There are a few different approaches you can take to input processed information from your web app into your AI model. Here are some general steps you can follow:
Understanding your Ai model
First, you would have to know what platform your deep learning framework was built with, such as TensorFlow, Keras, or PyTorch and how it expects to receive your data. The most common programming languages and libraries, including Python, Node.js, and Ruby are widely accepted.
Determine the input format required by your AI model
Before you can send data to your AI model, you need to know what format it expects. Depending on the model, this could be text, images, audio, or some other data format.
How to Preprocess the data
Once you know what format your model expects, you’ll need to preprocess the data from your web app to make it compatible with the model. For example, if your model requires images, you might need to resize and normalize the images before sending them to the model.
Define an API endpoint
You’ll need to define an API endpoint on your web app that your AI model can use to receive data. This endpoint should accept data in the required format for your model and perform any necessary preprocessing.
Send data to the endpoint
Once you have your API endpoint set up, you can send data to it from your web app using HTTP requests. You can use libraries like requests in Python to send POST requests to your endpoint.
Process the data in your AI model
Once your AI model receives the data, it can process it and return a prediction or other output. You’ll need to define another API endpoint to receive the output from your model and integrate it back into your web app.
Overall, the specific implementation details will depend on the AI model you’re using and the web framework you’re using to build your app. However, this general process should give you an idea of how to integrate your web app with your AI model.