app.py

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-NA_66b587ece7b433ff03455227_66b589a3c70d86c8306cdf86_85f3b0d0/",
    "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 Europe, 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()