🌿 Plant Detector

🌐 Live Demo

Experience the application here: Plant Detector on Render

⚠ Note: This application is hosted on a free server, which may enter sleep mode when not in use. The first request might take a few seconds to wake up the server.

📖 Description

Plant Detector is an AI-powered web application designed to identify plant species from user-uploaded images. Utilizing advanced image recognition techniques, it provides users with quick and accurate plant identifications.

🚀 Features

• ⚡ Instant Analysis: Quickly identifies plant species from images.
• 🤖 AI-Powered: Leverages machine learning models for precise recognition.
• 🎨 User-Friendly Interface: Simplified design for easy image uploads and results viewing.

🛠️ Installation

📌 Prerequisites

Before installing, ensure you have the required dependencies:

1. Check if Python is installed:

   python3 --version

   If Python is not installed, you can install it using the terminal:

   For Ubuntu/Debian:

   sudo apt update && sudo apt install python3

   For macOS (using Homebrew):

   If Homebrew is not installed, install it using:

   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

   brew install python3

   For Windows: Download and install it from Python's official site, then ensure python3 is added to your system PATH.

2. Check if pip is installed:

   pip3 --version

   If pip is missing, install it using:

   python3 -m ensurepip --default-pip

3. Ensure Git is installed:

   git --version

   If Git is not installed, follow the instructions on Git's official website.

4. Clone the repository:

   git clone https://github.com/Devehab/dream-interpretation.git
   cd dream-interpretation

5. Obtain a Google API Key:

   • Visit Google AI Studio.
   • Sign in with your Google account.
   • Navigate to the API section and generate a new API key.
   • Copy the generated API key.

6. Create a .env file and store the API key:

   • Open a terminal inside the project directory.
   • In the terminal, navigate to your project directory and run the following command to create the .env file:

     touch .env

   • Open the .env file with a text editor and add the following line:

     GOOGLE_API_KEY=your-api-key-here
     

   • Save and close the file.

7. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

8. Install dependencies:

   pip3 install -r requirements.txt

9. Run the application locally:

   python3 app.py

🚀 Deploying to a Live Server (Render)

You can deploy this application on a live server like Render by following these steps:

1. Create a Render account:

   • Go to Render and sign up.

2. Create a new web service:

   • Click on "New" -> "Web Service".
   • Connect your GitHub repository and select dream-interpretation.
   • Choose the Python runtime and configure the environment.

3. Set up environment variables:

   • In the Render dashboard, go to the "Environment" section.
   • Add GOOGLE_API_KEY with your API key.

4. Define the start command:

   gunicorn -w 4 -b 0.0.0.0:3000 app:app

5. Deploy and monitor:

   • Click "Deploy" and wait for the build to complete.
   • Your application will be live at the provided Render URL.

🐳 Docker Deployment

🚀 Option 1: Quick Start with Docker Compose

The fastest way to get up and running:

# Create a directory for the application
mkdir plant-detector
cd plant-detector

# Download only the compose.yml file from GitHub
curl -O https://raw.githubusercontent.com/Devehab/plant-detector/main/compose.yml

# Edit the compose.yml file to add your Google API key
# Replace 'your_api_key' with your actual Google API key

# Run with Docker Compose
docker compose up -d

This will pull the pre-built image from Docker Hub and start the application with all necessary configurations. Access the application at: http://localhost:3000

🛠️ Option 2: Manual Docker Setup

Step 1: Build the Docker Image

For Single Architecture:

docker build -t plant-detector .

For Multiple Architectures (AMD64 & ARM64):

# Create and use a buildx builder
docker buildx create --use

# Build and push the multi-architecture image
# Replace 'yourusername' with your Docker Hub username
docker buildx build --platform linux/amd64,linux/arm64 -t yourusername/plant-detector:latest --push .

This builds the image for both AMD64 (standard servers) and ARM64 (Mac M1/M2, Raspberry Pi) architectures. The --push flag uploads the image to Docker Hub. Remove it if you only want to build locally.

Step 2: Run the Container

Using environment variable directly:

docker run -d -p 3000:3000 -e GOOGLE_API_KEY=your_api_key plant-detector

Or using .env file:

docker run -d -p 3000:3000 --env-file .env plant-detector

Step 3: Access the Application

Open your browser and navigate to http://localhost:3000

📋 Useful Docker Commands

# View running containers
docker ps

# Stop the container
docker stop <container_id>

# Remove the container
docker rm <container_id>

🎯 Usage

Once the application is running, open a web browser and go to http://127.0.0.1:3000 or the deployed Render URL. Enter your dream description and click the "Interpret" button to receive an analysis.

🤝 Contributing

We welcome contributions! To contribute:

1. Fork the repository by clicking on the "Fork" button at the top right.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Make your changes and commit them.
4. Push the changes:

   git push origin feature/your-feature-name

5. Create a Pull Request on the original repository.

📜 License

This project is licensed under the MIT License. For more details, see the LICENSE file.

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🚀 Feel free to open issues or discussions for suggestions and improvements!