Reasoning with o1

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NeuraHack

Dayan_Atif

This Streamlit app enables users to upload images and have them classified using the pre-trained MobileNetV2 model. MobileNetV2, trained on the ImageNet dataset, is efficient and designed for mobile and edge devices. Users can upload a `.jpg`, `.png`, or `.jpeg` image, which is then displayed and resized to meet the model's input requirements (224x224 pixels). The image is preprocessed and passed to the model for prediction.

Once the model generates predictions, the app decodes the results and shows the top predicted class label alongside the confidence score as a percentage. The app provides a simple, user-friendly interface for image classification, allowing real-time interaction with a powerful deep learning model.

This application is ideal for users looking to explore image classification with minimal setup and a smooth interface, leveraging Streamlit's intuitive design and MobileNetV2's lightweight architecture for accurate and fast results.

This Streamlit app lets users upload an image, which is classified using the MobileNetV2 model. It processes the image, makes a prediction, and displays the predicted class label along with the confidence level.

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