Upload 2 files

app-2.py

@@ -0,0 +1,162 @@
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+CSV_FILE = "all-vehicles-model@public.csv"
+
+# עמודות חשובות מתוך הדאטה
+COLS = {
+    "make": "Make",
+    "model": "Model",
+    "fuel1": "Fuel Type1",
+    "fuel_any": "Fuel Type",
+    "drive": "Drive",
+    "trans": "Transmission",
+    "cyl": "Cylinders",
+    "disp": "Engine displacement",
+    "mpg_city_f1": "City Mpg For Fuel Type1",
+    "mpg_hwy_f1": "Highway Mpg For Fuel Type1",
+    "mpg_comb_f1": "Combined Mpg For Fuel Type1",
+    "mpg_city_f2": "City Mpg For Fuel Type2",
+    "mpg_hwy_f2": "Highway Mpg For Fuel Type2",
+    "mpg_comb_f2": "Combined Mpg For Fuel Type2",
+    "annual_fuel_cost_f1": "Annual Fuel Cost For Fuel Type1",
+    "annual_fuel_cost_f2": "Annual Fuel Cost For Fuel Type2",
+    "ev_range_f2": "Epa Range For Fuel Type2",
+    "charge_240": "Time to charge at 240V",
+    "co2_f1": "Co2 Fuel Type1",
+    "co2_tailpipe_f1": "Co2  Tailpipe For Fuel Type1",
+    "hatch_lug": "Hatchback luggage volume",
+    "hatch_pass": "Hatchback passenger volume",
+    "year": "Year"
+}
+
+def load_data():
+    df = pd.read_csv(CSV_FILE, sep=";", encoding="utf-8", engine="python")
+    # המרות טיפוס בסיסיות
+    for c in ["City Mpg For Fuel Type1", "Highway Mpg For Fuel Type1", "Combined Mpg For Fuel Type1",
+              "City Mpg For Fuel Type2", "Highway Mpg For Fuel Type2", "Combined Mpg For Fuel Type2",
+              "Annual Fuel Cost For Fuel Type1", "Annual Fuel Cost For Fuel Type2",
+              "Epa Range For Fuel Type2", "Time to charge at 240V", "Cylinders", "Engine displacement",
+              "Hatchback luggage volume", "Hatchback passenger volume"]:
+        if c in df.columns:
+            df[c] = pd.to_numeric(df[c], errors="coerce")
+    # הורדת כפילויות בסיסית
+    keep_cols = [v for v in COLS.values() if v in df.columns]
+    subset = [c for c in keep_cols if c in ["Make","Model","Year"]]
+    if subset:
+        df = df.drop_duplicates(subset=subset)
+    return df
+
+DF = load_data()
+
+def options_safe(col):
+    if col in DF.columns:
+        vals = DF[col].dropna().astype(str).str.strip().replace("", np.nan).dropna().unique().tolist()
+        vals = [v for v in vals if v.lower() not in {"nan","none"}]
+        vals.sort()
+        return vals[:50]
+    return []
+
+FUEL_OPTS = sorted(set([*options_safe(COLS["fuel1"]), *options_safe(COLS["fuel_any"]), "No preference"]))
+DRIVE_OPTS = [*options_safe(COLS["drive"]), "No preference"]
+TRANS_OPTS = [*options_safe(COLS["trans"]), "No preference"]
+
+def recommend(budget_fuel_per_year, usage, daily_km, seats_min, fuel_pref, drive_pref, trans_pref, cargo_need, perf_pref):
+    df = DF.copy()
+
+    # סינונים לפי העדפות
+    if fuel_pref and fuel_pref != "No preference":
+        df = df[(df.get(COLS["fuel1"], "").astype(str).str.contains(fuel_pref, case=False, na=False)) |
+                (df.get(COLS["fuel_any"], "").astype(str).str.contains(fuel_pref, case=False, na=False))]
+    if drive_pref and drive_pref != "No preference" and COLS["drive"] in df.columns:
+        df = df[df[COLS["drive"]].astype(str).str.contains(drive_pref, case=False, na=False)]
+    if trans_pref and trans_pref != "No preference" and COLS["trans"] in df.columns:
+        df = df[df[COLS["trans"]].astype(str).str.contains(trans_pref, case=False, na=False)]
+
+    def norm(s):
+        s = pd.to_numeric(s, errors="coerce")
+        return (s - s.min()) / (s.max() - s.min() + 1e-9)
+
+    # יעילות דלק לפי שימוש
+    if usage == "עיר":
+        eff = df.get(COLS["mpg_city_f1"], df.get(COLS["mpg_comb_f1"]))
+    elif usage == "בינעירוני":
+        eff = df.get(COLS["mpg_hwy_f1"], df.get(COLS["mpg_comb_f1"]))
+    else:
+        eff = df.get(COLS["mpg_comb_f1"])
+    eff_score = norm(eff) if eff is not None else pd.Series(0, index=df.index)
+
+    # עלות דלק שנתית נמוכה עדיפה
+    fuel_cost = df.get(COLS["annual_fuel_cost_f1"])
+    fuel_cost_score = 1 - norm(fuel_cost) if fuel_cost is not None else pd.Series(0.5, index=df.index)
+
+    # התאמת EV לפי טווח ונסועה יומית
+    ev_range = df.get(COLS["ev_range_f2"])
+    if ev_range is not None and daily_km:
+        ev_ok = (ev_range >= (daily_km * 0.62 * 3))  # ק"מ למייל, פי 3 מרווח ביטחון
+        ev_score = ev_ok.astype(float)
+    else:
+        ev_score = pd.Series(0.5, index=df.index)
+
+    # נפח מטען
+    cargo = df.get(COLS["hatch_lug"])
+    cargo_score = norm(cargo) if (cargo is not None and cargo_need) else pd.Series(0.5, index=df.index)
+
+    # ביצועים מול חסכון לפי צילינדרים ונפח מנוע
+    cyl = df.get(COLS["cyl"]); disp = df.get(COLS["disp"])
+    perf_raw = norm(cyl).fillna(0.5) * 0.5 + norm(disp).fillna(0.5) * 0.5
+    econ_raw = 1 - perf_raw
+    perf_mix = perf_pref * perf_raw + (1 - perf_pref) * econ_raw
+
+    # משקולות
+    w_eff = 0.3
+    w_cost = 0.25
+    w_ev = 0.15
+    w_cargo = 0.1
+    w_perf = 0.2
+
+    score = w_eff*eff_score.fillna(0.5) + w_cost*fuel_cost_score.fillna(0.5) + w_ev*ev_score.fillna(0.5) + \
+            w_cargo*cargo_score.fillna(0.5) + w_perf*perf_mix.fillna(0.5)
+
+    df_out = df.copy()
+    df_out["Score"] = score.round(4)
+    show_cols = [c for c in [
+        COLS["make"], COLS["model"], "Year" if "Year" in df.columns else None,
+        COLS["fuel1"] if COLS["fuel1"] in df.columns else COLS["fuel_any"],
+        COLS["drive"], COLS["trans"],
+        COLS["mpg_city_f1"], COLS["mpg_hwy_f1"], COLS["mpg_comb_f1"],
+        COLS["annual_fuel_cost_f1"], COLS["ev_range_f2"],
+        COLS["cyl"], COLS["disp"],
+        "Score"
+    ] if (c and (c in df.columns)) or c=="Score"]
+
+    return df_out.sort_values("Score", ascending=False).head(10)[show_cols]
+
+with gr.Blocks(title="ממליץ רכבים") as demo:
+    gr.Markdown("# ממליץ רכבים חכם\nהאפליקציה מתאימה דגמים לצרכים שלך על בסיס דאטה של EPA.\n")
+
+    with gr.Row():
+        usage = gr.Radio(["עיר", "בינעירוני", "מעורב"], value="מעורב", label="אופי שימוש")
+        daily_km = gr.Slider(0, 200, value=30, step=5, label="נסועה יומית ממוצעת בקילומטרים")
+        budget_fuel = gr.Slider(0, 6000, value=3000, step=100, label="תקציב דלק או חשמל שנתי")
+
+    with gr.Row():
+        fuel_pref = gr.Dropdown(choices=FUEL_OPTS, value="No preference", label="עדיפות לסוג דלק")
+        drive_pref = gr.Dropdown(choices=DRIVE_OPTS, value="No preference", label="הנעה")
+        trans_pref = gr.Dropdown(choices=TRANS_OPTS, value="No preference", label="תיבת הילוכים")
+
+    with gr.Row():
+        cargo_need = gr.Slider(0, 800, value=0, step=20, label="נפח מטען מינימלי רצוי")
+        perf_pref = gr.Slider(0, 1, value=0.4, step=0.05, label="עדיפות 0 חסכון 1 ביצועים")
+        seats_min = gr.Slider(2, 8, value=4, step=1, label="מושבים מינימום")
+
+    btn = gr.Button("מצא רכבים")
+    out = gr.Dataframe(interactive=False, wrap=True, label="התאמות מומלצות . טופ 10")
+
+    btn.click(fn=recommend,
+              inputs=[budget_fuel, usage, daily_km, seats_min, fuel_pref, drive_pref, trans_pref, cargo_need, perf_pref],
+              outputs=out)
+
+demo.queue().launch()

requirements-2.txt

@@ -0,0 +1,3 @@
+gradio
+pandas
+numpy