Image Classifier Deployment Template for Product Photos

Added Jun 2025 3 design docs

Many teams train a capable image classification model and then stall at the last step: nobody can use a model that only lives in a notebook. E-commerce companies in particular need lightweight ways to put product photo classifiers, for example distinguishing categories of merchandise, in front of catalog and operations staff quickly. The intern builds a reusable deployment template with Streamlit that wraps a pre-trained TensorFlow image classifier in a clean web interface. Users upload a product photo, the app preprocesses it to the model's expected input format, and predictions are displayed live with class probabilities and a simple explanation view showing why the model favored its top choice. The intern structures the code so that swapping in a different trained model requires minimal changes, which is what makes the project a template rather than a one-off demo, and documents the steps a teammate would follow to reuse it. Though scoped for beginners, the project delivers a professional lesson: the intern learns the standard pattern for serving an image model behind a Python web interface, practices handling user-supplied images defensively, and finishes with a working, demonstrable deployment that proves they can carry a computer vision model over the line from training artifact to usable product.

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