CASE STUDY LAUNCH_02
Kalpdrushti
Immersive Photo-Based AI Video Generator
Python
Django
Tailwind CSS
GSAP
The Challenge
Traditional video editing is time-consuming and computationally heavy on local machines. Automating video creation from images requires high-performance asynchronous processes and dynamic media queues.
The Solutions & Architecture
Built an AI media pipeline website that takes static imagery inputs, processes them through media interpolation scripts, and generates fluid high-definition video files.
Software Requirements Specification (SRS)
### 1. Introduction
#### 1.1 Purpose
Specifies requirements for the Kalpdrushti Photo-Based AI Video Generator.
#### 1.2 Scope
Enables static image ingestion, frame interpolation logic execution, and fluid video render assembly.
### 2. System Description
#### 2.1 Product Perspective
AI-driven rendering web application utilizing asynchronous task queuing for file compression.
#### 2.2 Operating Environment
- Backend: Python / Django.
- Task Queue: Celery/Redis (or simulated threading).
### 3. Functional Requirements
- **FR1 (Image Ingestion)**: Ingest multiple JPG/PNG images.
- **FR2 (Keyframe Control)**: Adjust keyframe rendering parameters.
- **FR3 (Video Compilation)**: Generate and download MP4 video files.
### 4. Non-Functional Requirements
- **NFR1 (Performance)**: Video compilation completes in under 60 seconds.
- **NFR2 (Usability)**: Smooth animations powered by GSAP.
Metrics Impact
Performance Shift
Enables content creators to build marketing reels and cinematic slideshows in under 60 seconds with simple zero-setup web interfaces.
Launch Profile
- Status: PRODUCTION
- Deploy Node: Vercel Ready
- Category: Full-Stack Django