Tales Of Puran
Project Description: Puran Story to Animation Generator This project is an AI-powered storytelling and animation system that transforms user prompts into narrated mythological stories and animated videos based on Hindu Puranic texts. The system uses a local Large Language Model (LLM) to generate context-aware stories with references from curated Puran datasets. It then converts these stories into visual scenes, generates images, adds voice narration, and compiles everything into a video animation. Key Features: π§ Domain-specific LLM trained on Hindu Purans π Semantic search (embeddings + vector DB) for accurate context retrieval βοΈ Story generation with citations π¬ Automatic scene breakdown from story π¨ AI-based image generation for each scene π Text-to-speech narration π₯ Video creation using generated visuals + audio π» Interactive UI using Streamlit π§© Tech Stack: LLM: Local models via Ollama Embeddings: Sentence Transformers Vector DB: ChromaDB Image Generation: Diffusers (or Stable Diffusion) Audio: pyttsx3 / TTS Video: FFmpeg UI: Streamlit π― Workflow: User Prompt β Retrieve Context β Generate Story β Split into Scenes β Generate Images β Generate Voice β Combine β Video Output π‘ Purpose: The project aims to: Make ancient Puranic knowledge more engaging Combine AI + storytelling + visualization Provide an interactive learning and entertainment tool Project Description: Puran Story to Animation Generator This project is an AI-powered storytelling and animation system that transforms user prompts into narrated mythological stories and animated videos based on Hindu Puranic texts. The system uses a local Large Language Model (LLM) to generate context-aware stories with references from curated Puran datasets. It then converts these stories into visual scenes, gener
Rank
43
Safety
79
Updated
Oct 9, 2026
Source
GitLab
About
What it does, and when to use it.
Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Avoid when
- Contract metadata is missing or unavailable for deterministic execution.
Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing
Public facts
Every fact links back to the source it came from.
Install and run
Setup complexity: low.
- Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
- Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Contract: missing
curl -s "https://www.xpersona.co/api/v1/agents/gitlab-public-projects-nir-p-parikh-group-tales-of-puran/snapshot"
Documentation
GitLab
1,506 characters of source documentation, loaded on request.
Machine-readable data
The same record, as JSON, for agents and crawlers.
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