#!/usr/bin/env python3 """ Revolutionary AI Agent System - The Most Advanced AI Agent with Voice Actor Capabilities Integrates all workflow capabilities, AI tools, and agent frameworks with cutting-edge voice synthesis """ import os import json import asyncio import sqlite3 import requests import subprocess import threading import queue import time from datetime import datetime from typing import Dict, List, Any, Optional, Union, Callable from pathlib import Path import uvicorn from fastapi import FastAPI, HTTPException, BackgroundTasks, Request, WebSocket, WebSocketDisconnect from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse from fastapi.staticfiles import StaticFiles from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel, Field import aiofiles import aiofiles.os from jinja2 import Environment, FileSystemLoader import openai import anthropic import replicate from dataclasses import dataclass import logging import numpy as np import soundfile as sf import librosa from scipy import signal import cv2 import mediapipe as mp from transformers import pipeline, AutoTokenizer, AutoModel import torch import torchaudio import whisper import edge_tts import pyttsx3 import speech_recognition as sr from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip import matplotlib.pyplot as plt import seaborn as sns from PIL import Image, ImageDraw, ImageFont import io import base64 # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Initialize FastAPI app app = FastAPI( title="Revolutionary AI Agent System", description="The most advanced AI agent with voice actor capabilities", version="4.0.0" ) # Add CORS middleware app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Configuration CONFIG = { "openai_api_key": os.getenv("OPENAI_API_KEY"), "anthropic_api_key": os.getenv("ANTHROPIC_API_KEY"), "replicate_api_key": os.getenv("REPLICATE_API_KEY"), "elevenlabs_api_key": os.getenv("ELEVENLABS_API_KEY"), "database_path": "revolutionary_agent.db", "workflows_dir": "n8n-workflows/workflows", "tools_dir": ".", "max_concurrent_tasks": 20, "default_model": "gpt-4", "claude_model": "claude-3-sonnet-20240229", "voice_model": "eleven_multilingual_v2", "audio_sample_rate": 44100, "video_fps": 30, "max_audio_duration": 300, # 5 minutes "max_video_duration": 600, # 10 minutes } # Data Models class AgentTask(BaseModel): task_id: str task_type: str description: str parameters: Dict[str, Any] = {} priority: int = Field(default=1, ge=1, le=10) status: str = "pending" created_at: datetime = Field(default_factory=datetime.now) updated_at: datetime = Field(default_factory=datetime.now) class AgentResponse(BaseModel): task_id: str status: str result: Optional[Dict[str, Any]] = None error: Optional[str] = None execution_time: float = 0.0 class VoiceActorRequest(BaseModel): text: str voice_id: str = "pNInz6obpgDQGcFmaJgB" # Adam voice model_id: str = "eleven_multilingual_v2" voice_settings: Dict[str, Any] = { "stability": 0.5, "similarity_boost": 0.75, "style": 0.0, "use_speaker_boost": True } class VideoGenerationRequest(BaseModel): prompt: str duration: int = 10 resolution: str = "1920x1080" fps: int = 30 style: str = "cinematic" voice_over: Optional[str] = None @dataclass class AgentCapability: name: str description: str category: str tools: List[str] models: List[str] workflows: List[str] voice_enabled: bool = False class RevolutionaryAIAgent: """Revolutionary AI Agent with advanced voice actor capabilities""" def __init__(self): self.capabilities = self._load_capabilities() self.active_tasks = {} self.workflow_cache = {} self.tool_registry = self._load_tools() self.model_registry = self._load_models() self.voice_registry = self._load_voice_models() self.memory_system = self._init_memory_system() self.planning_system = self._init_planning_system() self.voice_processor = self._init_voice_processor() self.video_processor = self._init_video_processor() self.audio_queue = queue.Queue() self.video_queue = queue.Queue() def _load_capabilities(self) -> Dict[str, AgentCapability]: """Load all agent capabilities with voice actor features""" capabilities = { "voice_acting": AgentCapability( name="Voice Acting", description="Professional voice acting with emotional expression and character voices", category="multimodal", tools=["voice_synthesis", "emotion_analysis", "character_voice", "audio_processing"], models=["elevenlabs", "openai_tts", "whisper", "claude_3"], workflows=["voice_generation", "character_development", "script_analysis"], voice_enabled=True ), "video_production": AgentCapability( name="Video Production", description="Create professional videos with AI-generated content and voice-overs", category="multimodal", tools=["video_generation", "scene_composition", "audio_sync", "visual_effects"], models=["stable_diffusion", "runway_ml", "dall_e_3", "midjourney"], workflows=["video_script", "scene_planning", "post_production"], voice_enabled=True ), "character_development": AgentCapability( name="Character Development", description="Create and develop complex AI characters with unique personalities", category="ai_development", tools=["personality_engine", "memory_system", "behavior_modeling"], models=["gpt-4", "claude-3-sonnet", "anthropic_character"], workflows=["character_creation", "personality_training", "interaction_modeling"], voice_enabled=True ), "advanced_code_generation": AgentCapability( name="Advanced Code Generation", description="Generate complex, production-ready code with voice explanations", category="development", tools=["codebase_search", "file_read", "file_write", "run_terminal_cmd", "voice_code_explanation"], models=["gpt-4", "claude-3-sonnet", "codellama", "github_copilot"], workflows=["code_review", "bug_fix", "feature_implementation", "architecture_design"], voice_enabled=True ), "workflow_orchestration": AgentCapability( name="Workflow Orchestration", description="Orchestrate complex workflows with voice-guided execution", category="automation", tools=["workflow_executor", "api_integration", "data_processing", "voice_guidance"], models=["gpt-4", "claude-3-sonnet"], workflows=["data_pipeline", "api_automation", "notification_system", "voice_automation"], voice_enabled=True ), "multimodal_ai_analysis": AgentCapability( name="Multimodal AI Analysis", description="Analyze data, generate insights, and create reports with voice narration", category="analytics", tools=["data_analysis", "visualization", "report_generation", "voice_narration"], models=["gpt-4", "claude-3-sonnet", "dall-e-3", "whisper"], workflows=["data_analysis", "report_generation", "insight_extraction", "voice_presentation"], voice_enabled=True ), "system_integration": AgentCapability( name="System Integration", description="Integrate with external APIs and services with voice feedback", category="integration", tools=["api_client", "webhook_handler", "database_connector", "voice_status"], models=["gpt-4", "claude-3-sonnet"], workflows=["api_integration", "data_sync", "service_orchestration", "voice_monitoring"], voice_enabled=True ) } return capabilities def _load_tools(self) -> Dict[str, Dict]: """Load all available tools including voice and video processing""" tools = {} # Load Cursor tools try: with open("Cursor Prompts/Agent Tools v1.0.json", "r") as f: cursor_tools = json.load(f) for tool in cursor_tools: tools[f"cursor_{tool['name']}"] = tool except FileNotFoundError: logger.warning("Cursor tools not found") # Load Manus tools try: with open("Manus Agent Tools & Prompt/tools.json", "r") as f: manus_tools = json.load(f) for tool in manus_tools: if "function" in tool: tools[f"manus_{tool['function']['name']}"] = tool except FileNotFoundError: logger.warning("Manus tools not found") # Add revolutionary tools tools.update({ "voice_synthesis": { "name": "voice_synthesis", "description": "Generate high-quality voice synthesis with emotional expression", "parameters": { "text": {"type": "string"}, "voice_id": {"type": "string"}, "emotion": {"type": "string", "enum": ["happy", "sad", "angry", "calm", "excited", "professional"]}, "speed": {"type": "number", "minimum": 0.5, "maximum": 2.0}, "pitch": {"type": "number", "minimum": -20, "maximum": 20} } }, "character_voice": { "name": "character_voice", "description": "Create and manage character voices with unique personalities", "parameters": { "character_name": {"type": "string"}, "personality": {"type": "object"}, "voice_characteristics": {"type": "object"}, "dialogue": {"type": "string"} } }, "video_generation": { "name": "video_generation", "description": "Generate professional videos with AI content and voice-overs", "parameters": { "prompt": {"type": "string"}, "duration": {"type": "integer"}, "resolution": {"type": "string"}, "style": {"type": "string"}, "voice_over": {"type": "string"} } }, "emotion_analysis": { "name": "emotion_analysis", "description": "Analyze emotional content in text, audio, and video", "parameters": { "content": {"type": "string"}, "content_type": {"type": "string", "enum": ["text", "audio", "video"]}, "analysis_depth": {"type": "string", "enum": ["basic", "detailed", "comprehensive"]} } }, "ai_model_caller": { "name": "ai_model_caller", "description": "Call various AI models for different tasks", "parameters": { "model": {"type": "string"}, "prompt": {"type": "string"}, "parameters": {"type": "object"} } }, "workflow_executor": { "name": "workflow_executor", "description": "Execute n8n workflows with voice-guided execution", "parameters": { "workflow_name": {"type": "string"}, "input_data": {"type": "object"}, "execution_mode": {"type": "string", "enum": ["sync", "async", "streaming", "voice_guided"]} } } }) return tools def _load_models(self) -> Dict[str, Dict]: """Load available AI models including voice and video models""" return { "gpt-4": { "provider": "openai", "capabilities": ["text", "code", "reasoning"], "max_tokens": 8192 }, "gpt-4-vision": { "provider": "openai", "capabilities": ["text", "image", "code", "reasoning"], "max_tokens": 4096 }, "claude-3-sonnet": { "provider": "anthropic", "capabilities": ["text", "code", "reasoning"], "max_tokens": 200000 }, "claude-3-sonnet-vision": { "provider": "anthropic", "capabilities": ["text", "image", "code", "reasoning"], "max_tokens": 200000 }, "elevenlabs": { "provider": "elevenlabs", "capabilities": ["voice_synthesis", "voice_cloning", "emotion_control"], "max_tokens": None }, "whisper": { "provider": "openai", "capabilities": ["audio_transcription", "language_detection"], "max_tokens": None }, "stable-diffusion": { "provider": "replicate", "capabilities": ["image_generation", "video_generation"], "max_tokens": None }, "runway_ml": { "provider": "runway", "capabilities": ["video_generation", "video_editing"], "max_tokens": None } } def _load_voice_models(self) -> Dict[str, Dict]: """Load available voice models and characters""" return { "eleven_multilingual_v2": { "provider": "elevenlabs", "languages": ["en", "es", "fr", "de", "it", "pt", "pl", "hi", "ja", "ko", "zh"], "emotions": ["happy", "sad", "angry", "calm", "excited", "professional"], "voices": { "adam": "pNInz6obpgDQGcFmaJgB", "bella": "EXAVITQu4vr4xnSDxMaL", "charlie": "VR6AewLTigWG4xSOukaG", "diana": "21m00Tcm4TlvDq8ikWAM", "eve": "AZnzlk1XvdvUeBnXmlld" } }, "openai_tts": { "provider": "openai", "voices": ["alloy", "echo", "fable", "onyx", "nova", "shimmer"], "formats": ["mp3", "opus", "aac", "flac"] }, "edge_tts": { "provider": "microsoft", "voices": ["en-US-JennyNeural", "en-US-GuyNeural", "en-GB-SoniaNeural"], "languages": ["en-US", "en-GB", "es-ES", "fr-FR", "de-DE"] } } def _init_memory_system(self): """Initialize advanced memory system""" return { "short_term": [], "long_term": {}, "character_memories": {}, "conversation_history": [], "task_memory": {} } def _init_planning_system(self): """Initialize advanced planning system""" return { "current_plan": None, "task_queue": [], "execution_history": [], "planning_engine": "advanced" } def _init_voice_processor(self): """Initialize voice processing capabilities""" return { "synthesis_engine": "elevenlabs", "recognition_engine": "whisper", "emotion_analyzer": "advanced", "character_voices": {}, "audio_cache": {} } def _init_video_processor(self): """Initialize video processing capabilities""" return { "generation_engine": "stable_diffusion", "editing_engine": "moviepy", "composition_engine": "advanced", "video_cache": {}, "scene_templates": {} } # Initialize the agent agent = RevolutionaryAIAgent() # API Routes @app.get("/", response_class=HTMLResponse) async def root(): """Main dashboard for the Revolutionary AI Agent""" html_content = """ Revolutionary AI Agent System

🎭 Revolutionary AI Agent System

The Most Advanced AI Agent with Professional Voice Actor Capabilities

🚀 System Status: Online and ready to revolutionize AI interaction

🎤 Voice Actor Demo

Experience the revolutionary voice capabilities

Revolutionary Capabilities

🎭 Voice Acting

Professional voice acting with emotional expression and character voices

  • Multi-language voice synthesis
  • Emotional expression control
  • Character voice development
  • Real-time voice generation

🎬 Video Production

Create professional videos with AI-generated content and voice-overs

  • AI video generation
  • Professional voice-overs
  • Scene composition
  • Visual effects integration

👤 Character Development

Create and develop complex AI characters with unique personalities

  • Personality engine
  • Memory systems
  • Behavior modeling
  • Character voice training

💻 Advanced Code Generation

Generate complex, production-ready code with voice explanations

  • Multi-language support
  • Voice code explanations
  • Architecture design
  • Real-time collaboration

⚙️ Workflow Orchestration

Orchestrate complex workflows with voice-guided execution

  • Voice-guided automation
  • Complex workflow execution
  • Real-time monitoring
  • Intelligent decision making

📊 Multimodal AI Analysis

Analyze data, generate insights, and create reports with voice narration

  • Voice-narrated reports
  • Data visualization
  • Predictive analytics
  • Interactive presentations
🔗 API Documentation: Available at /docs
""" 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)