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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.dataset_analysis import run_dataset_analyzer |
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class UIManager(): |
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"""Manages the user interface for the Streamlit application.""" |
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def __init__(self): |
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"""Initializes the UIManager with predefined tabs.""" |
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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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"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, display_function): |
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"""Adds a new tab to the UI.""" |
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self.tabs[tab_name] = display_function |
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def display_sidebar(self): |
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"""Displays the sidebar for navigation.""" |
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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("**[Mohammed H AlHaj](https://www.linkedin.com/in/m7mdal7aj)**") |
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return selection |
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def display_selected_page(self, selection): |
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"""Displays the selected page based on user's choice.""" |
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if selection in self.tabs: |
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self.tabs[selection]() |
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def display_home(self): |
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"""Displays the Home page of the application.""" |
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run_home() |
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def display_dataset_analysis(self): |
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"""Displays the Dataset Analysis page.""" |
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st.title("OK-VQA Dataset Analysis") |
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st.write("This is a Place Holder until the contents are uploaded.") |
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run_dataset_analyzer() |
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def display_results(self): |
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"""Displays Evaluation Results page.""" |
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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_run_inference(self): |
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"""Displays the Run Inference page.""" |
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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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inference_runner = InferenceRunner() |
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inference_runner.run_inference() |
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def display_dissertation_report(self): |
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"""Displays the Dissertation Report page.""" |
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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="example.pdf", |
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mime="application/octet-stream" |
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) |
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def display_code(self): |
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"""Displays the Code page with a link to the project's code repository.""" |
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st.title("Code") |
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st.markdown("You can view the code for this project on HuggingFace Space files page.") |
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st.markdown("[View Code](https://huggingface.co/spaces/m7mdal7aj/Mohammed_Alhaj_KB-VQA/tree/main)", unsafe_allow_html=True) |
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def display_placeholder(self): |
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"""Displays a placeholder for future content.""" |
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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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