🎵 StreamBreaker AI

StreamBreaker AI is a multi-model machine learning pipeline designed to help independent artists predict their streaming success and automatically generate data-driven marketing strategies for their music.

This is our collaborative Capstone Project, utilizing acoustic feature analysis, natural language processing for lyrics, and Large Language Models (LLMs) to create an end-to-end orchestration platform.

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👥 The Team & Architecture

The application is structured into four distinct modules orchestrated together:

• Model 1: Prediction Engine (Harsh)

  • Analyzes 14+ Spotify audio features (e.g., danceability, energy, valence).
  • Uses an XGBoost Classifier to predict if a track will hit the 1,000-stream threshold within its first 90 days.

• Model 2: NLP Lyric Analyzer (Stephanie)

  • Evaluates raw lyrics targeting emotional impact and catchiness.
  • Extracts metrics such as Sentiment, Lexical Diversity, Hook Repetition, and Semantic Coherence using TextBlob.

• Model 3: LLM Marketing Strategist (Miguel)

  • Synthesizes the prediction scores and NLP features to formulate an actionable marketing blueprint.
  • Supports multiple backends: Ollama (local/free execution), Groq, and OpenAI.

• Model 4: Application Orchestrator & UI (Gopi Krishna)

  • Connects the three models into a unified pipeline.
  • Wraps the infrastructure into a dynamic, beautiful Streamlit web application.

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🚀 Features

• Spotify Attribute Toggles: Adjust sliders to mirror your song's BPM, energy, danceability, and more.
• Copy & Paste Lyrics: Receive real-time insights on your song's hook repetition and vocabulary richness.
• Flexible AI Integration: Bring your own API key for fast inference (OpenAI) or use local AI (Ollama) to keep costs at zero.
• Actionable Advice: Outputs budget allocations and platform-specific targeting strategies for IG/TikTok/Spotify.

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🛠️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/katkurigopi05/StreambreakerAi.git
   cd StreambreakerAi

2. Set up a virtual environment (Recommended):

   python3 -m venv venv
   source venv/bin/activate  # On macOS/Linux
   # .\venv\Scripts\activate  # On Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Add API Keys (Optional but Recommended): Create a .streamlit/secrets.toml file to securely store your OpenAI or Groq keys. This ensures the cloud deployment works right out of the box!

   OPENAI_API_KEY = "sk-proj-YOUR-API-KEY-HERE"

5. Run the Streamlit app:

   streamlit run app.py

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☁️ Deployment

StreamBreaker AI is fully optimized for Streamlit Community Cloud.

• Simply link this repository to your Streamlit Cloud account.
• Note: Ensure you paste your OPENAI_API_KEY into the Streamlit Cloud App Settings -> Secrets section since local .streamlit/ folders are safely ignored by git.

📄 License & Case Studies

Be sure to check out CASE_STUDIES.md and TEAM_INTEGRATION.md for our performance benchmarks, testing history, and simulated campaign trackings.

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Built for the future of independent artist promotion.