making search faster

app.py

@@ -10,6 +10,7 @@ from data_loader import (
     build_leaderboard_table,
     clear_cache,
     search_model_across_leaderboards,
+    get_model_suggestions_fast,
     DATA_DIR
 )
 from ui_components import (
@@ -123,12 +124,17 @@ def compare_models(selected_models):
 
 
 def get_model_suggestions(value, key_up_data: gr.KeyUpData):
-    query = key_up_data.input_value if key_up_data else ""
+    query = ""
+    if key_up_data and key_up_data.input_value:
+        query = key_up_data.input_value
+    elif value:
+        query = value
+    
     if not query or len(query) < 2:
         return gr.update(choices=[])
     
-    _, matches = search_model_across_leaderboards(query)
-    return gr.update(choices=matches[:15])
+    matches = get_model_suggestions_fast(query, limit=15)
+    return gr.update(choices=matches)
 
 
 def export_leaderboard_to_csv(full_df, selected_leaderboard, search_query, selected_columns):
@@ -449,7 +455,7 @@ Submit via GitHub Pull Request:
         fn=get_model_suggestions,
         inputs=[model_dropdown],
         outputs=[model_dropdown],
-        show_progress="hidden"
+        show_progress="hidden",
     )
     
     model_dropdown.select(

data_loader.py

@@ -4,12 +4,17 @@ Data Loader: Load from HuggingFace, parse JSON files, and build tables.
 import json
 import pandas as pd
 from pathlib import Path
+from bisect import bisect_left
 from datasets import load_dataset
+from rapidfuzz import process, fuzz
 
 
 # Global caches
 HF_DATASET_CACHE = {}
 LEADERBOARD_CACHE = {}
+# Compact search index: sorted list of (lowercase_name, original_name) tuples
+MODEL_SEARCH_INDEX = []
+MODEL_NAMES = []
 DATA_DIR = Path("leaderboard_data")
 
 
@@ -83,6 +88,7 @@ def load_hf_dataset_on_startup():
             HF_DATASET_CACHE[split_name] = parsed_items
             
         print(f"Loaded {len(HF_DATASET_CACHE)} leaderboard(s) from HuggingFace")
+        _build_search_index()
         return True
     except Exception as e:
         print(f"Warning: Could not load HuggingFace dataset: {e}")
@@ -321,55 +327,98 @@ def clear_cache():
     LEADERBOARD_CACHE.clear()
 
 
+def _build_search_index():
+    """Build compact sorted search index for fast prefix/substring matching."""
+    global MODEL_SEARCH_INDEX, MODEL_NAMES
+    models = set()
+    for parsed_items in HF_DATASET_CACHE.values():
+        for item in parsed_items:
+            models.add(item.get("model", ""))
+    MODEL_NAMES = list(models)
+    # Store as sorted tuples (lowercase, original) for binary search on prefix
+    MODEL_SEARCH_INDEX = sorted((m.lower(), m) for m in models)
+    print(f"Built search index with {len(MODEL_SEARCH_INDEX)} unique models")
+
+
+def get_model_suggestions_fast(query, limit=15):
+    """Fast search: prefix first, then fuzzy (rapidfuzz) as fallback."""
+    if not query or len(query) < 2 or not MODEL_SEARCH_INDEX:
+        return []
+    
+    query_lower = query.lower()
+    results = []
+    
+    # 1. Fast prefix match using binary search (O(log n) to find start)
+    idx = bisect_left(MODEL_SEARCH_INDEX, (query_lower,))
+    while idx < len(MODEL_SEARCH_INDEX) and len(results) < limit:
+        name_lower, name_orig = MODEL_SEARCH_INDEX[idx]
+        if name_lower.startswith(query_lower):
+            results.append((0, len(name_lower), name_orig))  # pos=0 for prefix
+            idx += 1
+        else:
+            break
+    
+    # 2. If not enough, do substring search (still O(n) but only if needed)
+    if len(results) < limit:
+        seen = {r[2] for r in results}
+        for name_lower, name_orig in MODEL_SEARCH_INDEX:
+            if name_orig in seen:
+                continue
+            if query_lower in name_lower:
+                pos = name_lower.find(query_lower)
+                results.append((pos, len(name_lower), name_orig))
+                if len(results) >= limit * 2:  # Get extra for sorting
+                    break
+
+    # 3. Fuzzy fallback if still not enough
+    if len(results) < limit:
+        remaining = limit - len(results)
+        seen = {r[2] for r in results}
+        fuzzy_candidates = process.extract(
+            query,
+            MODEL_NAMES,
+            scorer=fuzz.ratio,  # fast scorer
+            limit=remaining + 5,
+            score_cutoff=60
+        )
+        for name_orig, _, _ in fuzzy_candidates:
+            if name_orig in seen:
+                continue
+            results.append((1, len(name_orig), name_orig))  # pos=1 to rank after prefixes
+            seen.add(name_orig)
+            if len(results) >= limit:
+                break
+    
+    # Sort by: match position, then length (shorter = more relevant)
+    results.sort(key=lambda x: (x[0], x[1]))
+    return [r[2] for r in results[:limit]]
+
+
 def search_model_across_leaderboards(model_query):
     """Search for a model across all leaderboards and return aggregated results."""
     if not model_query or not HF_DATASET_CACHE:
         return {}, []
     
-    model_query_lower = model_query.lower().strip()
-    results = {}
-    all_matches = []
+    # Use fast fuzzy search for suggestions
+    matches = get_model_suggestions_fast(model_query, limit=20)
     
+    # Get detailed results only for matched models
+    results = {}
     for leaderboard_name, parsed_items in HF_DATASET_CACHE.items():
         for item in parsed_items:
             model_id = item.get("model", "")
-            # Check if query matches model name (case insensitive, partial match)
-            if model_query_lower in model_id.lower():
-                all_matches.append(model_id)
-                
-                # Exact match gets priority
-                if model_id.lower() == model_query_lower or model_id == model_query:
-                    if model_id not in results:
-                        results[model_id] = {}
-                    results[model_id][leaderboard_name] = {
-                        "developer": item.get("developer"),
-                        "params": item.get("params"),
-                        "architecture": item.get("architecture"),
-                        "precision": item.get("precision"),
-                        "results": item.get("results", {})
-                    }
-    
-    # If no exact match, use partial matches
-    if not results and all_matches:
-        # Get the first partial match
-        for leaderboard_name, parsed_items in HF_DATASET_CACHE.items():
-            for item in parsed_items:
-                model_id = item.get("model", "")
-                if model_query_lower in model_id.lower():
-                    if model_id not in results:
-                        results[model_id] = {}
-                    results[model_id][leaderboard_name] = {
-                        "developer": item.get("developer"),
-                        "params": item.get("params"),
-                        "architecture": item.get("architecture"),
-                        "precision": item.get("precision"),
-                        "results": item.get("results", {})
-                    }
-    
-    # Return unique matches for autocomplete
-    unique_matches = sorted(set(all_matches))[:20]  # Limit to 20 suggestions
+            if model_id in matches:
+                if model_id not in results:
+                    results[model_id] = {}
+                results[model_id][leaderboard_name] = {
+                    "developer": item.get("developer"),
+                    "params": item.get("params"),
+                    "architecture": item.get("architecture"),
+                    "precision": item.get("precision"),
+                    "results": item.get("results", {})
+                }
     
-    return results, unique_matches
+    return results, matches
 
 
 def get_all_model_names():

pyproject.toml

@@ -13,4 +13,5 @@ dependencies = [
     "gradio>=6.1.0",
     "pandas>=2.3.2",
     "plotly>=6.5.0",
+    "rapidfuzz>=3.0.0",
 ]

requirements.txt

@@ -158,6 +158,8 @@ pyyaml==6.0.3
     #   datasets
     #   gradio
     #   huggingface-hub
+rapidfuzz==3.14.3
+    # via e3-space (pyproject.toml)
 requests==2.32.5
     # via datasets
 rich==14.2.0

uv.lock

@@ -336,6 +336,7 @@ dependencies = [
     { name = "gradio" },
     { name = "pandas" },
     { name = "plotly" },
+    { name = "rapidfuzz" },
 ]
 
 [package.metadata]
@@ -344,6 +345,7 @@ requires-dist = [
     { name = "gradio", specifier = ">=6.1.0" },
     { name = "pandas", specifier = ">=2.3.2" },
     { name = "plotly", specifier = ">=6.5.0" },
+    { name = "rapidfuzz", specifier = ">=3.0.0" },
 ]
 
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 ]
 
+[[package]]
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+version = "3.14.3"
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