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AppleRx

Apple Disease Recognition System 🍎

Model Setup Instructions ⬇️⬇️

To use this project, you need to download a pre-trained model from the given Google Drive link and place it in the models directory. Follow the steps below to set it up correctly:

To use this project run

git clone https://github.com/InvernessPeak/AppleRx.git

cd machine-learning-projects

pip install -r requirements.txt

Steps to Download and Place the Model

  1. Download the Model

    • Click [here](---Link of Our Project----) to open the Google Drive link.
    • Click the Download button to save the file to your local system.
  2. Create the Models Folder

    • Navigate to the root directory of this project.
    • Create a folder named models if it does not already exist.
      mkdir models
  3. Place the Model in the Folder

    • Move the downloaded file into the models directory.
      mv /path/to/downloaded/model models/
      Replace /path/to/downloaded/model with the actual path where you downloaded the file.
  4. Verify the Setup

    • Ensure that the model file is correctly placed in the models directory by listing the folder's contents:
      ls models
      You should see the downloaded model file in the output.

Usage

  1. Specify the Model File Location

    • Open the app.py file in a text editor.
    • Locate line 8, which contains the following code:
      tf.keras.models.load_model("")
    • Update the empty string with the relative path to the model file. For example:
      tf.keras.models.load_model("models/your_model_file.keras")
      Replace your_model_file.keras with the actual name of the model file you downloaded.
  2. Run the Server

    • Open a terminal and navigate to the root directory of this project.
    • Run the following command to start the server:
      python app.py
  3. Access the Application

    • Once the server is running, follow the instructions displayed in the terminal to access the application in your web browser.

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Apple Disease Recognition System Web Version

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  • HTML 50.3%
  • CSS 25.7%
  • Python 24.0%