NEW SwiftUI Image Analyzer Will Change How You Handle Sensitive Content

[@kavinda_dilshan_paramsoodi]

Good stuff. loved it.

[@appstuff]

Appreciate it!

[@Spacer-l3j]

You can't do much with this. It won't work if its disabled in the user's settings and by default it is disabled. We need to find a way to send the user an alert to enable it or have it enabled automatically when you install the app otherwise this is... useless... no one will manually enable it

[@appstuff]

You can check the status of the policy and notify the user that it needs to be turned on. Ultimately it’s useful for analyzing sensitive content without implementing a third party api

[@Spacer-l3j]

@@appstuffabsolutely, it's a very performant API i have this implemented in my current project which is a car marketplace. The user is not able to upload any car images or profile if they are sensitive but i'm stuck to the above point i need to find a logic to make the user enable it damn i love this API so much man you posted just in time about this thank you chief

[@DimNovo]

1. Open Xcode on Your Mac:
- In the top menu, select `Xcode > Open Developer Tool > Create ML`.
- In the window that appears, select `New Document` and set the project type to `Image Classifier`.
2. Setting Up Data (Datasets):
- Collecting Data:
- Find and download datasets to train the model.
Ensure you have at least two classes of images corresponding
to different categories.
- For example:
- SFW / NSFW
3. Setting Up the Dataset:
- Organize the images into folders, with each folder corresponding
to one of the classes. For example:
- SFW folder / NSFW folder
4. Training the Model:
- Setting Up Parameters:
- Set the parameters as needed.
- Start Training:
- Click the `Train` button and wait for the process to complete.
- Monitor the training process and the metrics on the graph to
ensure the model is improving.
5. Evaluate the Model:
- After training is complete, assess the results on the test set.
- If the model demonstrates sufficient accuracy, proceed to the next step.
If not, adjust the training parameters and run the training process again.
6. Integrate the Model into Your Xcode Project:
- Save the trained model by selecting `Save Model` in Create ML.
- Ensure the model is saved with a `.mlmodel` extension.
- Add the Model to Your Project:
- Drag the `.mlmodel` file into the project in the Project Navigator.
- Set Up the Code to Work with the Model:
- Write the necessary code to integrate the model.
- Ensure that the image is correctly passed to the model -
- (remember - the model love pixels)
7. Conclusion:
- You now have a image classification model integrated into your Xcode project.
- Good luck! 💪🦾💪