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Hateful Meme Classification

An AI-powered App's - Code Documentation

Introduction

This documentation provides a comprehensive overview of the code for the AI-powered Streamlit application. It details the functionalities offered, technical dependencies, and code structure, serving as a valuable resource for developers and users alike.

Functionalities

The application leverages Google GenerativeAI to deliver a range of functionalities:

  • Hateful Meme Classification: Identify and classify potentially offensive or NSFW content within images using image classification techniques.
  • Chatbot Interaction: Engage in stimulating conversations with a chatbot that utilizes natural language processing for responsive interactions.
  • Random Joke Generation: Generate laughter on demand with the random joke generator.
  • Random Meme Stream: Immerse yourself in a continuous stream of entertaining memes retrieved from a dedicated online API.
  • Research Paper Exploration: Gain valuable insights into relevant research papers through informative summaries and readily accessible links.

Technical Dependencies

The application relies on the following Python libraries:

  • Streamlit: Streamlines the creation of interactive web app interfaces.
  • Requests: Facilitates communication with external services like APIs.
  • JSON: Enables parsing of JSON-formatted data received from APIs.
  • Google GenerativeAI: Provides pre-trained models for image classification and text generation.
  • OS (Optional): May be used for environment variable management.
  • Dotenv (Optional): Enables secure loading of environment variables from a .env file (likely for API tokens).
  • Pillow (PIL.Image): Provides functionalities for image processing.

Code Breakdown

The code is structured to handle various functionalities and user interactions:

  • Functionalities:
    • generate_text(uploaded_file, prompt): Manages image upload, prompt input, image classification using a pre-trained model, and error handling for hateful/NSFW content and invalid images.
    • chatbot(prompt): Processes user prompts and generates responses using a pre-trained model, with exception management for potential errors.
    • get_random_meme() and get_random_joke(): Fetch random memes and jokes from external APIs, respectively, and handle potential errors during the process.
  • User Interface (Streamlit):
    • st.set_page_config: Sets the title of the app.
    • st.sidebar components: Create a navigation menu for users to select functionalities.
    • Conditional statements (if selected_page == "Home" etc.): Dynamically display content based on user selection.
    • Various Streamlit elements (st.title, st.text_area, st.button, st.image, etc.): Build an informative and interactive user interface.

To run this project, activate your virtual environment and type the following commands:

pip install -r requirements.txt

create ".env" file in your current folder and define your google api key variable (i.e. GOOGLE_API_KEY='[YOUR_API_KEY]')

streamlit run HMC.py

This will install the necessary dependencies and then start the Streamlit application.