my_model/tabs/readme.txt

Directory Overview: This directory contains all the atreamlit application pages:

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## 1. home.py
the `home.py` displays an introduction to the application with brief background and description of the application tools. 

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## 2. results.py
The `results.py` module manages the interactive Streamlit demo for visualizing model evaluation results and analysis. 
It provides an interface for users to explore different aspects of model performance and evaluation samples.

Notes:
Ensure the necessary dependencies are installed and properly configured.
The `run_demo` function relies on the ResultDemonstrator class to generate plots and display results.

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## 3. run_inference.py
The `run_inference.py` is responsible for the running inference to test and use the fine-tuned models. 
It manages the user interface and interactions for a Streamlit-based Knowledge-Based Visual Question 
Answering (KBVQA) application. 
This module handles image uploads, displays sample images, and facilitates the question-answering process 
using the KBVQA model.

Notes:
- Ensure the necessary dependencies are installed and properly configured.
- The `InferenceRunner` class relies on the KBVQA model to generate answers to questions based on image analysis.

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## 4. model_arch.py
The `model_arch.py` displays the model architecture and accompanying abstract and design details for the 
Knowledge-Based Visual Question Answering (KB-VQA) model.

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## 5. dataset_analysis.py
The dataset_analysis.py module provides tools for analyzing and visualizing distributions of question types 
within given question datasets for Knowledge-Based Visual Question Answering (KBVQA). It supports operations 
such as data loading, categorization of questions, visualization, and exporting data to CSV files. This module 
leverages Streamlit for interactive visualization and Altair for plotting.

Notes:
Ensure the necessary dependencies are installed and properly configured.
The `OKVQADatasetAnalyzer` class leverages `Altair` for creating interactive visualizations and `Streamlit` for displaying these visualizations in a web app format.
The `run_dataset_analyzer` function provides an overview of the dataset and utilizes the OKVQADatasetAnalyzer to visualize the data.
This module has a dependency on the `process_okvqa_dataset` function from `my_model.dataset.dataset_processor`.