MemesClassifier-Flask-Python

Memes Classification uses ML and Flask to analyze meme sentiment. Valuable tool for social media managers, marketers, and content creators. The Memes Classification project is a Machine Learning-based solution implemented in Python that aims to classify memes based on their sentiment. The project is integrated with Flask to provide a web-based GUI that allows users to upload memes, extract their context, and classify them into one of three categories: Positive, Neutral, or Negative. The classification is performed using a set of pre-trained models on a dataset of over 7,000 memes.

The solution is designed to be user-friendly and accessible to both technical and non-technical users. Users can easily upload their memes through the web interface, and the solution processes them automatically, providing the sentiment classification in real-time. The pre-trained models used in the classification are trained on a diverse dataset of memes, which ensures that the solution is accurate and reliable.

The Memes Classification project is an excellent example of how Machine Learning can be applied to analyze the sentiment of a popular form of social media content. By providing a user-friendly web interface, the solution can be used by anyone to quickly and easily classify their memes, making it an ideal tool for social media managers, marketers, and content creators.

# Data Description

The Memes Classification project includes the following files and folders:

• app.py: A Flask app file containing the Flask code integrated with the Machine Learning models used in the project.
• index.html: A webpage file containing the user interface for the project.
• models/: A folder containing all the pre-trained models used in the project.
• data.zip: A compressed file containing all the data used in the project.
• canny.py: A file containing the image preprocessing code using Canny filters.
• implementation.py: A file containing all the code used for preprocessing and generating the final output. The Memes Classification project uses Flask for web integration and includes pre-trained Machine Learning models for sentiment analysis. The app.py file provides Flask integration with the Machine Learning models, and the index.html file provides the user interface. The models/ folder contains all the pre-trained models used in the project, and the data.zip file includes all the data used for the project. The canny.py file provides the image preprocessing using Canny filters, and the implementation.py file contains all the code used for preprocessing and generating the final output.