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AI-Based-Humanoid-Broadcaster 🤖📰

Description

The AI-Based-Humanoid-Broadcaster project focuses on developing humanoid broadcasters utilizing Natural Language Processing (NLP) techniques to assist in providing summaries of news articles. With the help of this project, readers can easily obtain summarized short news articles.

Technologies Used

  • Python 🐍
  • NLP (Natural Language Processing) 📝
  • Data Cleaning 🧹
  • Text Pre-processing 📑
  • BERT (Bidirectional Encoder Representations from Transformers) 🤖
  • T5 (Text-To-Text Transfer Transformer) 📝➡️📝
  • Pickle (Python object serialization) 🥒
  • PyTorch Lightning ⚡
  • PyTorch 🔥
  • TensorFlow 🧠
  • Flask Framework 🌐

Usage

This project consists of functionalities for summarizing news articles, generating headlines, and classifying news articles into different categories. Here's a brief overview of the functionalities:

Summarization Function

The summarization function utilizes T5 model for text summarization. It takes a text input and generates a summary of the input text.

Headline Generation Function

The headline generation function generates a headline for a given news article using the T5 model.

Classification Function

The classification function utilizes BERT model for text classification. It classifies news articles into predefined categories such as SPORTS, BUSINESS, TRAVEL, etc.

Input from User

The application allows users to input a text message (presumably a news article), and then it provides the summarized text, generated headline, and classification of the article based on the predefined categories.

How to Run

  1. Clone this repository:

    git clone https://github.com/your-username/AI-Based-Humanoid-Broadcaster.git
cd AI-Based-Humanoid-Broadcaster

  2. Create a virtual environment (optional but recommended):

    # For virtualenv
virtualenv venv
# For venv (Python 3 built-in)
python3 -m venv venv

  3. Activate the virtual environment:

    • On Windows: 
      venv\Scripts\activate
    • On macOS and Linux: 
      source venv/bin/activate

  4. Install the required dependencies:

    pip install -r requirements.txt

  5. Run the Flask application:

    python app.py

  6. Access the application by navigating to http://localhost:5000 in your web browser.

Demo Video and Screenshots