import streamlit as st from helper import ( load_dataset, search, get_file_paths, get_cordinates, get_images_from_s3_to_display, get_images_with_bounding_boxes_from_s3, load_dataset_with_limit ) import os import time # Load environment variables AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID") AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY") # Predefined list of datasets datasets = ["MajorTom-Germany", "MajorTom-Netherlands","MajorTom-North-America", "MajorTom-Europe","WayveScenes"] folder_path_dict = { "WayveScenes": "", "MajorTom-Germany": "MajorTOM-DE/", "MajorTom-Netherlands": "MajorTOM-NL/", "MajorTom-Europe": "MajorTom-Europe/", "MajorTom-North-America" : "", "MajorTom-UK" :"" } description = { "WayveScenes": "A large-scale dataset featuring diverse urban driving scenes, captured from vehicles to advance AI perception and navigation in complex environments.", "MajorTom-Germany": "A geospatial dataset containing satellite imagery from across Germany, designed for tasks like land-use classification, environmental monitoring, and earth observation analytics.", "MajorTom-Netherlands": "A geospatial dataset containing satellite imagery from across Netherlands, designed for tasks like land-use classification, environmental monitoring, and earth observation analytics.", "MajorTom-UK" :"A geospatial dataset containing satellite imagery from across the United Kingdom, designed for tasks like land-use classification, environmental monitoring, and earth observation analytics.", "MajorTom-North-America" :"A geospatial dataset containing satellite imagery from across North America, designed for tasks like land-use classification, environmental monitoring, and earth observation analytics.", "MajorTom-Europe" :"A geospatial dataset containing satellite imagery from across Europe, designed for tasks like land-use classification, environmental monitoring, and earth observation analytics." } selection = { 'WayveScenes': [1, 10], #Is there problem? "MajorTom-Germany": [1, 1], "MajorTom-Netherlands": [1,1], "MajorTom-UK": [1,1], "MajorTom-North-America": [1,4], "MajorTom-Europe": [1,19] } example_queries = { 'WayveScenes': "Parking Signs, Pedestrian Crossing, Traffic Light (Red, Green, Orange)", "MajorTom-Germany": "Airports, Golf Courses, Wind Mills, Solar Panels ", "MajorTom-Netherlands": "Airports, Golf Courses, Wind Mills, Solar Panels ", "MajorTom-UK": "Airports, Golf Courses, Wind Mills, Solar Panels ", "MajorTom-Europe": "Airports, Golf Courses, Wind Mills, Solar Panels ", "MajorTom-North-America": "Airports, Golf Courses, Wind Mills, Solar Panels " } # AWS S3 bucket name bucket_name = "datasets-quasara-io" # Streamlit App def main(): # Initialize session state variables if not already initialized if 'search_in_small_objects' not in st.session_state: st.session_state.search_in_small_objects = False if 'dataset_number' not in st.session_state: st.session_state.dataset_number = 1 if 'df' not in st.session_state: st.session_state.df = None st.title("Semantic Search and Image Display") # Select dataset from dropdown dataset_name = st.selectbox("Select Dataset", datasets) st.session_state.df = None #For Loading from Box folder_path = folder_path_dict[dataset_name] st.caption(description[dataset_name]) if st.checkbox("Enable Small Object Search", value=st.session_state.search_in_small_objects): st.session_state.search_in_small_objects = True st.text("Small Object Search Enabled") st.session_state.dataset_number = st.selectbox("Select Subset of Data", list(range(1, selection[dataset_name][1] + 1))) st.session_state.df = None st.text(f"You have selected Split Dataset {st.session_state.dataset_number}") else: st.session_state.search_in_small_objects = False st.text("Small Object Search Disabled") st.session_state.dataset_number = st.selectbox("Select Subset of Data", list(range(1, selection[dataset_name][0] + 1))) st.session_state.df = None st.text(f"You have selected Main Dataset {st.session_state.dataset_number}") df, total_rows = load_dataset_with_limit(dataset_name, st.session_state.dataset_number, st.session_state.search_in_small_objects, limit=1) dataset_limit = st.slider("Size of Dataset to be searched from", min_value=0, max_value=min(total_rows, 80000), value=int(min(total_rows, 80000)/2)) st.text(f'The smaller the dataset the faster the search will work.') # Load dataset with limit only if not already loaded try: loading_dataset_text = st.empty() loading_dataset_text.text("Loading Dataset...") loading_dataset_bar = st.progress(0) # Simulate dataset loading progress for i in range(0, 100, 25): time.sleep(0.2) # Simulate work being done loading_dataset_bar.progress(i + 25) # Load dataset df, total_rows = load_dataset_with_limit(dataset_name, st.session_state.dataset_number, st.session_state.search_in_small_objects, limit=dataset_limit) # Store loaded dataset in session state st.session_state.df = df loading_dataset_bar.progress(100) loading_dataset_text.text("Dataset loaded successfully!") st.success(f"Dataset loaded successfully with {len(df)} rows.") except Exception as e: st.error(f"Failed to load dataset: {e}") # Input search query query = st.text_input("Enter your search query") st.text(f"Example Queries for your Dataset: {example_queries[dataset_name]}") # Number of results to display limit = st.number_input("Number of results to display", min_value=1, max_value=10, value=10) # Search button if st.button("Search"): # Validate input if not query: st.warning("Please enter a search query.") else: try: # Progress bar for search search_loading_text = st.empty() search_loading_text.text("Searching...") search_progress_bar = st.progress(0) # Perform search on the loaded dataset from session state df = st.session_state.df if st.session_state.search_in_small_objects: results = search(query, df, limit) top_k_paths = get_file_paths(df, results) top_k_cordinates = get_cordinates(df, results) search_type = 'Splits' else: # Normal Search results = search(query, df, limit) top_k_paths = get_file_paths(df, results) search_type = 'Main' # Complete the search progress search_progress_bar.progress(100) search_loading_text.text(f"Search completed among {dataset_limit} rows for {dataset_name} in {search_type} {st.session_state.dataset_number}") # Load Images with Bounding Boxes if applicable if st.session_state.search_in_small_objects and top_k_paths and top_k_cordinates: get_images_with_bounding_boxes_from_s3(bucket_name, top_k_paths, top_k_cordinates, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, folder_path) elif not st.session_state.search_in_small_objects and top_k_paths: st.write(f"Displaying top {len(top_k_paths)} results for query '{query}':") get_images_from_s3_to_display(bucket_name, top_k_paths, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, folder_path) else: st.write("No results found.") except Exception as e: st.error(f"Search failed: {e}") if __name__ == "__main__": main()