abishekra/image-classification

Image-Classification

This repository contains a simple Flask web application for image classification using a pre-trained neural network model. Users can upload an image through the web interface, and the application will predict whether the image contains a cat or a dog. Additionally, the prediction results are stored in a MySQL database along with the uploaded image.

Prerequisites: Before running the application, ensure you have the following prerequisites installed:

Python Flask TensorFlow NumPy MySQL Connector Other dependencies as listed in the README file

You can install the dependencies using the following command:

bash Copy code pip install -r requirements.txt Setup MySQL Database Setup:

Make sure you have MySQL installed and running on your system:

Create a database named predicted_logs. Update the database connection details in the app.py file (host, user, password). Model File:

Ensure that you have a trained model file (cat_dog_classifier.h5) available. You can use your own pre-trained model or train one using relevant data. Run the Application:

Execute the app.py script to start the Flask application:

bash Copy code python app.py The application will be accessible at http://127.0.0.1:5000/ in your web browser.

Usage:

Access the web interface at http://127.0.0.1:5000/. Upload an image using the provided form. Click the "Predict" button to submit the image for classification. The result will be displayed on the web page along with the uploaded image.

Project Structure:

app.py: The main Flask application script containing the web application logic. uploads: A folder where uploaded images are temporarily stored. cat_dog_classifier.h5: Pre-trained neural network model for cat and dog classification. templates: Folder containing HTML templates for rendering the web interface. Database Schema

The MySQL database (predicted_logs) has a table named prediction_data with the following columns:

id: Auto-incremented primary key. image: Binary data of the uploaded image. predicted_result: The predicted result (e.g., "It's a cat"). prediction_time: Timestamp indicating when the prediction was made.

License:

This project is licensed under the MIT License.