CineSense-Agent 🎬🍿

A system that goes beyond simple recommendations to actively craft a hyper-personalized cinematic experience. CineSense-Agent analyzes a user's emotional state, viewing history, and real-time biometric data to generate a custom movie trailer, script, or short film tailored to their current mood and preferences.

✨ Features

• Emotion-Driven Content Generation: An "Emotion Agent" processes real-time user data to gauge emotional state, which then informs the creative output.
• Multi-Modal Analysis: Utilizes VideoMAE-v2 to analyze video, AudioCLIP to understand audio cues, and a vision model to track facial expressions and gaze for a comprehensive understanding of user engagement.
• AI-Powered Creative Synthesis: A "Creative Agent" uses a large language model like Llama-3 for scriptwriting, while a "Synthesis Agent" combines video and audio generation models to produce the final, bespoke output.
• Semantic Cinematic Retrieval: Creates a vector space of cinematic concepts and styles, allowing the system to intelligently retrieve and combine elements from a vast library of films.
• Real-time Processing: The core backend, built with Rust, ensures low-latency, real-time processing of multi-modal inputs, facilitated by tRPC for efficient communication.

⚙️ Tech Stack

1. High-Performance Core (Processing)

• Language: Rust

• Why: Used for the core-engine to handle high-frequency data streams (biometrics/video) with zero-cost abstractions and memory safety.

• Computer Vision: OpenCV (Rust Bindings)

• Why: Real-time frame capture, normalization, and pre-processing before sending data to AI models.

• Concurrency: Tokio

• Why: Asynchronous runtime to manage multi-modal input channels without blocking the execution thread.

2. AI & Inference Layer (Intelligence)

• Language: Python 3.10+

• Video Analysis: VideoMAE-v2

• Why: A state-of-the-art masked autoencoder that excels at understanding temporal actions and micro-expressions.

• Audio Analysis: AudioCLIP

• Why: Multi-modal version of CLIP that understands the relationship between audio frequencies and concepts/emotions.

• Creative Logic: Llama-3 (8B or 70B)

• Why: Advanced reasoning capabilities for translating emotion vectors into structured cinematic screenplays.

• Model Serving: FastAPI + Uvicorn

• Why: Provides a high-performance bridge for the Rust engine to "talk" to the Python models via local POST requests.

3. Orchestration & API (The Glue)

• Framework: Node.js & Express

• Communication: tRPC

• Why: Provides end-to-end type safety between the backend and frontend. It eliminates the need for manual API documentation and prevents runtime errors.

• Validation: Zod

• Why: Strict schema validation for all data moving between the user and the agents.

4. Database & Semantic Retrieval

• Database: MongoDB Atlas
• Vector Engine: Atlas Vector Search
• Why: Allows for "Semantic Cinematic Retrieval." We store cinematic styles as high-dimensional vectors and retrieve them using cosine similarity based on the user's emotion.

5. Frontend & UI (Experience)

• Framework: Next.js 14+ (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• Icons & UI: Lucide React & Framer Motion
• Why: Used to animate the real-time "Emotion Heatmaps" and the smooth transition of generated scripts.

6. DevOps & Deployment

• Containerization: Docker & Docker Compose

• Why: Manages the complex environment requirements (CUDA for Python, OpenCV for Rust, and Node packages) in a single command.

• Hardware Acceleration: NVIDIA Container Toolkit

• Why: Enables the Docker containers to access your local GPU for real-time AI inference.

🚀 Getting Started

Prerequisites

• Rust
• Python 3.10+
• Node.js (for a potential front-end)
• Access to the Llama-3 API

Installation

1. Clone the repository:

   git clone https://github.com/saadsalmanakram/CineSense-Agent.git
   cd CineSense-Agent

2. Set up the Rust backend:

   cargo build --release

3. Set up the Python environment:

   # Install required Python libraries
   pip install -r requirements.txt

Configuration

Create a .env file to store your API keys and configuration variables for models like Llama-3.

LLAMA_API_KEY=your_llama3_api_key

Usage

To start the CineSense-Agent, run the main executable and provide it with user input, such as biometric data streams or viewing history.

./target/release/cinesense-agent --user-data-stream-path /path/to/data.json

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To wrap everything up, here is the finalized, comprehensive directory structure for CineSense-Agent. This structure organizes your multi-language stack (Rust, Python, TypeScript) into a clean, modular monorepo that is ready for Docker orchestration.

📂 Project Structure

CineSense-Agent/
├── apps/
│   ├── web/                    # Next.js (Frontend)
│   │   ├── src/
│   │   │   ├── components/     # UI: Dashboard, VideoPlayer, EmotionChart
│   │   │   ├── hooks/          # Custom hooks for tRPC and WebSockets
│   │   │   ├── utils/          # tRPC client configuration
│   │   │   └── app/            # App Router (Pages & Layouts)
│   │   ├── tailwind.config.ts
│   │   └── package.json
│   │
│   └── server/                 # Node.js/Express (Orchestrator)
│       ├── src/
│       │   ├── trpc/           # Router definitions & Procedures
│       │   ├── services/       # Llama-3 API & Vector Search logic
│       │   ├── models/         # MongoDB Mongoose schemas
│       │   └── index.ts        # Entry point
│       ├── tsconfig.json
│       └── package.json
│
├── core-engine/                # Rust (Data Processing)
│   ├── src/
│   │   ├── main.rs             # OpenCV loop & Buffer streaming
│   │   ├── emotion_agent/      # Biometric normalization logic
│   │   └── fusion/             # Perceiver IO data prep
│   ├── Cargo.toml
│   └── Cargo.lock
│
├── models/                     # Python (AI Inference)
│   ├── video_mae/              # VideoMAE-v2 weights & logic
│   ├── audioclip/              # AudioCLIP weights & logic
│   ├── inference_server.py     # FastAPI server (the bridge)
│   ├── requirements.txt        # AI dependencies
│   └── Dockerfile              # GPU-enabled container config
│
├── scripts/                    # Utility scripts for data seeding
│   └── seed_concepts.ts        # Script to upload movie vectors to MongoDB
│
├── .env                        # API keys & DB URIs
├── docker-compose.yml          # Full system orchestration
└── README.md                   # Project documentation

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