anshi02 / knife_detector_webapp

A simple webapp which allows user to upload images and detect whether it is a knife image or not using tensorflow.js

Knife Detection Web Application

This web application uses TensorFlow.js to detect whether an uploaded image contains a knife or not. It's a simple example of image classification using a pre-trained machine learning model.

Getting Started

Follow these steps to set up and run the Knife Detection Web Application locally:

Prerequisites

You will need:

• Python (for server-side processing)
• TensorFlow.js (for client-side machine learning)
• Flask (for the web server)

Installation

Clone this repository to your local machine:

git clone https://github.com/your-username/knife-detection.git

Getting Started

1. Navigate to the Project Directory:

   cd knife-detection

Usage

Convert Your Keras Model

In your Python environment, use the tensorflowjs_converter tool to convert your Keras model to TensorFlow.js format. Run the following command in your terminal/command prompt:

tensorflowjs_converter --input_format keras path/to/knife_detection_model.h5 path/to/converted_model

Replace path/to/knife_detection_model.h5 with the path to your saved Keras model, and path/to/converted_model with the desired output directory where the TensorFlow.js model will be saved.

Run the Web Application

Start the Flask web server by running:

python app.py

This will start the server, and you should see output indicating that the app is running locally.

Open a Web Browser Open a web browser and navigate to http://localhost. You should see the Knife Detection Web Application.

How to Use

Upload an Image:

Use the "Choose File" button to upload an image.

Detect Knife or Not:

Click the "Detect" button to classify the uploaded image as "Knife" or "Not Knife."

View Result:

The result will be displayed on the web page.