Update app.py

app.py

@@ -26,14 +26,12 @@ NUM = [
 def ensure_emb():
     if not (os.path.exists('emb_text.npy') and os.path.exists('emb_num.npy')):
         from sentence_transformers import SentenceTransformer
-        # text
         m = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
         te = m.encode(DF['text_record'].astype(str).tolist(),
                       batch_size=256, show_progress_bar=True, normalize_embeddings=True)
         np.save('emb_text.npy', np.asarray(te, dtype='float32'))
-        # numeric (invert 0-100 time)
         X = DF[NUM].copy()
-        X['zero_to_100_kmh_s'] = -X['zero_to_100_kmh_s']
+        X['zero_to_100_kmh_s'] = -X['zero_to_100_kmh_s']  # smaller time = better
         Xs = StandardScaler().fit_transform(X.values.astype('float32'))
         np.save('emb_num.npy', Xs.astype('float32'))
     return np.load('emb_text.npy'), np.load('emb_num.npy')
@@ -49,17 +47,15 @@ def _map():
                 'years': sorted(g2['year'].astype(int).unique().tolist())
             }
     return m
-
 MAP = _map()
 
-def models_for(mk):
-    return sorted(MAP.get(mk, {}).keys()) if mk else []
+def models_for(mk): return sorted(MAP.get(mk, {}).keys()) if mk else []
 
 def trim_year(mk, md):
     d = MAP.get(mk, {}).get(md, {})
     return d.get('trims', []), d.get('years', [])
 
-# ---------- helpers ----------
+# ---------- filtering & rec ----------
 def anchor_row(mk, md, tr, yr):
     sub = DF.copy()
     if mk: sub = sub[sub['make'] == mk]
@@ -69,13 +65,12 @@ def anchor_row(mk, md, tr, yr):
     if sub.empty: return None
     return sub.sort_values('popularity_score', ascending=False).iloc[0]
 
-def apply_filters(df, body, fuel, yr_rng, price, safety, rel):
+def apply_filters(df, body, fuel, y_min, y_max, p_min, p_max, safety, rel):
     out = df.copy()
     if body != 'Any': out = out[out['body_type'] == body]
     if fuel != 'Any': out = out[out['fuel'] == fuel]
-    y0, y1 = yr_rng; p0, p1 = price
-    out = out[(out['year'] >= y0) & (out['year'] <= y1)]
-    out = out[(out['price_usd'] >= p0) & (out['price_usd'] <= p1)]
+    out = out[(out['year'] >= y_min) & (out['year'] <= y_max)]
+    out = out[(out['price_usd'] >= p_min) & (out['price_usd'] <= p_max)]
     out = out[(out['safety_rating'] >= safety) & (out['reliability_score'] >= rel)]
     return out
 
@@ -91,11 +86,12 @@ def fmt_card(r):
         f"Reliability {int(r['reliability_score'])}/100 • Safety {int(r['safety_rating'])}★"
     )
 
-def recommend(mk, md, tr, yr, topk, alpha, body, fuel, yr_rng, price, safety, rel):
+def recommend(mk, md, tr, yr, topk, alpha,
+              body, fuel, y_min, y_max, p_min, p_max, safety, rel):
     a = anchor_row(mk, md, tr, yr)
     if a is None:
         return "No match for that combo.", None, None
-    sub = apply_filters(DF, body, fuel, yr_rng, price, safety, rel)
+    sub = apply_filters(DF, body, fuel, int(y_min), int(y_max), int(p_min), int(p_max), int(safety), int(rel))
     if sub.empty:
         return "No cars after filters.", None, None
 
@@ -121,7 +117,7 @@ def recommend(mk, md, tr, yr, topk, alpha, body, fuel, yr_rng, price, safety, re
             'ownership_cost_score','safety_rating','similarity_%']
     return fmt_card(a), sel[cols], f"α = {alpha:.2f} (text ↔ numeric)"
 
-# ---------- UI ----------
+# ---------- UI (no RangeSlider; use min/max sliders) ----------
 with gr.Blocks() as demo:
     gr.Markdown("# RideSearch — pick a car, get similar across brands")
 
@@ -136,18 +132,25 @@ with gr.Blocks() as demo:
             t, y = trim_year(a,b); return t, y
         md.change(_up, [mk, md], [tr, yr])
 
+        ylo, yhi = int(DF['year'].min()), int(DF['year'].max())
+        plo, phi = int(DF['price_usd'].min()), int(DF['price_usd'].max())
+
         with gr.Row():
             body = gr.Dropdown(['Any']+sorted(DF['body_type'].unique().tolist()), value='Any', label='Body')
             fuel = gr.Dropdown(['Any']+sorted(DF['fuel'].unique().tolist()), value='Any', label='Fuel')
-            yr_rng = gr.RangeSlider(int(DF['year'].min()), int(DF['year'].max()),
-                                    value=[int(DF['year'].min()), int(DF['year'].max())],
-                                    step=1, label='Year range')
+
+        with gr.Row():
+            y_min = gr.Slider(ylo, yhi, value=ylo, step=1, label='Year min')
+            y_max = gr.Slider(ylo, yhi, value=yhi, step=1, label='Year max')
+
+        with gr.Row():
+            p_min = gr.Slider(plo, phi, value=plo, step=500, label='Price min (USD)')
+            p_max = gr.Slider(plo, phi, value=min(phi, 60000), step=500, label='Price max (USD)')
+
         with gr.Row():
-            price = gr.RangeSlider(int(DF['price_usd'].min()), int(DF['price_usd'].max()),
-                                   value=[int(DF['price_usd'].min()), min(int(DF['price_usd'].max()), 60000)],
-                                   step=500, label='Price (USD)')
             safety = gr.Slider(3,5,value=4,step=1,label='Min Safety ★')
             rel = gr.Slider(55,99,value=70,step=1,label='Min Reliability')
+
         with gr.Row():
             topk = gr.Slider(1,10,value=5,step=1,label='Recommendations')
             alpha = gr.Slider(0,1,value=0.7,step=0.05,label='α — Text vs Numeric')
@@ -157,9 +160,11 @@ with gr.Blocks() as demo:
         table = gr.Dataframe(interactive=False)
         note = gr.Markdown()
 
-        go.click(recommend, [mk,md,tr,yr,topk,alpha,body,fuel,yr_rng,price,safety,rel],
-                 [anchor_md, table, note])
+        go.click(
+            recommend,
+            [mk,md,tr,yr,topk,alpha,body,fuel,y_min,y_max,p_min,p_max,safety,rel],
+            [anchor_md, table, note]
+        )
 
 # IMPORTANT for Hugging Face Spaces: do NOT call demo.launch()
-# Returning `demo` is enough:
 demo