added console info

app.py

@@ -1,11 +1,16 @@
 import streamlit as st
+import logging
+import os
+import time
+import psutil
 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
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
 
 # Load environment variables
 AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
@@ -15,8 +20,8 @@ AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
 datasets = ["WayveScenes", "MajorTom-Europe"]
 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."
+    "WayveScenes": "A large-scale dataset featuring diverse urban driving scenes.",
+    "MajorTom-Europe": "A geospatial dataset containing satellite imagery from across Europe."
 }
 selection = {
     'WayveScenes': [1, 8],
@@ -26,19 +31,24 @@ selection = {
 # AWS S3 bucket name
 bucket_name = "datasets-quasara-io"
 
+# Function to log CPU and memory usage
+def log_resource_usage(stage):
+    cpu_usage = psutil.cpu_percent(interval=1)
+    memory_info = psutil.virtual_memory()
+    logging.info(f"{stage} - CPU Usage: {cpu_usage}%, Memory Usage: {memory_info.percent}%")
+
 # 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")
+    log_resource_usage("Initialization")
 
     # Select dataset from dropdown
     dataset_name = st.selectbox("Select Dataset", datasets)
@@ -54,12 +64,10 @@ def main():
         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.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.text(f"You have selected Main Dataset {st.session_state.dataset_number}")
 
     dataset_limit = st.slider("Size of Dataset to be searched from", min_value=1000, max_value=20000, value=10000)
     st.text(f'The smaller the dataset, the faster the search will work.')
@@ -67,28 +75,26 @@ def main():
     # Load dataset with limit only if not already loaded
     if st.button("Load Dataset"):
         try:
-            # Clear old dataset from memory
-            if st.session_state.df is not None:
-                st.session_state.df = None  # Clear the old dataset
-                st.info("Previous dataset cleared from memory.")
-
             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
+                time.sleep(0.2)
                 loading_dataset_bar.progress(i + 25)
 
-            # Load new dataset
+            log_resource_usage("Before Loading 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.")
+            log_resource_usage("After Loading Dataset")
 
         except Exception as e:
+            logging.error(f"Failed to load dataset: {e}")
             st.error(f"Failed to load dataset: {e}")
 
     # Input search query
@@ -104,25 +110,23 @@ def main():
             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
+                log_resource_usage("Before Search")
                 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)
                 else:
-                    # Normal Search
                     results = search(query, df, limit)
                     top_k_paths = get_file_paths(df, results)
 
-                # Complete the search progress
                 search_progress_bar.progress(100)
                 search_loading_text.text("Search completed!")
+                log_resource_usage("After Search")
 
                 # Load Images with Bounding Boxes if applicable
                 if st.session_state.search_in_small_objects and top_k_paths and top_k_cordinates:
@@ -134,6 +138,7 @@ def main():
                     st.write("No results found.")
 
             except Exception as e:
+                logging.error(f"Search failed: {e}")
                 st.error(f"Search failed: {e}")
 
 if __name__ == "__main__":