Added Example Queries

app.py

@@ -13,7 +13,7 @@ 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-Germany", "MajorTom-Netherlands"]
+datasets = ["MajorTom-Germany", "MajorTom-Netherlands", "WayveScenes"]
 folder_path_dict = {
     "WayveScenes": "WayveScenes/",
     "MajorTom-Germany": "MajorTOM-DE/",
@@ -33,7 +33,12 @@ selection = {
     "MajorTom-UK": [1,1]
 }
 
-
+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 "
+}
 
 # AWS S3 bucket name
 bucket_name = "datasets-quasara-io"
@@ -78,17 +83,15 @@ def main():
     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.')
-    st.text('Please click Load Dataset to finalise selection for search')
+    st.text('Please click Load Dataset to finalize selection for search')
     
-
     # Load dataset with limit only if not already loaded
-    if st.button("Load Dataset"):
+    if st.button("Load Dataset", key="load_dataset"):
         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
@@ -103,61 +106,63 @@ def main():
             loading_dataset_text.text("Dataset loaded successfully!")
             st.success(f"Dataset loaded successfully with {len(df)} rows.")
             
+            # After dataset is loaded, show search options
+            query = st.text_input("Enter your search query", key="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, key="result_limit")
             
+            # Search button
+            if st.button("Search", key="search_button"):
+                # 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}")
+
         except Exception as e:
             st.error(f"Failed to load dataset: {e}")
-    
-    
-    # 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.session_state.df == None and st.button("Search"):
-        st.warning("Please Load Datset")
-    else:
-        # 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()
+
+            
+
+
 
 if __name__ == "__main__":
     main()