fixes to search + some css

app.py

@@ -123,18 +123,13 @@ def compare_models(selected_models):
         """, None
 
 
-def get_model_suggestions(value, key_up_data: gr.KeyUpData):
-    query = ""
-    if key_up_data and key_up_data.input_value:
-        query = key_up_data.input_value
-    elif value:
-        query = value
-    
+def get_model_suggestions(value):
+    query = value or ""
     if not query or len(query) < 2:
-        return gr.update(choices=[])
+        return gr.update(choices=[], value=[])
     
-    matches = get_model_suggestions_fast(query, limit=15)
-    return gr.update(choices=matches)
+    matches = get_model_suggestions_fast(query, limit=10)
+    return gr.update(choices=matches, value=[])
 
 
 def export_leaderboard_to_csv(full_df, selected_leaderboard, search_query, selected_columns):
@@ -300,13 +295,21 @@ with gr.Blocks(title="Every Eval Ever", theme=get_theme(), css=get_custom_css())
             """
             
             with gr.Row(elem_classes="controls-bar"):
-                with gr.Column(scale=4):
-                    model_dropdown = gr.Dropdown(
+                with gr.Column(scale=3):
+                    model_search_box = gr.Textbox(
+                        label="Search models",
+                        placeholder="Type model name...",
+                        interactive=True,
+                    )
+                with gr.Column(scale=1, min_width=120):
+                    search_button = gr.Button("Search", variant="secondary")
+                with gr.Column(scale=3):
+                    search_results = gr.CheckboxGroup(
                         choices=[],
-                        label="Search and select models",
+                        label="Top matches (select to add)",
                         interactive=True,
-                        allow_custom_value=True,
-                        filterable=True,
+                        value=[],
+                        elem_classes=["match-pills"],
                     )
                 with gr.Column(scale=1, min_width=80):
                     clear_models_btn = gr.Button("Clear", variant="secondary", size="sm")
@@ -414,26 +417,15 @@ Submit via GitHub Pull Request:
         outputs=[export_btn]
     )
     
-    def add_model_and_compare(selected_model, current_selected):
-        if not selected_model:
-            comparison_html, plot = compare_models(current_selected) if current_selected else (default_compare_html, None)
-            return (
-                current_selected,
-                gr.update(value=None, choices=[]),
-                gr.update(choices=current_selected, value=current_selected),
-                comparison_html,
-                plot
-            )
-        
-        if selected_model not in current_selected:
-            current_selected = current_selected + [selected_model]
-        
-        comparison_html, plot = compare_models(current_selected)
-        
+    def add_models_from_search(selected_from_results, current_selected):
+        selected_from_results = selected_from_results or []
+        current_selected = current_selected or []
+        merged = list(dict.fromkeys(current_selected + selected_from_results))
+        comparison_html, plot = compare_models(merged) if merged else (default_compare_html, None)
         return (
-            current_selected,
-            gr.update(value=None, choices=[]),
-            gr.update(choices=current_selected, value=current_selected),
+            merged,
+            gr.update(choices=[], value=[]),
+            gr.update(choices=merged, value=merged),
             comparison_html,
             plot
         )
@@ -445,23 +437,30 @@ Submit via GitHub Pull Request:
     def clear_all_models():
         return (
             [],
-            gr.update(value=None, choices=[]),
+            gr.update(value=""),
+            gr.update(choices=[], value=[]),
             gr.update(choices=[], value=[]),
             default_compare_html,
             None
         )
     
-    model_dropdown.key_up(
+    search_button.click(
+        fn=get_model_suggestions,
+        inputs=[model_search_box],
+        outputs=[search_results],
+        queue=False,
+    )
+    model_search_box.submit(
         fn=get_model_suggestions,
-        inputs=[model_dropdown],
-        outputs=[model_dropdown],
-        show_progress="hidden",
+        inputs=[model_search_box],
+        outputs=[search_results],
+        queue=False,
     )
     
-    model_dropdown.select(
-        fn=add_model_and_compare,
-        inputs=[model_dropdown, selected_models_state],
-        outputs=[selected_models_state, model_dropdown, selected_models_group, model_card_view, radar_view]
+    search_results.change(
+        fn=add_models_from_search,
+        inputs=[search_results, selected_models_state],
+        outputs=[selected_models_state, search_results, selected_models_group, model_card_view, radar_view],
     )
     
     selected_models_group.change(
@@ -472,7 +471,7 @@ Submit via GitHub Pull Request:
     
     clear_models_btn.click(
         fn=clear_all_models,
-        outputs=[selected_models_state, model_dropdown, selected_models_group, model_card_view, radar_view]
+        outputs=[selected_models_state, model_search_box, search_results, selected_models_group, model_card_view, radar_view]
     )
     
     export_comparison_btn.click(

data_loader.py

@@ -6,15 +6,18 @@ 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
+# Compact search index: tuples (model_lower, model_name, leaderboard_lower)
 MODEL_SEARCH_INDEX = []
-MODEL_NAMES = []
+# Prefix map for fast narrowing by model prefix
+MODEL_PREFIX_MAP = {}
+# Lightweight incremental cache
+LAST_QUERY = ""
+LAST_RESULTS = []
 DATA_DIR = Path("leaderboard_data")
 
 
@@ -329,68 +332,83 @@ def clear_cache():
 
 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():
+    global MODEL_SEARCH_INDEX, MODEL_PREFIX_MAP, LAST_QUERY, LAST_RESULTS
+    entries = []
+    for leaderboard_name, parsed_items in HF_DATASET_CACHE.items():
+        lb_lower = leaderboard_name.lower()
         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")
+            model_name = item.get("model", "")
+            entries.append((model_name.lower(), model_name, lb_lower))
+    # Sort by model_lower for prefix binary search
+    MODEL_SEARCH_INDEX = sorted(entries, key=lambda x: x[0])
+    # Build small prefix map (first 2 chars of model) to narrow searches
+    MODEL_PREFIX_MAP = {}
+    for model_lower, model_name, lb_lower in MODEL_SEARCH_INDEX:
+        key = model_lower[:2] if len(model_lower) >= 2 else model_lower
+        MODEL_PREFIX_MAP.setdefault(key, []).append((model_lower, model_name, lb_lower))
+    # Reset incremental cache
+    LAST_QUERY = ""
+    LAST_RESULTS = []
+    print(f"Built search index with {len(MODEL_SEARCH_INDEX)} entries")
 
 
 def get_model_suggestions_fast(query, limit=15):
-    """Fast search: prefix first, then fuzzy (rapidfuzz) as fallback."""
+    """Fast search with prefix narrowing and incremental reuse (substring only)."""
+    global LAST_QUERY, LAST_RESULTS
     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
+    # Incremental reuse: if user keeps typing the same prefix, reuse last pool
+    base_pool = None
+    if LAST_QUERY and query_lower.startswith(LAST_QUERY) and LAST_RESULTS:
+        base_pool = LAST_RESULTS
+    else:
+        prefix_key = query_lower[:2]
+        base_pool = MODEL_PREFIX_MAP.get(prefix_key, MODEL_SEARCH_INDEX)
     
-    # 2. If not enough, do substring search (still O(n) but only if needed)
+    # 1) Prefix match on model names
+    if base_pool is MODEL_SEARCH_INDEX:
+        idx = bisect_left(MODEL_SEARCH_INDEX, (query_lower,))
+        while idx < len(MODEL_SEARCH_INDEX) and len(results) < limit:
+            name_lower, name_orig, lb_lower = MODEL_SEARCH_INDEX[idx]
+            if name_lower.startswith(query_lower):
+                results.append((0, len(name_lower), name_orig))
+                idx += 1
+            else:
+                break
+    else:
+        for name_lower, name_orig, lb_lower in base_pool:
+            if name_lower.startswith(query_lower):
+                results.append((0, len(name_lower), name_orig))
+                if len(results) >= limit:
+                    break
+    
+    # 2) Substring fallback on the narrowed pool
     if len(results) < limit:
         seen = {r[2] for r in results}
-        for name_lower, name_orig in MODEL_SEARCH_INDEX:
+        # Use full index for substring to catch leaderboard-name matches
+        scan_pool = MODEL_SEARCH_INDEX
+        for name_lower, name_orig, lb_lower in scan_pool:
             if name_orig in seen:
                 continue
-            if query_lower in name_lower:
-                pos = name_lower.find(query_lower)
+            pos_model = name_lower.find(query_lower)
+            pos_lb = lb_lower.find(query_lower)
+            if pos_model != -1 or pos_lb != -1:
+                # Prefer model matches; leaderboard-only matches still allowed
+                pos = pos_model if pos_model != -1 else (pos_lb + 1)
                 results.append((pos, len(name_lower), name_orig))
-                if len(results) >= limit * 2:  # Get extra for sorting
+                if len(results) >= limit * 2:
                     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]))
+    
+    # Update incremental cache
+    LAST_QUERY = query_lower
+    LAST_RESULTS = base_pool if base_pool is not None else MODEL_SEARCH_INDEX
+    
     return [r[2] for r in results[:limit]]
 
 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 authors = [
     { name = "Sree Harsha Nelaturu", email = "nelaturu.harsha@gmail.com" },
-    { name = "Every Eval Ever Team"}
+    { name = "Every Eval Ever Team" }
 ]
 name = "e3_space"
 version = "0.1.1"
@@ -13,5 +13,4 @@ dependencies = [
     "gradio>=6.1.0",
     "pandas>=2.3.2",
     "plotly>=6.5.0",
-    "rapidfuzz>=3.0.0",
 ]

ui_components.py

@@ -57,6 +57,44 @@ body, .gradio-container {
     box-shadow: none !important;
 }
 
+/* Match pill styling */
+.match-pills .wrap,
+.match-pills .container {
+    display: flex !important;
+    flex-wrap: wrap !important;
+    gap: 0.35rem !important;
+}
+.match-pills .wrap > div,
+.match-pills .container > div {
+    margin: 0 !important;
+}
+.match-pills input[type="checkbox"] {
+    display: none;
+}
+.match-pills label {
+    display: inline-flex;
+    align-items: center;
+    border: 1px solid #d6d9de;
+    background: #f5f7fb;
+    border-radius: 999px;
+    padding: 0.28rem 0.75rem;
+    font-weight: 500;
+    color: #0a0a0a;
+    transition: all 120ms ease;
+    cursor: pointer;
+}
+.match-pills label:hover {
+    border-color: #3b82f6;
+    background: #eef4ff;
+}
+.match-pills input[type="checkbox"]:checked + label {
+    border-color: #3b82f6;
+    background: rgba(59, 130, 246, 0.12);
+    color: #0a0a0a;
+    font-weight: 600;
+}
+
+
 .app-header {
     display: flex;
     align-items: center;

uv.lock

@@ -336,7 +336,6 @@ dependencies = [
     { name = "gradio" },
     { name = "pandas" },
     { name = "plotly" },
-    { name = "rapidfuzz" },
 ]
 
 [package.metadata]
@@ -345,7 +344,6 @@ 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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