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| import pandas as pd | |
| import streamlit as st | |
| from my_model.tabs.run_inference import run_inference | |
| class UIManager: | |
| def __init__(self): | |
| self.tabs = { | |
| "Home": self.display_home, | |
| "Dataset Analysis": self.display_dataset_analysis, | |
| "Finetuning and Evaluation Results": self.display_finetuning_evaluation, | |
| "Run Inference": self.display_run_inference, | |
| "Dissertation Report": self.display_dissertation_report, | |
| "Code": self.display_code, | |
| "More Pages will follow .. ": self.display_placeholder | |
| } | |
| def add_tab(self, tab_name, display_function): | |
| self.tabs[tab_name] = display_function | |
| def display_sidebar(self): | |
| st.sidebar.title("Navigation") | |
| selection = st.sidebar.radio("Go to", list(self.tabs.keys())) | |
| st.sidebar.write("More Pages will follow .. ") | |
| return selection | |
| def display_selected_page(self, selection): | |
| if selection in self.tabs: | |
| self.tabs[selection]() | |
| def display_home(self): | |
| st.title("MultiModal Learning for Knowledge-Based Visual Question Answering") | |
| st.write("""This application is an interactive element of the project and prepared by Mohammed Alhaj as part of the dissertation for Masters degree in Artificial Intelligence at the University of Bath. | |
| Further details will be updated later""") | |
| def display_dataset_analysis(self): | |
| st.title("OK-VQA Dataset Analysis") | |
| st.write("This is a Place Holder until the contents are uploaded.") | |
| def display_finetuning_evaluation(self): | |
| st.title("Finetuning and Evaluation Results") | |
| st.write("This is a Place Holder until the contents are uploaded.") | |
| def display_run_inference(self): | |
| run_inference() | |
| def display_dissertation_report(self): | |
| st.title("Dissertation Report") | |
| st.write("Click the link below to view the PDF.") | |
| st.download_button( | |
| label="Download PDF", | |
| data=open("Files/Dissertation Report.pdf", "rb"), | |
| file_name="example.pdf", | |
| mime="application/octet-stream" | |
| ) | |
| def display_code(self): | |
| st.title("Code") | |
| st.markdown("You can view the code for this project on the Hugging Face Space file page.") | |
| st.markdown("[View Code](https://huggingface.co/spaces/m7mdal7aj/Mohammed_Alhaj_PlayGround/tree/main)", unsafe_allow_html=True) | |
| def display_placeholder(self): | |
| st.title("Stay Tuned") | |
| st.write("This is a Place Holder until the contents are uploaded.") | |
| class StateManager: | |
| def __init__(self): | |
| self.initialize_state() | |
| def initialize_state(self): | |
| if 'images_data' not in st.session_state: | |
| st.session_state['images_data'] = {} | |
| if 'model_settings' not in st.session_state: | |
| st.session_state['model_settings'] = {'detection_model': None, 'confidence_level': None} | |
| if 'kbvqa' not in st.session_state: | |
| st.session_state['kbvqa'] = None | |
| if 'selected_method' not in st.session_state: | |
| st.session_state['selected_method'] = None | |
| def update_model_settings(self, detection_model=None, confidence_level=None, selected_method=None): | |
| if detection_model is not None: | |
| st.session_state['model_settings']['detection_model'] = detection_model | |
| if confidence_level is not None: | |
| st.session_state['model_settings']['confidence_level'] = confidence_level | |
| if selected_method is not None: | |
| st.session_state['selected_method'] = selected_method | |
| def check_settings_changed(self, current_selected_method, current_detection_model, current_confidence_level): | |
| return (st.session_state['model_settings']['detection_model'] != current_detection_model or | |
| st.session_state['model_settings']['confidence_level'] != current_confidence_level | |
| st.session_state['model_settings']['selected_method'] != current_selected_method) | |
| def display_model_settings(self): | |
| st.write("### Current Model Settings:") | |
| st.table(pd.DataFrame(st.session_state['model_settings'], index=[0])) | |
| def display_session_state(self): | |
| st.write("### Current Session State:") | |
| data = [{'Key': key, 'Value': str(value)} for key, value in st.session_state.items()] | |
| df = pd.DataFrame(data) | |
| st.table(df) | |
| def is_model_loaded(self): | |
| """Check if the model is loaded in the session state.""" | |
| return 'kbvqa' in st.session_state and st.session_state['kbvqa'] is not None | |
| def reload_detection_model(self, detection_model, confidence_level): | |
| """Reload only the detection model with new settings.""" | |
| try: | |
| free_gpu_resources() | |
| if self.is_model_loaded(): | |
| prepare_kbvqa_model(detection_model, only_reload_detection_model=True) | |
| st.session_state['kbvqa'].detection_confidence = confidence_level | |
| self.update_model_settings(detection_model, confidence_level) | |
| free_gpu_resources() | |
| except Exception as e: | |
| st.error(f"Error reloading detection model: {e}") | |