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š± From Machine Learning Model to API Testing
This is one of my recent projects where I integrated a TensorFlow Lite (TFLite) leaf disease classification model with a Python-based Flask application and tested the model inference through Postman.
The current prototype allows an image to be sent to the application, processed by the TFLite model, and returned with a predicted leaf disease classification. Iām currently working on the API integration and testing stage as part of the process of turning the trained ML model into a service that can eventually be integrated into a mobile or web application.
š ļø Technologies & Tools
ā¢ Python
ā¢ Flask
ā¢ TensorFlow Lite (TFLite)
ā¢ Machine Learning / Image Classification
ā¢ REST API Development
ā¢ Postman
ā¢ Android Integration ā planned next
This project is helping me build practical experience in taking a trained machine learning model beyond the notebook and preparing it for real-world application integration.
I'm interested in projects involving Android development, machine learning integration, AI-powered applications, REST APIs, and intelligent mobile solutions.
š Have an ML model that you want to integrate into an application? Let's connect.
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