Sample Recipe Generator

This repository contains a sample project for a recipe generator application built using Python, FastAPI, Streamlit, Supabase, and Gemini. The application allows users to upload images of items in their refrigerator, analyzes the images, and provides recipe suggestions.

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🚀 Getting Started

Open Using Daytona

1. Install Daytona: Follow the Daytona installation guide.

2. Create the Workspace:

   daytona create https://github.com/anurag-b72/ai-recipe-generator

3. Start the Application:

   uvicorn main:app --reload
   streamlit run streamlit_app.py

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Prerequisites

1. Install Dependencies:
   Ensure you have Python installed and run the following command to install the required libraries:

   pip install -r requirements.txt

2. Set up Environment Variables:
   Create a .env file in the project root directory and add the following configurations:

   # Configure Gemini API and Supabase
   SUPABASE_URL = "https://example.supabase.co"
   SUPABASE_KEY = "your_supabase_key"
   GEMINI_API_KEY = "your_gemini_key"

   Replace your_supabase_key and your_gemini_key with your actual Supabase and Gemini API keys.

3. Set up Supabase:
   Create a table in your Supabase database using the following SQL query:

   CREATE TABLE recipes (
       id SERIAL PRIMARY KEY,
       file_id UUID NOT NULL,
       file_name TEXT NOT NULL,
       recipe_title TEXT NOT NULL,
       recipe_description TEXT NOT NULL,
       recipe_ingredients TEXT[] NOT NULL,
       recipe_making_time TEXT NOT NULL,
       recipe_instructions_to_make TEXT[] NOT NULL,
       recipe_number_of_people_servings TEXT NOT NULL
   );

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Backend Setup

To start the FastAPI backend, run:

uvicorn main:app --reload

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Frontend Setup

To start the Streamlit frontend, run:

streamlit run streamlit_app.py

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📷 Screen Shots

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✨ Features

• AI-Powered Recipe Suggestions: Analyze uploaded images using Gemini to generate recipes.
• Interactive UI: Built with Streamlit for an intuitive user experience.
• Database Integration: Store recipe data in Supabase.
• Modular Architecture: Clean separation between backend and frontend.

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This project demonstrates how to combine FastAPI, Streamlit, and Supabase with AI capabilities to build a practical application. Feel free to explore, modify, and enhance!