app.py

import streamlit as st
from helper import load_dataset, parallel_load_and_combine,search, get_file_paths, get_cordinates, get_images_from_s3_to_display, get_images_with_bounding_boxes_from_s3,  batch_search
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 = ["WayveScenes","MajorTom-Europe"]  # Example dataset names
description = {
        "StopSign_test" : "A test dataset for me",
        "WayveScenes": "A large-scale dataset featuring diverse urban driving scenes, captured from autonomous vehicles to advance AI perception and navigation in complex environments.",
        "MajorTom-Europe": "A geospatial dataset containing satellite imagery from across Europe, designed for tasks like land-use classification, environmental monitoring, and earth observation analytics."}
# AWS S3 bucket name
bucket_name = "datasets-quasara-io"

# Streamlit App
def main():
    st.title("Semantic Search and Image Display")

    # Select dataset from dropdown
    dataset_name = st.selectbox("Select Dataset", datasets)
    
    if dataset_name == 'StopSign_test':
        folder_path = ""
    else:
        folder_path = f'{dataset_name}/'
    st.caption(description[dataset_name]) #trial area
    # Progress bar for loading dataset
    loading_text = st.empty()  # Placeholder for dynamic text
    loading_text.text("Loading dataset...")
    progress_bar = st.progress(0)
    
    # Simulate dataset loading progress
    for i in range(0, 100, 25):
        time.sleep(0.2)  # Simulate work being done
        progress_bar.progress(i + 25)
    
    # Load the selected dataset
    dataset = load_dataset(f"quasara-io/{dataset_name}")

    # Complete progress when loading is done
    progress_bar.progress(100)
    loading_text.text("Dataset loaded successfully!")

    # Input search query
    query = st.text_input("Enter your search query")

    # Number of results to display
    limit = st.number_input("Number of results to display", min_value=1, max_value=10, value=10)
    if st.checkbox("Enable Small Object Search"):
        search_in_small_objects = True
        st.text("Small Object Search Enabled")
    else:
        search_in_small_objects = False
        st.text("Small Object Search Disabled")
    
    # Search button
    if st.button("Search"):
        # Validate input
        if not query:
            st.warning("Please enter a search query.")
        else:
            # Progress bar for search
            search_loading_text = st.empty()
            search_loading_text.text("Performing search...")
            search_progress_bar = st.progress(0)
            
            # Simulate search progress (e.g., in 4 steps)
            for i in range(0, 100, 25):
                time.sleep(0.3)  # Simulate work being done
                search_progress_bar.progress(i + 25)

            #Get Dataset Keys to speed up processing/search
            dataset_keys = dataset.keys()
            main_df,split_df = parallel_load_and_combine(dataset_keys,dataset)

            #Small Search
            if search_in_small_objects:
                # Perform the search
                results = batch_search(query, split_df)
                top_k_paths = get_file_paths(split_df,results)
                top_k_cordinates = get_cordinates(split_df, results)
                # Complete the search progress
                search_progress_bar.progress(100)
                search_loading_text.text("Search completed!")
                #Load Images with Bounding boxes
                if 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)
                else:
                    st.write("No results found.")
            else:
            #Normal Search
                results = batch_search(query, main_df)
                top_k_paths = get_file_paths(main_df, results)
                # Complete the search progress
                search_progress_bar.progress(100)
                search_loading_text.text("Search completed!")
                #Load Images
                # Display images from S3
                if 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.")

    
if __name__ == "__main__":
    main()