Update my_model/tabs/home.py

my_model/tabs/home.py

@@ -2,26 +2,94 @@ import streamlit as st
 import streamlit.components.v1 as components
 
 
-def run_home():
+def run_home() -> None:
     """
     Displays the home page for the Knowledge-Based Visual Question Answering (KB-VQA) project using Streamlit.
     This function sets up the main home page for demonstrating the project.
+
+    Returns:
+        None
     """
 
-    st.markdown(
-                """
-                <div style="text-align: center;">
-                    <h1>Multimodal Learning for Visual Question Answering using World Knowledge</h1>
-                    <h2>Knowledge-Based Visual Question Answering (KB-VQA)</h2>
-                </div>
-                """,
-                unsafe_allow_html=True
-                )
-    
-    st.write("""\n\n\nThis is an interactive application built to demonstrate the project developed and allow for interaction with the KB-VQA model as part of the dissertation for Masters degree in Artificial Intelligence at the [University of Bath](https://www.bath.ac.uk/). 
-                    \n\n\nDeveloped by: [Mohammed H AlHaj](https://www.linkedin.com/in/m7mdal7aj)""")
+    st.markdown("""
+            <div style="text-align: justify;">
+            
+            \n\n\n**Welcome to the interactive application for the Knowledge-Based Visual Question Answering (KB-VQA) 
+            project. This application is an integral part of a 
+            [Master’s dissertation in Artificial Intelligence](https://info.online.bath.ac.uk/msai/) at the 
+            [University of Bath](https://www.bath.ac.uk/). As we delve into the fascinating world of VQA, I invite you 
+            to explore the intersection of visual perception, language understanding, and cutting-edge AI research.**
+            </div>""",
+                unsafe_allow_html=True)
+    st.markdown("### Background")
+    with st.expander("Read Background"):
+        st.write("""
+                <div style="text-align: justify;">
+                
+                Since its inception by **Alan Turing** in 1950, the **Turing Test** has been a fundamental benchmark for 
+                evaluating machine intelligence against human standards. As technology evolves, so too must the criteria 
+                for assessing AI. The **Visual Turing Test** represents a modern extension that includes visual cognition 
+                within the scope of AI evaluation. At the forefront of this advancement is **Visual Question Answering 
+                (VQA)**, a field that challenges AI systems to perceive, comprehend, and articulate insights about 
+                visual inputs in natural language. This progression reflects the complex interplay between perception 
+                and cognition that characterizes human intelligence, positioning VQA as a crucial metric for gauging 
+                AI’s ability to emulate human-like understanding.
+                
+                Mature VQA systems hold transformative potential across various domains. In robotics, VQA systems can 
+                enhance autonomous decision-making by enabling robots to interpret and respond to visual cues. In 
+                medical imaging and diagnosis, VQA systems can assist healthcare professionals by accurately 
+                interpreting complex medical images and providing insightful answers to diagnostic questions, thereby 
+                enhancing both the speed and accuracy of medical assessments. In manufacturing, VQA systems can optimize 
+                quality control processes by enabling automated systems to identify defects and ensure product 
+                consistency with minimal human intervention. These advancements underscore the importance of developing 
+                robust VQA capabilities, as they push the boundaries of the Visual Turing Test and bring us closer to 
+                achieving true human-like AI cognition.
+                
+                Unlike other vision-language tasks, VQA requires many CV sub-tasks to be solved in the process, 
+                including: **Object recognition**, **Object detection**, **Attribute classification**, **Scene 
+                classification**, **Counting**, **Activity recognition**, **Spatial relationships among objects**, 
+                and **Commonsense reasoning**. These VQA tasks often do not require external factual knowledge and only 
+                in rare cases require common-sense reasoning. Furthermore, VQA models cannot derive additional knowledge 
+                from existing VQA datasets should a question require it, therefore **Knowledge-Based Visual Question 
+                Answering (KB-VQA)** has been introduced. KB-VQA is a relatively new extension to VQA with datasets 
+                representing a knowledge-based VQA task where the visual question cannot be answered without external 
+                knowledge, where the essence of this task is centred around knowledge acquisition and integration with 
+                the visual contents of the image.
+                </div>""",
+                 unsafe_allow_html=True)
 
-    st.write("""## Credits
-                -This project predominantly uses [LLaMA-2](https://ai.meta.com/llama/) and derivative models for language inference. Models are made available under the [Meta LlaMA license](https://ai.meta.com/llama/license/).
-                -This application is built on [streamlit](https://streamlit.io).
-            """)
+    st.write("""
+            <div style="text-align: justify;">
+            
+            This application showcases the advanced capabilities of the KB-VQA model, empowering users to seamlessly 
+            upload images, pose questions, and obtain  answers derived from both visual and textual data. 
+            By leveraging sophisticated Multimodal Learning techniques, this project bridges the gap between visual 
+            perception and linguistic interpretation, effectively merging these modalities to provide coherent and 
+            contextually relevant responses. This research not only showcases the cutting-edge progress in artificial 
+            intelligence but also pushes the boundaries of AI systems towards passing the **Visual Turing Test**, where 
+            machines exhibit **human-like** understanding and reasoning in processing and responding to visual 
+            information.
+            
+            ## Tools:
+            
+            - **Dataset Analysis**: Provides an overview of the KB-VQA datasets and displays various analysis of the 
+            OK-VQA dataset.
+            - **Model Architecture**: Displays the model architecture and accompanying abstract and design details for 
+            the Knowledge-Based Visual Question Answering (KB-VQA) model.
+            - **Results**: Manages the interactive Streamlit demo for visualizing model evaluation results and analysis. 
+            It provides an interface for users to explore different aspects of the model performance and evaluation 
+            samples.
+            - **Run Inference**: This tool allows users to run inference to test and use the fine-tuned KB-VQA model 
+            using various configurations.
+            </div>""",
+             unsafe_allow_html=True)
+    st.markdown("<br>" * 1, unsafe_allow_html=True)
+    st.write(" ##### Developed by: [Mohammed H AlHaj](https://www.linkedin.com/in/m7mdal7aj)")
+    st.markdown("<br>" * 1, unsafe_allow_html=True)
+    st.write("""
+            **Credit:** 
+            * The project predominantly uses [LLaMA-2](https://ai.meta.com/llama/) language inference. It is 
+            made available under [Meta LlaMA license](https://ai.meta.com/llama/license/).
+            * This application is built on [Streamlit](https://streamlit.io), providing an interactive and user-friendly 
+            interface.
+            """)