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app_new.py

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+
+# app_new.py — RideSearch (brand-correct trims, cross-brand, smart fallbacks, optional photos)
+# Drop into your Hugging Face Space and set as the app file.
+
+import os, glob, urllib.parse
+import numpy as np
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import StandardScaler
+import gradio as gr
+
+def load_df():
+    if os.path.exists('RideSearch_dataset.csv'):
+        return pd.read_csv('RideSearch_dataset.csv')
+    parts = sorted(glob.glob('RideSearch_part*_small.csv'))
+    if not parts:
+        raise FileNotFoundError("Upload RideSearch_dataset.csv OR the 10 parts RideSearch_part*_small.csv.")
+    df = pd.concat([pd.read_csv(p) for p in parts], ignore_index=True)
+    df.to_csv('RideSearch_dataset.csv', index=False)
+    return df
+
+DF = load_df()
+
+NUM_COLS = [
+    'horsepower','zero_to_100_kmh_s','seats','cargo_liters','price_usd',
+    'popularity_score','comfort_score','reliability_score','tech_score',
+    'ownership_cost_score','safety_rating'
+]
+
+def ensure_emb():
+    txt_ok = os.path.exists('emb_text.npy')
+    num_ok = os.path.exists('emb_num.npy')
+    if txt_ok and num_ok:
+        return np.load('emb_text.npy'), np.load('emb_num.npy')
+    from sentence_transformers import SentenceTransformer
+    m = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+    texts = DF['text_record'].astype(str).tolist()
+    Etext = m.encode(texts, batch_size=256, show_progress_bar=True, normalize_embeddings=True)
+    Etext = np.asarray(Etext, dtype='float32'); np.save('emb_text.npy', Etext)
+    X = DF[NUM_COLS].copy()
+    if 'zero_to_100_kmh_s' in X.columns:
+        # lower time is better -> invert so higher is better
+        X['zero_to_100_kmh_s'] = -X['zero_to_100_kmh_s'].astype('float32')
+    Xs = StandardScaler().fit_transform(X.values.astype('float32'))
+    Enum = Xs.astype('float32'); np.save('emb_num.npy', Enum)
+    return Etext, Enum
+
+# ---- trims mapping (loaded from trims_map.json if present) ----
+import json
+TRIM_CHOICES = {}
+TRIM_ALIAS_TO_GENERIC = {}
+if os.path.exists('trims_map.json'):
+    with open('trims_map.json','r',encoding='utf-8') as f:
+        data = json.load(f)
+        TRIM_CHOICES = {tuple(k.split('||')): v['display'] for k, v in data.items()}
+        TRIM_ALIAS_TO_GENERIC = {}
+        for k, v in data.items():
+            for alias, generic in v['alias_to_generic'].items():
+                TRIM_ALIAS_TO_GENERIC[alias] = generic
+else:
+    # minimal fallback so the app still runs
+    TRIM_CHOICES = {("BMW","3 Series"): ["320i","330i","330e","340i","M3"]}
+    TRIM_ALIAS_TO_GENERIC = {"320i":"Base","330i":"Sport","330e":"Sport","340i":"Premium","M3":"Performance"}
+
+def generic_to_display(make, model, generic_trim):
+    if not generic_trim:
+        return ""
+    if (make, model) not in TRIM_CHOICES:
+        return str(generic_trim)
+    for alias in TRIM_CHOICES[(make, model)]:
+        if TRIM_ALIAS_TO_GENERIC.get(alias) == generic_trim:
+            return alias
+    return str(generic_trim)
+
+def models_for(make):
+    if not make:
+        return gr.update(choices=[], value=None)
+    opts = sorted(DF.loc[DF['make'].eq(make), 'model'].dropna().unique().tolist())
+    return gr.update(choices=opts, value=None)
+
+def trim_year(make, model):
+    if make and model and (make, model) in TRIM_CHOICES:
+        trims = TRIM_CHOICES[(make, model)]
+    else:
+        sub = DF
+        if make:  sub = sub[sub['make'] == make]
+        if model: sub = sub[sub['model'] == model]
+        trims = sorted(sub['trim'].astype(str).dropna().unique().tolist())[:20]
+    if make and model:
+        years = sorted(
+            DF.loc[(DF['make'].eq(make)) & (DF['model'].eq(model)), 'year']
+              .dropna().astype(int).unique().tolist()
+        )
+    else:
+        years = []
+    return trims, years
+
+def on_model_change(make, model):
+    trims, years = trim_year(make, model)
+    return gr.update(choices=trims, value=None), gr.update(choices=years, value=None)
+
+def apply_filters(df, body, fuel, y_min, y_max, p_min, p_max, safety, reliab):
+    out = df.copy()
+    if body != 'Any': out = out[out['body_type'] == body]
+    if fuel != 'Any': out = out[out['fuel'] == fuel]
+    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'] >= reliab)]
+    return out
+
+def placeholder_svg_data_uri(title):
+    svg = f\"\"\"<svg xmlns='http://www.w3.org/2000/svg' width='480' height='320'>
+      <rect width='100%' height='100%' fill='#e8eef7'/>
+      <text x='50%' y='50%' dominant-baseline='middle' text-anchor='middle'
+            font-family='Arial' font-size='26' fill='#223'>
+        {title}
+      </text>
+    </svg>\"\"\"
+    return "data:image/svg+xml;utf8," + urllib.parse.quote(svg)
+
+def build_gallery_html(df_rows):
+    cards = []
+    for _, r in df_rows.iterrows():
+        label = f"{r['make']} {r['model']} {generic_to_display(r['make'], r['model'], r['trim'])}"
+        img_src = ""
+        if 'image_url' in r and isinstance(r['image_url'], str) and r['image_url'].strip():
+            img_src = r['image_url'].strip()
+        else:
+            img_src = placeholder_svg_data_uri(f"{r['make']} {r['model']}")
+        cards.append(f\"\"\"
+        <div style="width:240px;margin:6px;border:1px solid #ddd;border-radius:12px;overflow:hidden;background:#fff;">
+          <img src="{img_src}" style="width:240px;height:160px;object-fit:cover;display:block" />
+          <div style="padding:8px 10px;font:14px/1.3 Arial,sans-serif;color:#111">{label}</div>
+        </div>
+        \"\"\")
+    return f"<div style='display:flex;flex-wrap:wrap'>{''.join(cards)}</div>"
+
+def find_anchor(make, model, trim_display, year):
+    # Map display trim to dataset generic using alias mapping if present
+    def norm(make, model, t):
+        if not t: return None
+        # if the alias exists globally, use its generic
+        return TRIM_ALIAS_TO_GENERIC.get(t, t)
+
+    trim_generic = norm(make, model, trim_display)
+
+    sub = DF.copy()
+    if make:  sub = sub[sub['make'] == make]
+    if model: sub = sub[sub['model'] == model]
+
+    def pick(df_):
+        if df_.empty: return None
+        return df_.sort_values('popularity_score', ascending=False).iloc[0]
+
+    exact = sub.copy()
+    if trim_generic: exact = exact[exact['trim'] == trim_generic]
+    if year:         exact = exact[exact['year'] == year]
+    if not exact.empty:
+        return pick(exact)
+
+    if year:
+        y_only = sub[sub['year'] == year]
+        if not y_only.empty: return pick(y_only)
+
+    if trim_generic:
+        t_only = sub[sub['trim'] == trim_generic]
+        if not t_only.empty: return pick(t_only)
+
+    return pick(sub)
+
+def recommend(make, model, trim_display, year, topk, alpha,
+              body, fuel, y_min, y_max, p_min, p_max, safety, reliab,
+              cross_brand_only=True, exclude_same_model=True):
+
+    a = find_anchor(make, model, trim_display, year)
+    if a is None:
+        return "No match for that combo.", None, "", None
+
+    pool = DF.copy()
+    if cross_brand_only:
+        pool = pool[pool['make'] != a['make']]
+    if exclude_same_model:
+        pool = pool[~((pool['make'] == a['make']) & (pool['model'] == a['model']))]
+
+    pool = apply_filters(pool, body, fuel, int(y_min), int(y_max), int(p_min), int(p_max), int(safety), int(reliab))
+    if pool.empty:
+        return "No cars after filters. Try widening year/price/safety.", None, "", None
+
+    Etext, Enum = ensure_emb()
+    idx_anchor = int(a.name)
+    cand_idx = pool.index.values
+
+    st = cosine_similarity(Etext[idx_anchor:idx_anchor+1], Etext[cand_idx])[0]
+    sn = cosine_similarity(Enum[idx_anchor:idx_anchor+1],  Enum[cand_idx])[0]
+    s = float(alpha)*st + (1-float(alpha))*sn
+
+    order = np.argsort(-s)
+    seen = set(); chosen = []
+    for j in order:
+        r = DF.loc[cand_idx[j]]
+        key = (r['make'], r['model'])
+        if key in seen: continue  # enforce cross-brand & unique model
+        seen.add(key)
+        chosen.append(cand_idx[j])
+        if len(chosen) >= int(topk): break
+
+    if not chosen:
+        return "No recommendations found after constraints.", None, "", None
+
+    sel = DF.loc[chosen].copy()
+    sel['trim_display'] = sel.apply(lambda r: generic_to_display(r['make'], r['model'], r['trim']), axis=1)
+
+    sim_lookup = {cand_idx[j]: round(float(s[j])*100, 1) for j in order}
+    sel['similarity_%'] = sel.index.map(lambda k: sim_lookup.get(k, 0.0))
+
+    cols = ['name','make','model','trim_display','year','body_type','fuel','engine_type',
+            'price_usd','horsepower','zero_to_100_kmh_s','popularity_score','comfort_score',
+            'reliability_score','tech_score','ownership_cost_score','safety_rating','similarity_%']
+
+    anchor_text = (f"**{a['make']} {a['model']} {generic_to_display(a['make'], a['model'], a['trim'])} "
+                   f"{int(a['year'])}**  \\n"
+                   f"Body: {a['body_type']} • Fuel: {a['fuel']} • Engine: {a['engine_type']}  \\n"
+                   f"HP: {int(a['horsepower'])} • 0–100: {a['zero_to_100_kmh_s']}s • Price: ${int(a['price_usd']):,}  \\n"
+                   f"Popularity {int(a['popularity_score'])}/10 • Comfort {int(a['comfort_score'])}/10 • "
+                   f"Reliability {int(a['reliability_score'])}/100 • Safety {int(a['safety_rating'])}★")
+
+    note = (f"α = {float(alpha):.2f} (text ↔ numeric) • Cross-brand only = {cross_brand_only} "
+            f"• Exclude same model = {exclude_same_model}")
+
+    gallery = build_gallery_html(sel)
+    return anchor_text, sel[cols], note, gallery
+
+def build_ui():
+    y_lo, y_hi = int(DF['year'].min()), int(DF['year'].max())
+    p_lo, p_hi = int(DF['price_usd'].min()), int(DF['price_usd'].max())
+
+    with gr.Blocks() as demo:
+        gr.Markdown("# RideSearch — cross-brand recommendations with real trims")
+
+        with gr.Tab("Pick & Recommend"):
+            with gr.Row():
+                mk = gr.Dropdown(sorted(DF['make'].dropna().unique().tolist()), label="Make", value=None)
+                md = gr.Dropdown([], label="Model", value=None)
+                tr = gr.Dropdown([], label="Trim (optional)", value=None)
+                yr = gr.Dropdown([], label="Year (optional)", value=None)
+            mk.change(models_for, mk, md)
+            md.change(lambda a,b: on_model_change(a,b), [mk, md], [tr, yr])
+
+            with gr.Row():
+                body = gr.Dropdown(['Any'] + sorted(DF['body_type'].dropna().unique().tolist()), value='Any', label='Body')
+                fuel = gr.Dropdown(['Any'] + sorted(DF['fuel'].dropna().unique().tolist()), value='Any', label='Fuel')
+            with gr.Row():
+                y_min = gr.Slider(y_lo, y_hi, value=y_lo, step=1, label='Year min')
+                y_max = gr.Slider(y_lo, y_hi, value=y_hi, step=1, label='Year max')
+            with gr.Row():
+                p_min = gr.Slider(p_lo, p_hi, value=p_lo, step=500, label='Price min (USD)')
+                p_max = gr.Slider(p_lo, p_hi, value=min(p_hi, 80000), step=500, label='Price max (USD)')
+            with gr.Row():
+                safety = gr.Slider(3, 5, value=4, step=1, label='Min Safety ★')
+                reliab = 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')
+            with gr.Row():
+                cross = gr.Checkbox(label="Cross-brand only", value=True)
+                xmodel = gr.Checkbox(label="Exclude same model family", value=True)
+
+            go = gr.Button("Recommend")
+            anchor_md = gr.Markdown()
+            table = gr.Dataframe(interactive=False)
+            note = gr.Markdown()
+            gallery = gr.HTML()
+
+            go.click(
+                recommend,
+                [mk, md, tr, yr, topk, alpha, body, fuel, y_min, y_max, p_min, p_max, safety, reliab, cross, xmodel],
+                [anchor_md, table, note, gallery]
+            )
+        gr.Markdown("Tip: Add an 'image_url' column in the CSV for real photos.")
+
+    return demo
+
+demo = build_ui()
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)

trims_map.json

@@ -0,0 +1,382 @@
+{
+  "BMW||3 Series": {
+    "display": [
+      "318i",
+      "320i",
+      "330i",
+      "330e",
+      "340i",
+      "M3"
+    ],
+    "alias_to_generic": {
+      "318i": "Base",
+      "320i": "Base",
+      "330i": "Sport",
+      "330e": "Sport",
+      "340i": "Premium",
+      "M3": "Performance"
+    }
+  },
+  "Audi||A3": {
+    "display": [
+      "30 TFSI",
+      "35 TFSI",
+      "40 TFSI",
+      "45 TFSI",
+      "S3",
+      "RS3"
+    ],
+    "alias_to_generic": {
+      "30 TFSI": "Base",
+      "35 TFSI": "Base",
+      "40 TFSI": "Sport",
+      "45 TFSI": "Premium",
+      "S3": "Performance",
+      "RS3": "Performance"
+    }
+  },
+  "Audi||A4": {
+    "display": [
+      "35 TFSI",
+      "40 TFSI",
+      "45 TFSI",
+      "S4",
+      "RS4"
+    ],
+    "alias_to_generic": {
+      "35 TFSI": "Base",
+      "40 TFSI": "Sport",
+      "45 TFSI": "Premium",
+      "S4": "Performance",
+      "RS4": "Performance"
+    }
+  },
+  "Mercedes-Benz||C-Class": {
+    "display": [
+      "C180",
+      "C200",
+      "C220d",
+      "C300",
+      "AMG C43",
+      "AMG C63"
+    ],
+    "alias_to_generic": {
+      "C180": "Base",
+      "C200": "Base",
+      "C220d": "Base",
+      "C300": "Premium",
+      "AMG C43": "Performance",
+      "AMG C63": "Performance"
+    }
+  },
+  "Lexus||IS": {
+    "display": [
+      "IS 300",
+      "IS 350",
+      "IS 500 F SPORT"
+    ],
+    "alias_to_generic": {
+      "IS 300": "Base",
+      "IS 350": "Premium",
+      "IS 500 F SPORT": "Performance"
+    }
+  },
+  "Toyota||Corolla": {
+    "display": [
+      "L",
+      "LE",
+      "SE",
+      "XSE",
+      "GR"
+    ],
+    "alias_to_generic": {
+      "L": "Base",
+      "LE": "Base",
+      "SE": "Sport",
+      "XSE": "Premium",
+      "GR": "Performance"
+    }
+  },
+  "Mini||Cooper": {
+    "display": [
+      "Classic",
+      "Signature",
+      "Iconic",
+      "John Cooper Works"
+    ],
+    "alias_to_generic": {
+      "Classic": "Base",
+      "Signature": "Premium",
+      "Iconic": "Premium",
+      "John Cooper Works": "Performance"
+    }
+  },
+  "Jeep||Wrangler": {
+    "display": [
+      "Sport",
+      "Willys",
+      "Sahara",
+      "Rubicon",
+      "392"
+    ],
+    "alias_to_generic": {
+      "Sport": "Base",
+      "Willys": "Sport",
+      "Sahara": "Premium",
+      "Rubicon": "Performance",
+      "392": "Performance"
+    }
+  },
+  "Kia||Sportage": {
+    "display": [
+      "LX",
+      "EX",
+      "SX",
+      "X-Line",
+      "X-Pro"
+    ],
+    "alias_to_generic": {
+      "LX": "Base",
+      "EX": "Premium",
+      "SX": "Premium",
+      "X-Line": "Sport",
+      "X-Pro": "Performance"
+    }
+  },
+  "Land Rover||Range Rover Evoque": {
+    "display": [
+      "S",
+      "SE",
+      "R-Dynamic S",
+      "R-Dynamic SE",
+      "Autobiography"
+    ],
+    "alias_to_generic": {
+      "S": "Base",
+      "SE": "Premium",
+      "R-Dynamic S": "Sport",
+      "R-Dynamic SE": "Premium",
+      "Autobiography": "Premium"
+    }
+  },
+  "Lexus||RX": {
+    "display": [
+      "RX 350",
+      "RX 350h",
+      "RX 500h F SPORT"
+    ],
+    "alias_to_generic": {
+      "RX 350": "Premium",
+      "RX 350h": "Premium",
+      "RX 500h F SPORT": "Performance"
+    }
+  },
+  "Mazda||Mazda3": {
+    "display": [
+      "S",
+      "Select",
+      "Preferred",
+      "Premium",
+      "Turbo"
+    ],
+    "alias_to_generic": {
+      "S": "Base",
+      "Select": "Base",
+      "Preferred": "Premium",
+      "Premium": "Premium",
+      "Turbo": "Performance"
+    }
+  },
+  "Mitsubishi||Outlander": {
+    "display": [
+      "ES",
+      "SE",
+      "SEL",
+      "Black Edition",
+      "PHEV"
+    ],
+    "alias_to_generic": {
+      "ES": "Base",
+      "SE": "Sport",
+      "SEL": "Premium",
+      "Black Edition": "Premium",
+      "PHEV": "Premium"
+    }
+  },
+  "Nissan||X-Trail": {
+    "display": [
+      "Visia",
+      "Acenta",
+      "N-Connecta",
+      "Tekna",
+      "Tekna+"
+    ],
+    "alias_to_generic": {
+      "Visia": "Base",
+      "Acenta": "Base",
+      "N-Connecta": "Premium",
+      "Tekna": "Premium",
+      "Tekna+": "Premium"
+    }
+  },
+  "Peugeot||3008": {
+    "display": [
+      "Active",
+      "Allure",
+      "GT",
+      "GT Pack"
+    ],
+    "alias_to_generic": {
+      "Active": "Base",
+      "Allure": "Premium",
+      "GT": "Premium",
+      "GT Pack": "Premium"
+    }
+  },
+  "Porsche||911": {
+    "display": [
+      "Carrera",
+      "Carrera S",
+      "GTS",
+      "Turbo",
+      "GT3"
+    ],
+    "alias_to_generic": {
+      "Carrera": "Base",
+      "Carrera S": "Premium",
+      "GTS": "Premium",
+      "Turbo": "Performance",
+      "GT3": "Performance"
+    }
+  },
+  "Ram||1500": {
+    "display": [
+      "Tradesman",
+      "Big Horn",
+      "Laramie",
+      "Rebel",
+      "Limited"
+    ],
+    "alias_to_generic": {
+      "Tradesman": "Base",
+      "Big Horn": "Sport",
+      "Laramie": "Premium",
+      "Rebel": "Sport",
+      "Limited": "Premium"
+    }
+  },
+  "Renault||Clio": {
+    "display": [
+      "Authentique",
+      "Expression",
+      "Dynamique",
+      "RS Line"
+    ],
+    "alias_to_generic": {
+      "Authentique": "Base",
+      "Expression": "Sport",
+      "Dynamique": "Premium",
+      "RS Line": "Performance"
+    }
+  },
+  "Seat||Leon": {
+    "display": [
+      "Reference",
+      "Style",
+      "FR",
+      "Cupra"
+    ],
+    "alias_to_generic": {
+      "Reference": "Base",
+      "Style": "Sport",
+      "FR": "Sport",
+      "Cupra": "Performance"
+    }
+  },
+  "Skoda||Octavia": {
+    "display": [
+      "Active",
+      "Ambition",
+      "Style",
+      "RS"
+    ],
+    "alias_to_generic": {
+      "Active": "Base",
+      "Ambition": "Sport",
+      "Style": "Premium",
+      "RS": "Performance"
+    }
+  },
+  "Subaru||Outback": {
+    "display": [
+      "Base",
+      "Premium",
+      "Limited",
+      "Wilderness",
+      "Touring"
+    ],
+    "alias_to_generic": {
+      "Base": "Base",
+      "Premium": "Premium",
+      "Limited": "Premium",
+      "Wilderness": "Sport",
+      "Touring": "Premium"
+    }
+  },
+  "Tesla||Model 3": {
+    "display": [
+      "RWD",
+      "Long Range",
+      "Performance"
+    ],
+    "alias_to_generic": {
+      "RWD": "Base",
+      "Long Range": "Premium",
+      "Performance": "Performance"
+    }
+  },
+  "Volkswagen||Golf": {
+    "display": [
+      "Trendline",
+      "Comfortline",
+      "Highline",
+      "GTI",
+      "R"
+    ],
+    "alias_to_generic": {
+      "Trendline": "Base",
+      "Comfortline": "Base",
+      "Highline": "Premium",
+      "GTI": "Performance",
+      "R": "Performance"
+    }
+  },
+  "Volkswagen||Tiguan": {
+    "display": [
+      "S",
+      "SE",
+      "SEL",
+      "R-Line"
+    ],
+    "alias_to_generic": {
+      "S": "Base",
+      "SE": "Sport",
+      "SEL": "Premium",
+      "R-Line": "Performance"
+    }
+  },
+  "Volvo||XC60": {
+    "display": [
+      "Core",
+      "Plus",
+      "Ultimate",
+      "Polestar Engineered"
+    ],
+    "alias_to_generic": {
+      "Core": "Base",
+      "Plus": "Premium",
+      "Ultimate": "Premium",
+      "Polestar Engineered": "Performance"
+    }
+  }
+}