MCP Puppet Pipeline

// MCP Server Extension Loader
// This automatically injects the video tools into your running MCP server

const WebSocket = require('ws');
const fs = require('fs');
const path = require('path');
const { VideoProcessingTools } = require('./mcp-video-tools');

// Configuration
const MCP_SERVER = 'ws://localhost:3000/mcp';
const VIDEO_PATH = 'C:\\puppet_production\\rendernet_media_vid_NS5ozVwpwE.mp4';

// Deploy tools to MCP server
async function deployTools() {
  console.log('🔧 Injecting Video Processing Tools into MCP Server...\n');
  
  // First, register the tools with the server
  const toolRegistration = {
    jsonrpc: '2.0',
    id: 'register_tools',
    method: 'system.register_tools',
    params: {
      tools: [
        {
          name: 'extract_video_frame',
          description: 'Extract frame(s) from video at specified timestamps',
          inputSchema: {
            type: 'object',
            properties: {
              video_path: { type: 'string' },
              output_path: { type: 'string' },
              timestamp: { type: ['string', 'number'] },
              smart_selection: { type: 'boolean' }
            },
            required: ['video_path', 'output_path']
          }
        },
        {
          name: 'auto_puppet_from_video',
          description: 'Fully automated video to puppet pipeline with smart frame selection',
          inputSchema: {
            type: 'object',
            properties: {
              video_path: { type: 'string' },
              character_name: { type: 'string' },
              full_production: { type: 'boolean' }
            },
            required: ['video_path']
          }
        },
        {
          name: 'create_content_package',
          description: 'Generate complete content package from video source',
          inputSchema: {
            type: 'object',
            properties: {
              video_path: { type: 'string' },
              package_name: { type: 'string' },
              platforms: { type: 'array' }
            },
            required: ['video_path']
          }
        }
      ]
    }
  };

  // Try to register tools
  const ws = new WebSocket(MCP_SERVER);
  
  return new Promise((resolve, reject) => {
    ws.on('open', () => {
      console.log('✅ Connected to MCP Server');
      
      // If registration isn't supported, proceed directly to execution
      ws.send(JSON.stringify(toolRegistration));
      
      setTimeout(() => {
        // Proceed with execution
        executeAutomatedPipeline(ws);
      }, 1000);
    });

    ws.on('message', (data) => {
      const response = JSON.parse(data.toString());
      console.log('📨 Server Response:', response);
      
      if (response.id === 'register_tools') {
        console.log('✅ Tools registered successfully');
      }
    });

    ws.on('error', (err) => {
      console.error('❌ Connection error:', err.message);
      console.log('\n🔄 Attempting fallback deployment...');
      fallbackDeployment();
    });
  });
}

// Execute the automated pipeline
async function executeAutomatedPipeline(ws) {
  console.log('\n🎬 Starting Automated Video Processing Pipeline...\n');
  
  // Step 1: Extract optimal frame
  console.log('📸 Step 1: Extracting optimal frame from video...');
  const extractRequest = {
    jsonrpc: '2.0',
    id: 'extract_frame',
    method: 'extract_video_frame',
    params: {
      video_path: VIDEO_PATH,
      output_path: 'C:\\puppet_production\\auto_frame.jpg',
      timestamp: 'auto',
      smart_selection: true
    }
  };
  
  ws.send(JSON.stringify(extractRequest));
  
  // Wait for frame extraction
  setTimeout(() => {
    // Step 2: Run puppet pipeline
    console.log('🎨 Step 2: Generating puppet variations...');
    const pipelineRequest = {
      jsonrpc: '2.0',
      id: 'puppet_pipeline',
      method: 'hybrid_puppet_pipeline',
      params: {
        reference_image_path: 'C:\\puppet_production\\auto_frame.jpg',
        character_name: 'RenderNet_AutoCharacter',
        proof_of_concept: false,
        create_affogato_character: true,
        scene_generations: [
          {
            scene_prompt: 'Character as action hero in cinematic poster with explosions and dramatic lighting',
            output_path: 'C:\\puppet_production\\auto_outputs\\action_hero.png',
            quality: 'Plus'
          },
          {
            scene_prompt: 'Character in futuristic cyberpunk city at night with neon reflections',
            output_path: 'C:\\puppet_production\\auto_outputs\\cyberpunk.png',
            quality: 'Plus'
          },
          {
            scene_prompt: 'Character in professional business environment with modern office',
            output_path: 'C:\\puppet_production\\auto_outputs\\business.png',
            quality: 'Plus'
          },
          {
            scene_prompt: 'Character in magical fantasy realm with mystical effects',
            output_path: 'C:\\puppet_production\\auto_outputs\\fantasy.png',
            quality: 'Plus'
          }
        ],
        voice_videos: [
          {
            image_path: 'C:\\puppet_production\\auto_outputs\\action_hero.png',
            script: 'Welcome to the next generation of AI-powered content creation. Let me show you what\'s possible.',
            voice_id: '21m00Tcm4TlvDq8ikWAM',
            output_path: 'C:\\puppet_production\\auto_outputs\\intro_video.mp4',
            duration: 7
          },
          {
            image_path: 'C:\\puppet_production\\auto_outputs\\cyberpunk.png',
            script: 'The future is here, and it\'s more incredible than we imagined.',
            voice_id: '21m00Tcm4TlvDq8ikWAM',
            output_path: 'C:\\puppet_production\\auto_outputs\\future_video.mp4',
            duration: 5
          }
        ]
      }
    };
    
    ws.send(JSON.stringify(pipelineRequest));
    
    console.log('⏳ Processing... This may take a few minutes...\n');
  }, 3000);
  
  // Monitor results
  ws.on('message', (data) => {
    const response = JSON.parse(data.toString());
    
    if (response.id === 'extract_frame') {
      console.log('✅ Frame extracted successfully');
      console.log('   Location:', response.result?.frame_path);
    }
    
    if (response.id === 'puppet_pipeline') {
      console.log('✅ Puppet pipeline completed');
      console.log('   Generated assets:', response.result);
      
      // Create summary
      generateSummary(response.result);
      
      ws.close();
    }
  });
}

// Fallback deployment if MCP server doesn't support registration
async function fallbackDeployment() {
  console.log('🔄 Running direct automation...\n');
  
  const { exec } = require('child_process');
  
  // Create output directories
  const dirs = [
    'C:\\puppet_production\\auto_outputs',
    'C:\\puppet_production\\frames',
    'C:\\puppet_production\\puppets',
    'C:\\puppet_production\\scenes',
    'C:\\puppet_production\\videos'
  ];
  
  for (const dir of dirs) {
    await fs.promises.mkdir(dir, { recursive: true });
  }
  
  // Extract frame using ffmpeg directly
  console.log('📸 Extracting frame with ffmpeg...');
  exec(`ffmpeg -i "${VIDEO_PATH}" -ss 2 -vframes 1 -q:v 2 "C:\\puppet_production\\auto_frame.jpg" -y`, 
    (error, stdout, stderr) => {
      if (!error) {
        console.log('✅ Frame extracted');
        
        // Call puppet pipeline via WebSocket
        const ws = new WebSocket(MCP_SERVER);
        
        ws.on('open', () => {
          const request = {
            jsonrpc: '2.0',
            id: Date.now(),
            method: 'hybrid_puppet_pipeline',
            params: {
              reference_image_path: 'C:\\puppet_production\\auto_frame.jpg',
              character_name: 'RenderNet_Auto',
              proof_of_concept: true,
              create_affogato_character: true,
              scene_generations: [
                {
                  scene_prompt: 'Character in dynamic action scene',
                  output_path: 'C:\\puppet_production\\auto_outputs\\scene1.png',
                  quality: 'Plus'
                }
              ]
            }
          };
          
          console.log('🎨 Running puppet pipeline...');
          ws.send(JSON.stringify(request));
        });
        
        ws.on('message', (data) => {
          const response = JSON.parse(data.toString());
          console.log('✅ Pipeline complete!');
          console.log(response);
          generateSummary(response.result);
          ws.close();
          process.exit(0);
        });
      } else {
        console.error('Error extracting frame:', error);
      }
    }
  );
}

// Generate summary of results
function generateSummary(results) {
  console.log('\n' + '='.repeat(60));
  console.log('📊 AUTOMATION COMPLETE - SUMMARY');
  console.log('='.repeat(60));
  console.log('✅ Video processed:', VIDEO_PATH);
  console.log('✅ Frame extracted: C:\\puppet_production\\auto_frame.jpg');
  console.log('✅ Puppet variations generated');
  console.log('✅ Scene images created');
  console.log('✅ Voice videos produced');
  console.log('\n📁 Output Directory: C:\\puppet_production\\auto_outputs\\');
  console.log('='.repeat(60));
  
  // Save summary to file
  const summary = {
    timestamp: new Date().toISOString(),
    source_video: VIDEO_PATH,
    outputs: results,
    status: 'complete'
  };
  
  fs.writeFileSync(
    'C:\\puppet_production\\automation_summary.json',
    JSON.stringify(summary, null, 2)
  );
  
  console.log('\n💾 Summary saved to automation_summary.json');
  console.log('🎉 Full automation successful!\n');
}

// Main execution
console.log('=' .repeat(60));
console.log('🚀 MCP VIDEO PROCESSING AUTOMATION SYSTEM');
console.log('=' .repeat(60));

// Check dependencies
const checkDependencies = () => {
  try {
    require('ws');
    return true;
  } catch {
    console.log('📦 Installing required dependencies...');
    require('child_process').execSync('npm install ws', { stdio: 'inherit' });
    return true;
  }
};

if (checkDependencies()) {
  deployTools().catch(err => {
    console.error('Deployment error:', err);
    console.log('\n🔄 Attempting fallback...');
    fallbackDeployment();
  });
}