An AI-powered script analysis and semantic concept matching platform that helps screenwriters, producers, and studios evaluate story concepts against historical movie datasets, predict potential ROI, and analyze commercial risk.
- Semantic Similarity Search: Finds top comparable movies using ChromaDB and Sentence-Transformers (
all-MiniLM-L6-v2) based on narrative plot summaries. - ROI & Financial Benchmarking: Calculates average budgets, worldwide revenues, and historical success rates.
- TMDb Integration: Fetches real-time movie posters and metadata dynamically.
- Qualitative Risk Assessment: Generates executive recommendations and risk levels based on historical comps.
The platform is powered by a custom-curated historical movie dataset located in data/processed/.
- Data Pipeline: Raw movie data is cleaned and structured using custom Python scripts (
scripts/build_dataset.py). - Vector Indexing: The plot summaries and metadata are embedded and indexed into a local ChromaDB vector database via
scripts/index_vectors.py. - Metrics Tracked: Each record maps out critical financial and narrative attributes including production budgets, worldwide box-office collections, ROI calculations, and genres.
Here is a quick look at the platform in action:
- Backend: Python, Flask (
3.0.2) - Vector Database: ChromaDB (
0.5.x) - ML / NLP: Sentence-Transformers, PyTorch
- APIs: The Movie Database (TMDb) API