Update my_model/utilities/ui_manager.py

my_model/utilities/ui_manager.py

@@ -56,8 +56,9 @@ class UIManager():
             st.text('')
             st.text('')
             st.write("""\n\n\n\nThis is an interactive application developed to demonstrate my project 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) | Dissertation Supervisor: [Andreas Theophilou](https://researchportal.bath.ac.uk/en/persons/andreas-theophilou)
+                        \n\n\nDeveloped by: [Mohammed H AlHaj](https://www.linkedin.com/in/m7mdal7aj)
                         \n\n""")
+            st.header("Abstract")
             st.write("""Navigating the frontier of the Visual Turing Test, this research delves into multimodal learning to bridge the gap between visual perception and linguistic interpretation, a foundational challenge in artificial intelligence. It scrutinizes the integration of visual cognition and external knowledge, emphasizing the pivotal role of the Transformer model in enhancing language processing and supporting complex multimodal tasks.
                         This research explores the task of Knowledge-Based Visual Question Answering (KB-VQA), it examines the influence of Pre-Trained Large Language Models (PT-LLMs) and Pre-Trained Multimodal Models (PT-LMMs), which have transformed the machine learning landscape by utilizing expansive, pre-trained knowledge repositories to tackle complex tasks, thereby enhancing KB-VQA systems.
                         An examination of existing Knowledge-Based Visual Question Answering (KB-VQA) methodologies led to a refined approach that converts visual content into the linguistic domain, creating detailed captions and object enumerations. This process leverages the implicit knowledge and inferential capabilities of PT-LLMs. The research refines the fine-tuning of PT-LLMs by integrating specialized tokens, enhancing the models’ ability to interpret visual contexts. The research also reviews current image representation techniques and knowledge sources, advocating for the utilization of implicit knowledge in PT-LLMs, especially for tasks that do not require specialized expertise.
@@ -66,8 +67,8 @@ class UIManager():
         with col2:
             st.image("Files/mm.jpeg")
             st.write("""I am profoundly grateful for the support and guidance I have received throughout the course of my dissertation. I would like to extend my deepest appreciation to the following individuals:
-                        To my supervisor, Dr. Andreas Theophilou, whose expertise, and insightful guidance have been instrumental in the completion of this research. Your mentorship has not only profoundly shaped my work but also my future endeavours in the field of computer science.
-                        Special mention must be made of my mentors at the University of Bath—Dr. Ben Ralph, Dr. Hongping Cai, and Dr. Nadejda Roubtsova. The wealth of knowledge and insights I have gained from you has been indispensable. Your unwavering dedication to academic excellence and steadfast support have been crucial in navigating my academic journey.
+                        To my supervisor, [Dr. Andreas Theophilou](https://researchportal.bath.ac.uk/en/persons/andreas-theophilou), whose expertise, and insightful guidance have been instrumental in the completion of this research. Your mentorship has not only profoundly shaped my work but also my future endeavours in the field of computer science.
+                        Special mention must be made of my mentors at the University of Bath— [Dr. Ben Ralph](https://www.linkedin.com/in/benjamin-ralph-99a72b88/), [Dr. Hongping Cai](https://www.linkedin.com/in/hongping-cai-6836542a/), and Dr. Nadejda Roubtsova. The wealth of knowledge and insights I have gained from you has been indispensable. Your unwavering dedication to academic excellence and steadfast support have been crucial in navigating my academic journey.
                         My colleagues deserve equal gratitude, for their camaraderie and collaborative spirit have not only made this journey feasible but also deeply enjoyable. The shared experiences and the challenges we have overcome together have been integral to my personal and professional growth.
                         Lastly, my heartfelt thanks are extended to my family, whose unyielding love and encouragement have been my steadfast anchor. Your belief in my abilities has consistently inspired me and bolstered my strength throughout this process.
                         This dissertation is not merely a reflection of my individual efforts but stands as a testament to the collective support and wisdom of each individual mentioned above. I am honoured and privileged to be part of such a supportive and enriching academic community.