Here's a README file that describes the two separate code components: the image captioning model and the Flask application that uses this model.

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ImageCaptionAI

ImageCaptionAI is a comprehensive project for generating image captions using deep learning. It consists of two main components:

1. Image Captioning Model: A deep learning model trained to generate captions for images.
2. Flask Application: A web application that serves the model for real-time image captioning.

📁 Project Overview

1. Image Captioning Model

This component includes the code to preprocess images, train the model, and generate captions. It utilizes:

• ResNet50 for extracting high-level image features.
• LSTM for generating captions from these features.
• GloVe Embeddings to enhance caption accuracy.

Key Files:

• image_captioning_model.py: Contains code for preprocessing images, encoding them, and generating captions.
• train_model.py: Script to train the captioning model.
• data_preprocessing.py: Handles data cleaning and tokenization for training.

Main Functions:

• preprocess_img(img): Preprocesses the image for ResNet50.
• encode_image(img): Extracts features from the image using ResNet50.
• predict_caption(photo): Generates a caption for the image features using the trained LSTM model.

2. Flask Application

The Flask application serves as an interface to interact with the image captioning model. Users can upload images through a web interface, and the server will respond with generated captions.

Key Files:

• app.py: The main Flask application file that handles image uploads and returns generated captions.
• requirements.txt: Lists all necessary Python dependencies for running the Flask app.

Main Functions:

• predict(): Endpoint that accepts image uploads, processes the image, and returns the generated caption.

🛠️ Getting Started

Prerequisites

• Python 3.x
• TensorFlow 2.x
• Keras
• Flask
• Other dependencies listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/Vamsi404/ImageCaptionAI.git
   cd ImageCaptionAI

2. Install dependencies:

   pip install -r requirements.txt

3. Download the dataset and embeddings:

   • Flickr8k dataset
   • GloVe embeddings (50-dimensional)

   Place these files in the appropriate directories (/data and /glove).

Usage

1. Image Captioning Model

To use the model directly, run the following script to generate a caption for an image:

python image_captioning_model.py --image_path /path/to/image.jpg

2. Flask Application

1. Start the Flask server:

   python app.py

   By default, the server will be accessible at http://127.0.0.1:5000/.

2. Upload an image through the web interface:

   Navigate to http://127.0.0.1:5000/ and use the provided form to upload an image. The server will return the generated caption.

🔗 Link Between Components

The Image Captioning Model and the Flask Application are linked as follows:

• Model: The trained image captioning model (model_9.h5) is loaded in the Flask application to generate captions for uploaded images.
• Flask App: Handles HTTP requests, processes the uploaded image, and uses the model's functions to generate and return captions.

app.py uses functions from image_captioning_model.py to process images and generate captions. The Flask server interacts with the model to provide real-time captioning through a web interface.

📁 Directory Structure

• data/: Contains datasets and processed files.
• glove/: Contains GloVe embedding files.
• src/: Source code for preprocessing, model training, and inference.
• flask_app/: Flask application code.
• requirements.txt: Python dependencies for the Flask app.

🤝 Contributing

Contributions are welcome! Please refer to CONTRIBUTING.md for guidelines on how to contribute to the project.

📬 Contact

For questions or feedback, please open an issue or contact mail.