sniff_ai

Sniff AI

Sniff AI is an AI-powered fragrance creation platform that translates poetic and descriptive text into custom fragrance compositions. This project combines generative AI with a curated fragrance database to inspire and generate scent profiles, showcasing both Product Management and AI/ML engineering capabilities.

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Table of Contents

• Project Overview
• Key Features
• Tech Stack
• Project Phases & Progress
• Installation
• Usage
• Future Enhancements

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Project Overview

Sniff AI generates custom fragrance compositions based on user-provided descriptions, ranging from simple phrases to poetic imagery. The platform leverages a dataset of existing fragrances for model training and reference, creating unique blends that align with user input.

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Key Features

• Text-to-Fragrance Generation: Transform descriptive language into a unique fragrance profile.
• Fragrance Database: Access to a searchable database of existing fragrances with detailed fragrance notes, scent families, and additional information.
• Customizable Interface: User-friendly UI for inputting descriptions, selecting preferred notes, and viewing generated fragrance compositions.
• Feedback System: A mechanism for users to rate generated fragrances, aiding in model refinement.

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Tech Stack

• Backend: Python, Flask or Django
• Frontend: React or Vue.js
• Database: PostgreSQL with Elasticsearch for fast search
• Modeling: Generative AI (e.g., GPT or custom transformer models) and NLP

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Project Phases & Progress

Phase 1: Research and Data Collection

• Gather fragrance data from online databases and public sources
• Curate dataset of fragrance notes, descriptions, and scent families
• Compile paired data of poetic descriptions and fragrance compositions for initial training

Dataset was built in this project Fragrances

Phase 2: Initial Model Development

• Fine-tune language model (GPT, BERT, or similar) on paired text-fragrance data
• Train fragrance composition model to create blends from semantic interpretations of descriptions
• Evaluate model outputs to ensure relevance and accuracy using similarity scores

Phase 3: Building the Database

• Set up relational database (PostgreSQL) for storing fragrance data
• Integrate Elasticsearch for efficient fragrance search
• Populate database with curated fragrance data

Phase 4: User Interface and Experience

• Design user interface mockups for text input and fragrance output display
• Implement frontend in React or Vue.js
• Integrate model with UI for real-time or asynchronous fragrance generation

Phase 5: Model Refinement

• Establish user feedback loops allowing users to rate or tweak generated fragrances
• Integrate feedback mechanism to refine model outputs based on user interactions
• Iterate on model improvements based on feedback and testing

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Installation

Instructions for setting up the project locally will go here.

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Usage

Instructions for using Sniff AI will go here, including how to input descriptions, select fragrance notes, and view outputs.

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Future Enhancements

• Additional Input Customization: Allow users to select fragrance intensity, season, or occasion.
• Improved Fragrance Matching: Refine similarity scoring for more precise fragrance outputs.
• User Accounts and Saved Fragrances: Enable users to save generated compositions to their profiles.
• Mobile Version: Develop a responsive mobile version of the platform.

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Feel free to contribute, suggest features, or reach out with feedback. This project is in progress, and any input is appreciated!

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