BigBasket AI-Powered Dynamic Banner and Video Generator

🚀 Project Overview

Welcome to the BigBasket AI-Powered Dynamic Banner and Video Generator! This innovative project revolutionizes promotional content creation for e-commerce platforms, specifically tailored for BigBasket's marketing needs. By leveraging cutting-edge AI technologies, we automate and enhance the process of generating eye-catching banners and videos for various promotional campaigns.

🎯 Problem Statement

In the fast-paced world of e-commerce, creating engaging and personalized promotional content at scale is a significant challenge. BigBasket needs a solution that can:

• Rapidly generate high-quality banners and videos for multiple products and offers
• Maintain brand consistency across all promotional materials
• Adapt to various themes and seasonal events
• Optimize content for different platforms and formats

💡 Our Solution

Our AI-powered system takes the following inputs:

• Product Images: Multiple images showcasing products
• Promotional Offer Details: Sale information, discounts, and special offers
• Color Palette: Brand-specific colors for consistency
• Theme: Festive or event-based themes (e.g., Diwali, Independence Day)
• Output Specifications: Desired size, resolution, and format (banner or video)

And produces:

• Dynamic, visually appealing banners
• Engaging promotional videos
• Content optimized for various platforms and devices

🛠️ Key Features

1. AI-Powered Background Generation: Utilizes advanced diffusion models to create contextually relevant backgrounds for product images.
2. Intelligent Text Generation: Employs Google's Gemini AI to craft compelling and concise promotional copy.
3. Dynamic Layout Engine: Automatically arranges elements (images, text, logos) in multiple appealing layouts.
4. Theme-Based Customization: Adapts designs to match seasonal events and festivities.
5. Multi-Format Output: Generates both static banners and dynamic video content.
6. Brand Consistency: Ensures all generated content adheres to BigBasket's brand guidelines.

🚀 Technologies Used

• Python
• PyTorch
• Diffusers library
• Google Generative AI (Gemini)
• Pillow (PIL) for image processing
• Gradio for user interface

📊 Impact and Benefits

• Time Efficiency: Reduces banner and video creation time from hours to minutes.
• Cost Reduction: Minimizes the need for large design teams for promotional content.
• Scalability: Easily scales to handle thousands of products and offers simultaneously.
• Consistency: Ensures brand coherence across all generated content.
• Personalization: Enables creation of tailored promotions for different customer segments.

🏆 Why This Project Stands Out

1. Innovation: Combines multiple AI technologies for a comprehensive solution.
2. Practical Application: Solves a real-world problem for a major e-commerce player.
3. Scalability: Designed to handle large-scale operations of BigBasket.
4. Versatility: Adaptable to various promotional needs and themes.
5. User-Friendly: Intuitive interface makes it accessible to marketing teams.

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/bigbasket-ai-promo-generator.git
   cd bigbasket-ai-promo-generator

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

   If you prefer to install packages individually, you can use the following commands:

   pip install torch
   pip install diffusers
   pip install google-generativeai
   pip install Pillow
   pip install gradio
   pip install transparent-background

4. Set up your Google API key:

   export GOOGLE_API_KEY='your_api_key_here'

   On Windows, use set instead of export.

Usage

1. Run the app script:

   python app.py

2. Access the Gradio interface by opening the URL provided in the console output.