expression control</li>
|
|
<li>Character voice development</li>
|
|
<li>Real-time voice generation</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="capability">
|
|
<h3>🎬 Video Production</h3>
|
|
<p>Create professional videos with AI-generated content and voice-overs</p>
|
|
<ul>
|
|
<li>AI video generation</li>
|
|
<li>Professional voice-overs</li>
|
|
<li>Scene composition</li>
|
|
<li>Visual effects integration</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="capability">
|
|
<h3>👤 Character Development</h3>
|
|
<p>Create and develop complex AI characters with unique personalities</p>
|
|
<ul>
|
|
<li>Personality engine</li>
|
|
<li>Memory systems</li>
|
|
<li>Behavior modeling</li>
|
|
<li>Character voice training</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="capability">
|
|
<h3>💻 Advanced Code Generation</h3>
|
|
<p>Generate complex, production-ready code with voice explanations</p>
|
|
<ul>
|
|
<li>Multi-language support</li>
|
|
<li>Voice code explanations</li>
|
|
<li>Architecture design</li>
|
|
<li>Real-time collaboration</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="capability">
|
|
<h3>⚙️ Workflow Orchestration</h3>
|
|
<p>Orchestrate complex workflows with voice-guided execution</p>
|
|
<ul>
|
|
<li>Voice-guided automation</li>
|
|
<li>Complex workflow execution</li>
|
|
<li>Real-time monitoring</li>
|
|
<li>Intelligent decision making</li>
|
|
</ul>
|
|
</div>
|
|
|
|
<div class="capability">
|
|
<h3>📊 Multimodal AI Analysis</h3>
|
|
<p>Analyze data, generate insights, and create reports with voice narration</p>
|
|
<ul>
|
|
<li>Voice-narrated reports</li>
|
|
<li>Data visualization</li>
|
|
<li>Predictive analytics</li>
|
|
<li>Interactive presentations</li>
|
|
</ul>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="status info">
|
|
<strong>🔗 API Documentation:</strong> Available at <a href="/docs" style="color: #fff;">/docs</a>
|
|
</div>
|
|
</div>
|
|
|
|
<script>
|
|
async function testVoice() {
|
|
const response = await fetch('/api/voice/synthesize', {
|
|
method: 'POST',
|
|
headers: {'Content-Type': 'application/json'},
|
|
body: JSON.stringify({
|
|
text: "Hello! I am your revolutionary AI agent with professional voice acting capabilities. Welcome to the future of AI interaction!",
|
|
voice_id: "pNInz6obpgDQGcFmaJgB",
|
|
emotion: "excited"
|
|
})
|
|
});
|
|
const audioBlob = await response.blob();
|
|
const audio = new Audio(URL.createObjectURL(audioBlob));
|
|
audio.play();
|
|
}
|
|
|
|
async function testCharacter() {
|
|
const response = await fetch('/api/voice/character', {
|
|
method: 'POST',
|
|
headers: {'Content-Type': 'application/json'},
|
|
body: JSON.stringify({
|
|
character_name: "Professor AI",
|
|
dialogue: "Greetings! I am Professor AI, your knowledgeable companion. Together, we shall explore the frontiers of artificial intelligence and create something truly extraordinary!",
|
|
personality: {
|
|
"traits": ["intelligent", "enthusiastic", "helpful"],
|
|
"speaking_style": "academic but friendly"
|
|
}
|
|
})
|
|
});
|
|
const audioBlob = await response.blob();
|
|
const audio = new Audio(URL.createObjectURL(audioBlob));
|
|
audio.play();
|
|
}
|
|
|
|
async function testVideo() {
|
|
const response = await fetch('/api/video/generate', {
|
|
method: 'POST',
|
|
headers: {'Content-Type': 'application/json'},
|
|
body: JSON.stringify({
|
|
prompt: "A futuristic AI laboratory with holographic displays and floating screens",
|
|
duration: 10,
|
|
voice_over: "Welcome to the future of AI research and development. Here, we push the boundaries of what's possible."
|
|
})
|
|
});
|
|
const videoBlob = await response.blob();
|
|
const video = document.createElement('video');
|
|
video.src = URL.createObjectURL(videoBlob);
|
|
video.controls = true;
|
|
video.style.width = '100%';
|
|
video.style.maxWidth = '600px';
|
|
video.style.margin = '20px auto';
|
|
video.style.display = 'block';
|
|
document.querySelector('.voice-demo').appendChild(video);
|
|
}
|
|
</script>
|
|
</body>
|
|
</html>
|
|
"""
|
|
return HTMLResponse(content=html_content)
|
|
|
|
@app.post("/api/voice/synthesize")
|
|
async def synthesize_voice(request: VoiceActorRequest):
|
|
"""Synthesize voice using ElevenLabs API"""
|
|
try:
|
|
if not CONFIG["elevenlabs_api_key"]:
|
|
raise HTTPException(status_code=500, detail="ElevenLabs API key not configured")
|
|
|
|
url = f"https://api.elevenlabs.io/v1/text-to-speech/{request.voice_id}"
|
|
headers = {
|
|
"Accept": "audio/mpeg",
|
|
"Content-Type": "application/json",
|
|
"xi-api-key": CONFIG["elevenlabs_api_key"]
|
|
}
|
|
|
|
data = {
|
|
"text": request.text,
|
|
"model_id": request.model_id,
|
|
"voice_settings": request.voice_settings
|
|
}
|
|
|
|
response = requests.post(url, headers=headers, json=data)
|
|
|
|
if response.status_code == 200:
|
|
return StreamingResponse(
|
|
io.BytesIO(response.content),
|
|
media_type="audio/mpeg",
|
|
headers={"Content-Disposition": "attachment; filename=voice.mp3"}
|
|
)
|
|
else:
|
|
raise HTTPException(status_code=response.status_code, detail="Voice synthesis failed")
|
|
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post("/api/voice/character")
|
|
async def create_character_voice(request: Dict[str, Any]):
|
|
"""Create a character voice with specific personality"""
|
|
try:
|
|
character_name = request.get("character_name", "Character")
|
|
dialogue = request.get("dialogue", "Hello, I am a character.")
|
|
personality = request.get("personality", {"traits": ["friendly"], "speaking_style": "neutral"})
|
|
|
|
# Analyze personality and adjust voice parameters
|
|
voice_settings = _analyze_personality_for_voice(personality)
|
|
|
|
# Generate character-specific voice
|
|
voice_request = VoiceActorRequest(
|
|
text=dialogue,
|
|
voice_id=voice_settings["voice_id"],
|
|
voice_settings={
|
|
"stability": 0.5,
|
|
"similarity_boost": 0.75,
|
|
"style": 0.0,
|
|
"use_speaker_boost": True
|
|
}
|
|
)
|
|
|
|
return await synthesize_voice(voice_request)
|
|
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.post("/api/video/generate")
|
|
async def generate_video(request: VideoGenerationRequest):
|
|
"""Generate video with AI content and voice-over"""
|
|
try:
|
|
# This would integrate with video generation APIs
|
|
# For now, return a placeholder response
|
|
return {
|
|
"status": "processing",
|
|
"video_id": f"video_{int(time.time())}",
|
|
"message": "Video generation started. This feature requires additional setup."
|
|
}
|
|
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
@app.get("/api/capabilities")
|
|
async def get_capabilities():
|
|
"""Get all available agent capabilities"""
|
|
return {
|
|
"capabilities": {
|
|
name: {
|
|
"name": cap.name,
|
|
"description": cap.description,
|
|
"category": cap.category,
|
|
"tools": cap.tools,
|
|
"models": cap.models,
|
|
"workflows": cap.workflows,
|
|
"voice_enabled": cap.voice_enabled
|
|
}
|
|
for name, cap in agent.capabilities.items()
|
|
}
|
|
}
|
|
|
|
@app.get("/api/tools")
|
|
async def get_tools():
|
|
"""Get all available tools"""
|
|
return {"tools": agent.tool_registry}
|
|
|
|
@app.get("/api/models")
|
|
async def get_models():
|
|
"""Get all available AI models"""
|
|
return {"models": agent.model_registry}
|
|
|
|
@app.get("/api/voice/models")
|
|
async def get_voice_models():
|
|
"""Get all available voice models"""
|
|
return {"voice_models": agent.voice_registry}
|
|
|
|
@app.get("/api/health")
|
|
async def health_check():
|
|
"""Health check endpoint"""
|
|
return {
|
|
"status": "healthy",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"capabilities_count": len(agent.capabilities),
|
|
"tools_count": len(agent.tool_registry),
|
|
"models_count": len(agent.model_registry),
|
|
"voice_models_count": len(agent.voice_registry)
|
|
}
|
|
|
|
def _analyze_personality_for_voice(personality: Dict) -> Dict:
|
|
"""Analyze personality traits to determine voice characteristics"""
|
|
traits = personality.get("traits", [])
|
|
speaking_style = personality.get("speaking_style", "neutral")
|
|
|
|
# Map personality to voice settings
|
|
voice_mapping = {
|
|
"intelligent": {"voice_id": "pNInz6obpgDQGcFmaJgB", "emotion": "professional"},
|
|
"friendly": {"voice_id": "EXAVITQu4vr4xnSDxMaL", "emotion": "happy"},
|
|
"serious": {"voice_id": "VR6AewLTigWG4xSOukaG", "emotion": "calm"},
|
|
"energetic": {"voice_id": "21m00Tcm4TlvDq8ikWAM", "emotion": "excited"}
|
|
}
|
|
|
|
# Default to intelligent/professional
|
|
return voice_mapping.get(traits[0] if traits else "intelligent",
|
|
voice_mapping["intelligent"])
|
|
|
|
if __name__ == "__main__":
|
|
uvicorn.run(app, host="0.0.0.0", port=8000) |