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AI-Based Recipe Recommender #146
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Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊 |
Hi @UppuluriKalyani, The AI-Based Recipe Recommender sounds like a fantastic project, and I would love to contribute! I have experience in building web applications and working with natural language processing (NLP), which will be useful for matching ingredients to recipes. Additionally, I'm comfortable with Flask/Django and working with external APIs like Spoonacular. Could you kindly assign this issue to me? |
@JahnaviDhanaSri Proceed |
Hey @JahnaviDhanaSri, can you share the progress of this project? |
This issue has been automatically closed because it has been inactive for more than 30 days. If you believe this is still relevant, feel free to reopen it or create a new one. Thank you! |
Project Title: AI-Based Recipe Recommender
Overview:
Build an AI-powered web application that recommends recipes based on ingredients the user inputs. The user can list ingredients they have on hand, and the model will suggest various recipes that can be made with those ingredients. Additionally, users can filter recipes based on dietary preferences (vegetarian, vegan, gluten-free, etc.), cuisine type, or time required.
Key Features:
Ingredient Input: Users can input the ingredients they have, either through text or voice.
Recipe Recommendation: The application will return a list of recipes, ranked by relevance to the ingredients.
Filters: Users can filter recipes by preferences like vegan, vegetarian, or dietary restrictions.
Steps Display: Once a recipe is selected, show step-by-step cooking instructions.
Difficulty Level: Display recipe difficulty (easy, medium, hard).
Save and Share: Users can save favorite recipes or share them on social media.
Input:
List of ingredients.
Optional filters (dietary preferences, cuisine type, etc.).
Output:
A ranked list of recipes that can be made using the provided ingredients.
Recipe details: ingredients required, steps, cooking time, and difficulty level.
Tools/Tech:
Frontend: Streamlit (or any web framework like React).
Backend: Flask/Django.
Machine Learning: NLP for matching ingredients to recipes (could use a pre-built recipe dataset or API).
Database: MongoDB, Firebase, or any other NoSQL database to store user preferences and recipes.
External APIs: You can use recipe APIs like Spoonacular for fetching recipes.
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