- Updated auth.py to load and save users from/to MongoDB instead of a JSON file.
- Refactored gallery.py to manage gallery entries in MongoDB, removing local file dependencies.
- Introduced mongo.py for MongoDB connection handling and index management.
- Removed obsolete JSON data files for users and gallery entries.
- Updated create_user.py to create users directly in MongoDB.
This change enhances data persistence and scalability by leveraging MongoDB.
- Introduced `canva_export.py` for handling Canva OAuth (PKCE), token storage, and asset uploads.
- Added functions to manage user tokens and pending states for OAuth.
- Implemented API endpoints for connecting to Canva, handling callbacks, and checking connection status.
- Enhanced frontend to support exporting creatives to Canva and managing connection status.
- Updated gallery and creatives components to integrate Canva functionality and improve user experience.
Refactor ad creative generation and analysis: Updated the analyze_product and generate_ad_creatives functions to accept target audience segments for tailored analysis and creative generation. Enhanced the frontend to include a multi-select dropdown for target audiences, improving user experience. Adjusted styles for the new dropdown component and ensured proper handling of product image URLs for better ad results.
Update ad creative generation and dependencies: Increased the number of ad concepts generated from 18 to 20, with a new distribution of product-focused and no-product creatives. Added a function to fetch upcoming Indian festivals for contextual relevance in ad copy. Updated requirements to include duckduckgo-search for festival data retrieval.
Enhance product analysis and ad creative generation: Updated analysis prompts for specificity and performance focus, refined price handling in creatives, improved gallery entry loading logic, and added new creative entries. Adjusted App component to streamline state management and removed unused prompt state.
