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import pandas as pd |
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import streamlit as st |
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from my_model.tabs.run_inference import run_inference |
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class UIManager: |
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def __init__(self): |
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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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"Finetuning and Evaluation Results": self.display_finetuning_evaluation, |
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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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"More Pages will follow .. ": self.display_placeholder |
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} |
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def add_tab(self, tab_name, display_function): |
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self.tabs[tab_name] = display_function |
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def display_sidebar(self): |
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st.sidebar.title("Navigation") |
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selection = st.sidebar.radio("Go to", list(self.tabs.keys())) |
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st.sidebar.write("More Pages will follow .. ") |
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return selection |
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def display_selected_page(self, selection): |
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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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st.title("MultiModal Learning for Knowledge-Based Visual Question Answering") |
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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. |
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Further details will be updated later""") |
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def display_dataset_analysis(self): |
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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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def display_finetuning_evaluation(self): |
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st.title("Finetuning and Evaluation Results") |
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st.write("This is a Place Holder until the contents are uploaded.") |
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def display_run_inference(self): |
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run_inference() |
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def display_dissertation_report(self): |
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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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st.title("Code") |
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st.markdown("You can view the code for this project on the Hugging Face Space file page.") |
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st.markdown("[View Code](https://huggingface.co/spaces/m7mdal7aj/Mohammed_Alhaj_PlayGround/tree/main)", unsafe_allow_html=True) |
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def display_placeholder(self): |
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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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class StateManager: |
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def __init__(self): |
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self.initialize_state() |
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def initialize_state(self): |
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if 'images_data' not in st.session_state: |
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st.session_state['images_data'] = {} |
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if 'model_settings' not in st.session_state: |
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st.session_state['model_settings'] = {'detection_model': None, 'confidence_level': None} |
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if 'kbvqa' not in st.session_state: |
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st.session_state['kbvqa'] = None |
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if 'selected_method' not in st.session_state: |
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st.session_state['selected_method'] = None |
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def update_model_settings(self, detection_model=None, confidence_level=None, selected_method=None): |
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if detection_model is not None: |
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st.session_state['model_settings']['detection_model'] = detection_model |
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if confidence_level is not None: |
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st.session_state['model_settings']['confidence_level'] = confidence_level |
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if selected_method is not None: |
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st.session_state['selected_method'] = selected_method |
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def check_settings_changed(self, current_selected_method, current_detection_model, current_confidence_level): |
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return (st.session_state['model_settings']['detection_model'] != current_detection_model or |
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st.session_state['model_settings']['confidence_level'] != current_confidence_level or |
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st.session_state['selected_method'] != current_selected_method) |
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def display_model_settings(self): |
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st.write("### Current Model Settings:") |
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st.table(pd.DataFrame(st.session_state['model_settings'], index=[0])) |
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def display_session_state(self): |
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st.write("### Current Session State:") |
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data = [{'Key': key, 'Value': str(value)} for key, value in st.session_state.items()] |
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df = pd.DataFrame(data) |
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st.table(df) |
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def get_model(self): |
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"""Retrieve the KBVQA model from the session state.""" |
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return st.session_state.get('kbvqa', None) |
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def is_model_loaded(self): |
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return 'kbvqa' in st.session_state and st.session_state['kbvqa'] is not None |
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def reload_detection_model(self, detection_model, confidence_level): |
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try: |
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free_gpu_resources() |
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if self.is_model_loaded(): |
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prepare_kbvqa_model(detection_model, only_reload_detection_model=True) |
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st.session_state['kbvqa'].detection_confidence = confidence_level |
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self.update_model_settings(detection_model, confidence_level) |
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free_gpu_resources() |
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except Exception as e: |
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st.error(f"Error reloading detection model: {e}") |
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def process_new_image(self, image_key, image, kbvqa): |
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if image_key not in st.session_state['images_data']: |
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st.session_state['images_data'][image_key] = { |
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'image': image, |
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'caption': '', |
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'detected_objects_str': '', |
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'qa_history': [], |
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'analysis_done': False |
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} |
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def analyze_image(self, image, kbvqa): |
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img = copy.deepcopy(image) |
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caption = kbvqa.get_caption(img) |
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image_with_boxes, detected_objects_str = kbvqa.detect_objects(img) |
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return caption, detected_objects_str, image_with_boxes |
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def add_to_qa_history(self, image_key, question, answer): |
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if image_key in st.session_state['images_data']: |
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st.session_state['images_data'][image_key]['qa_history'].append((question, answer)) |
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def get_images_data(self): |
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return st.session_state['images_data'] |
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def update_image_data(self, image_key, caption, detected_objects_str, analysis_done): |
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if image_key in st.session_state['images_data']: |
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st.session_state['images_data'][image_key].update({ |
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'caption': caption, |
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'detected_objects_str': detected_objects_str, |
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'analysis_done': analysis_done |
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}) |
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