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import streamlit as st |
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import streamlit.components.v1 as components |
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import pandas as pd |
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from my_model.tabs.run_inference import InferenceRunner |
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from my_model.tabs.results import run_demo |
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from my_model.tabs.home import run_home |
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from my_model.state_manager import StateManager |
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from my_model.tabs.dataset_analysis import run_dataset_analyzer |
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from my_model.tabs.model_arch import run_model_arch |
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class UIManager(): |
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""" |
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Manages the user interface for the Streamlit application. |
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This class handles the creation and navigation of various tabs in the Streamlit app. It provides methods to add new |
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tabs, display the sidebar, and render the content of the selected tab. |
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Attributes: |
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tabs (dict): A dictionary mapping tab names to their corresponding display functions. |
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""" |
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def __init__(self)-> None: |
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""" |
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Initializes the UIManager with predefined tabs. |
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This method sets up the initial tabs for the application and initializes the state manager. |
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""" |
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self.tabs = { |
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"Home": self.display_home, |
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"Dataset Analysis": self.display_dataset_analysis, |
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"Model Architecture": self.display_model_arch, |
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"Results": self.display_results, |
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"Run Inference": self.display_run_inference, |
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"Dissertation Report": self.display_dissertation_report, |
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"Code": self.display_code |
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} |
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state_manager = StateManager() |
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state_manager.initialize_state() |
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def add_tab(self, tab_name: str, display_function: callable) -> None: |
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""" |
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Adds a new tab to the UI. |
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Args: |
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tab_name (str): The name of the new tab. |
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display_function (callable): The function to be called when the tab is selected. |
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Returns: |
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None |
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""" |
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self.tabs[tab_name] = display_function |
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def display_sidebar(self) -> str: |
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""" |
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Displays the sidebar for navigation. |
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This method creates a sidebar with navigation options and returns the user's selection. |
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Returns: |
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str: The name of the selected tab. |
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""" |
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st.sidebar.image("Files/logo.jpg") |
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st.sidebar.title("Navigation") |
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selection = st.sidebar.radio("Go to", list(self.tabs.keys()), disabled=st.session_state['loading_in_progress']) |
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st.sidebar.image("Files/mm.jpeg", use_column_width=True) |
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st.sidebar.markdown( |
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""" |
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<div style="text-align: center;"> |
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<a href="https://www.linkedin.com/in/m7mdal7aj" style="font-weight: bold; text-decoration: none;">Mohammed Bin Ali AlHaj</a> |
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</div> |
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""", |
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unsafe_allow_html=True |
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) |
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return selection |
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def display_selected_page(self, selection: str) -> None: |
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""" |
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Displays the selected page based on the user's choice. |
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Args: |
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selection (str): The name of the selected tab. |
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Returns: |
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None |
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""" |
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if selection in self.tabs: |
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self.tabs[selection]() |
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def display_home(self) -> None: |
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""" |
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Displays the Home page of the application. |
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Returns: |
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None |
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""" |
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st.markdown("<h1 style='text-align: center;'>Multimodal Learning for Visual Question Answering using World Knowledge</h1>", unsafe_allow_html=True) |
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st.markdown("<h2 style='text-align: center;'>Knowledge-Based Visual Question Answering - KBVQA</h2>", unsafe_allow_html=True) |
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run_home() |
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def display_dataset_analysis(self) -> None: |
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""" |
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Displays the Dataset Analysis page. |
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This page provides an overview of various KB-VQA datasets and analyses of the OK-VQA dataset. |
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Returns: |
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None |
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""" |
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st.title("Dataset Analysis") |
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st.write("""This page shows an overview of some of the KB-VQA datasets, and various analysis of |
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the [OK-VQA Dataset](https://okvqa.allenai.org/) that this KB-VQA model was fine-tuned |
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and evaluated on.""") |
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run_dataset_analyzer() |
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def display_results(self) -> None: |
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""" |
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Displays the Evaluation Results page. |
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This page demonstrates the model evaluation results and analyses in an interactive way. |
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Returns: |
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None |
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""" |
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st.title("Evaluation Results & Analyses") |
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st.write("This page demonstrates the model evaluation results and analyses in an interactive way.") |
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st.write("\n") |
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run_demo() |
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def display_model_arch(self) -> None: |
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""" |
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Displays the Model Architecture page. |
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This page provides detailed information about the model architecture. |
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Returns: |
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None |
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""" |
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st.title("Model Architecture") |
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st.write("This page shows the detailed Model Architecture.") |
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st.write("\n") |
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run_model_arch() |
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def display_run_inference(self) -> None: |
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""" |
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Displays the Run Inference page. |
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This page allows users to run inference using the fine-tuned model on the OK-VQA dataset. |
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Returns: |
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None |
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""" |
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st.title("Run Inference") |
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st.write("""Please note that this is not a general purpose model, it is specifically trained on |
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[OK-VQA Dataset](https://okvqa.allenai.org/) and desgined to give short and direct answers to the |
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given questions about the given image.\n""") |
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st.write("""**Note:** To load and run this model, the space must be configured to run on a GPU. |
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If the space is not set to run on a GPU, please contact me.""") |
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inference_runner = InferenceRunner() |
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inference_runner.run_inference() |
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def display_dissertation_report(self) -> None: |
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""" |
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Displays the Dissertation Report page. |
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This page provides a link to download the dissertation report PDF. |
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Returns: |
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None |
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""" |
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st.title("Dissertation Report") |
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st.write("Click the link below to view the PDF.") |
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st.download_button( |
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label="Download PDF", |
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data=open("Files/Dissertation Report.pdf", "rb"), |
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file_name="KB-VQA Dissertation Report for Mohammed Bin Ali Alhaj.pdf", |
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mime="application/octet-stream" |
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) |
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def display_code(self) -> None: |
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""" |
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Displays the Code page with a link to the project's code repository. |
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Returns: |
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None |
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""" |
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st.title("Code") |
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st.markdown("You can view the code for this project on HuggingFace repository.") |
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st.markdown("[View Code](https://huggingface.co/spaces/m7mdal7aj/KB-VQA/tree/main)", unsafe_allow_html=True) |
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def display_placeholder(self) -> None: |
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""" |
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Displays a placeholder for future content. |
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Returns: |
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None |
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""" |
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st.title("Stay Tuned") |
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st.write("This is a Place Holder until the contents are uploaded.") |
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