HyperEdit - AI Video Editor

A video editing application that uses AI to process natural language editing requests and verifies LLM responses on-chain using Hyperbolic's Agent Kit.

Pitch

https://www.youtube.com/watch?v=Qemztd5XH18

Prerequisites

• Node.js 18+
• Python 3.11+
• Poetry
• Docker and Docker Compose
• Anthropic API Key (for Claude)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/Project-Jarvis.git
cd Project-Jarvis

2. Install Python dependencies:

poetry install

3. Install frontend dependencies:

cd frontend
npm install

4. Set up environment variables:

# In project root
cp .env.example .env
# Add your Anthropic API key to .env

cd llm_inference_verifier directory
cp .env.example .env
# Add your Alchemy API key to .env

Running the Application

1. Start the LLM Verification Services:

cd llm_inference_verifier
docker-compose up -d

2. Start the Backend Server:

# In project root
poetry run python -m uvicorn api.main:app --reload --port 8000

3. Start the Frontend Development Server:

# In frontend directory
npm run dev

4. Access the application:

• Frontend: http://localhost:3001
• Backend API: http://localhost:8000

Usage

1. Upload one or more video files (supported formats: MP4, MOV, AVI)
2. Enter your editing request in natural language
3. Click "Process Video" to start the editing process
4. Once complete, you can:
   • View the processed video
   • Check the on-chain verification details
   • Download the result

Architecture Details

• Frontend: Next.js application with modern UI components

  • Modern UI built with Radix UI and Tailwind
  • Real-time video processing status updates
  • Seamless video upload and preview

• Backend: FastAPI server handling video processing and LLM interactions

  • Asynchronous video processing pipeline
  • Integration with Claude 3.5 for natural language understanding
  • File management for video uploads and processed results
  • Error handling and status reporting

• Video Toolkit: Core processing engine using OpenCV and MoviePy

  • GPU-accelerated video processing
  • Support for multiple video formats
  • Effects processing
  • Customizable video layouts and transitions

• LLM Verification: Autonomous Verification Service (AVS) powered by Othentic

  • Decentralized verification of LLM responses
  • Multi-attester consensus mechanism
  • Components:
    • Aggregator: Manages consensus and transaction submission
    • Attesters: Verify LLM responses independently
    • Validation Service: Validates response correctness
    • Execution Service: Handles transaction execution

Environment Setup

LLM Verification Environment

The LLM verification service uses Othentic's AVS framework. Set up the .env in llm_inference_verifier:

1. Set up operator keys:

AVS_GOVERNANCE_ADDRESS=
ATTESTATION_CENTER_ADDRESS=

PRIVATE_KEY_PERFORMER=
PRIVATE_KEY_AGGREGATOR=
PRIVATE_KEY_ATTESTER1=
PRIVATE_KEY_ATTESTER2=
PRIVATE_KEY_ATTESTER3=
PRIVATE_KEY_SYNCER=
PRIVATE_KEY_DEPLOYER=

PINATA_API_KEY=7824585a98fe36414d68
PINATA_SECRET_API_KEY=41a53a837879721969e73008d91180df30dbc66097c7f75f08cd5489176b43ea
IPFS_HOST=https://othentic.mypinata.cloud/ipfs/

OTHENTIC_BOOTSTRAP_ID=12D3KooWBNFG1QjuF3UKAKvqhdXcxh9iBmj88cM5eU2EK5Pa91KB
OTHENTIC_BOOTSTRAP_SEED=97a64de0fb18532d4ce56fb35b730aedec993032b533f783b04c9175d465d9bf
LLM_BEARER_TOKEN=

References

• Othentic AVS Documentation
• Hyperbolic Agent Kit Documentation
• LLM Inference

License

This project is licensed under the MIT License - see the LICENSE file for details.