Update app.py

app.py

@@ -5,20 +5,13 @@ import gradio as gr
 import pycountry
 
 # === CONFIG ===
-DATA_FILE = "players.dataset.xlsx"  # upload this after the app files
-POSITIONS = ["CB","RB","LB","CDM","CM","CAM","RW","ST","LW"]
+DATA_FILE = "players.dataset.xlsx"
+BASE_POSITIONS = ["CB","RB","LB","CDM","CM","CAM","RW","ST","LW"]
 
-# User-specified exclusions for Nation values (exact/variant matches handled below)
 EXCLUDED_NATIONS = {
-    "PALESTINE",
-    "STATE OF PALESTINE",
-    "PALESTINIAN TERRITORY",
-    "PALESTINIAN TERRITORIES",
-    "PSE",
-    "PS",
+    "PALESTINE","STATE OF PALESTINE","PALESTINIAN TERRITORY","PALESTINIAN TERRITORIES","PSE","PS"
 }
 
-# Canonical header mapping (tolerant to your Excel headers)
 CANON = {
     "name": ["name","player","player_name"],
     "age": ["age"],
@@ -53,8 +46,6 @@ def _to_number(x):
 def _to_full_country(n):
     if pd.isna(n): return None
     s = str(n).strip()
-
-    # Convert alpha-3/alpha-2 codes to full names; otherwise keep as-is
     candidate = None
     if len(s) <= 3:
         c = pycountry.countries.get(alpha_3=s.upper())
@@ -64,8 +55,6 @@ def _to_full_country(n):
             if c: candidate = c.name
     if candidate is None:
         candidate = s
-
-    # Apply user-specified exclusion
     norm = re.sub(r"\s+"," ", candidate).strip().upper()
     if norm in EXCLUDED_NATIONS:
         return None
@@ -77,7 +66,6 @@ def load_df():
             "Name","Age","Position","Nation","Club","Overall","Potential",
             "Height_cm","Weight_kg","Value","Wage"
         ])
-
     df = pd.read_excel(DATA_FILE, engine="openpyxl")
     df.columns = [str(c).strip() for c in df.columns]
     cmap = _canon_map(df.columns)
@@ -100,14 +88,13 @@ def load_df():
     out["Height_cm"]  = df.get(cmap.get("height_cm"), pd.Series(dtype=object)).apply(_to_number)
     out["Weight_kg"]  = df.get(cmap.get("weight_kg"), pd.Series(dtype=object)).apply(_to_number)
 
-    # Drop excluded/empty nations
     out = out[~out["Nation"].isna()].reset_index(drop=True)
     return out
 
 DF = load_df()
 
 def positions_list():
-    vals = set(POSITIONS)
+    vals = set(BASE_POSITIONS)
     if "Position" in DF.columns:
         vals |= set(str(x).upper().strip() for x in DF["Position"].dropna().unique())
     return sorted(vals)
@@ -115,7 +102,6 @@ def positions_list():
 def nations_list():
     if "Nation" in DF.columns and not DF["Nation"].dropna().empty:
         return sorted(set(DF["Nation"].dropna().tolist()))
-    # Fallback to all pycountry names (minus exclusions)
     vals = []
     for c in pycountry.countries:
         nm = c.name
@@ -131,7 +117,6 @@ def clubs_list():
 def filter_players(positions, nations, clubs, min_overall, min_potential,
                    max_age, min_h, max_h, min_w, max_w, max_val, max_wage, query):
     df = DF.copy()
-
     if positions:   df = df[df["Position"].isin(positions)]
     if nations:     df = df[df["Nation"].isin(nations)]
     if clubs:       df = df[df["Club"].isin(clubs)]
@@ -144,14 +129,12 @@ def filter_players(positions, nations, clubs, min_overall, min_potential,
     if not math.isnan(max_w):         df = df[df["Weight_kg"] <= max_w]
     if not math.isnan(max_val):       df = df[df["Value"]     <= max_val]
     if not math.isnan(max_wage):      df = df[df["Wage"]      <= max_wage]
-
     if query:
         q = query.strip().lower()
         df = df[df.apply(lambda r: any(
             q in str(r.get(c, "")).lower()
             for c in ["Name","Club","Position","Nation"]
         ), axis=1)]
-
     cols = [c for c in ["Name","Age","Position","Nation","Club","Overall","Potential",
                         "Height_cm","Weight_kg","Value","Wage"] if c in df.columns]
     return df[cols].reset_index(drop=True)
@@ -161,7 +144,7 @@ def to_csv_bytes(df):
     df.to_csv(buf, index=False, encoding="utf-8")
     return buf.getvalue().encode("utf-8")
 
-# === UI ===
+# === UI (spread out, no accordion) ===
 THEME = gr.themes.Soft(primary_hue="blue")
 CSS = """
 #title { text-align:center; }
@@ -171,28 +154,37 @@ CSS = """
 
 with gr.Blocks(theme=THEME, css=CSS) as demo:
     gr.Markdown("<h1 id='title'>ProScout — Player Finder</h1>")
+
+    # ROW: filters (3 columns) + results (wide on the right)
     with gr.Row():
+        # Column 1: categorical filters + search
         with gr.Column(scale=1, elem_classes="card"):
-            pos = gr.CheckboxGroup(positions_list(), label="Positions", info="Pick one or more")
-            nat = gr.Dropdown(nations_list(), multiselect=True, label="Nations", filterable=True)
-            clu = gr.Dropdown(clubs_list(), multiselect=True, label="Clubs", filterable=True)
-            query = gr.Textbox(label="Search (Name / Club / Nation / Position)")
-
-            with gr.Accordion("Advanced filters", open=False):
-                min_ovr   = gr.Slider(0, 99, value=70, step=1, label="Min Overall")
-                min_pot   = gr.Slider(0, 99, value=70, step=1, label="Min Potential")
-                max_age   = gr.Slider(15, 45, value=30, step=1, label="Max Age")
-                min_h = gr.Slider(140, 210, value=140, step=1, label="Min Height (cm)")
-                max_h = gr.Slider(140, 210, value=210, step=1, label="Max Height (cm)")
-                min_w = gr.Slider(45, 120, value=45, step=1, label="Min Weight (kg)")
-                max_w = gr.Slider(45, 120, value=120, step=1, label="Max Weight (kg)")
-                max_val   = gr.Number(value=np.nan, label="Max Value (EUR)")
-                max_wage  = gr.Number(value=np.nan, label="Max Wage (EUR)")
+            pos = gr.CheckboxGroup(positions_list(), label="Positions", value=[], info="Pick one or more")
+            nat = gr.Dropdown(nations_list(), multiselect=True, label="Nations", value=[], filterable=True)
+            clu = gr.Dropdown(clubs_list(), multiselect=True, label="Clubs", value=[], filterable=True)
+            query = gr.Textbox(label="Search (Name / Club / Nation / Position)", placeholder="e.g., Maccabi, Brazil, CAM")
 
+        # Column 2: ratings & age
+        with gr.Column(scale=1, elem_classes="card"):
+            min_ovr = gr.Slider(0, 99, value=0, step=1, label="Min Overall")
+            min_pot = gr.Slider(0, 99, value=0, step=1, label="Min Potential")
+            max_age = gr.Slider(15, 45, value=45, step=1, label="Max Age")
+
+            gr.Markdown("")  # spacer
             btn = gr.Button("Search", variant="primary")
 
+        # Column 3: physical & financial
+        with gr.Column(scale=1, elem_classes="card"):
+            min_h = gr.Slider(140, 210, value=140, step=1, label="Min Height (cm)")
+            max_h = gr.Slider(140, 210, value=210, step=1, label="Max Height (cm)")
+            min_w = gr.Slider(45, 120, value=45, step=1, label="Min Weight (kg)")
+            max_w = gr.Slider(45, 120, value=120, step=1, label="Max Weight (kg)")
+            max_val = gr.Number(value=np.nan, label="Max Value (EUR)")
+            max_wage = gr.Number(value=np.nan, label="Max Wage (EUR)")
+
+        # Results panel
         with gr.Column(scale=2, elem_classes="card"):
-            results = gr.Dataframe(row_count=(10,"dynamic"), wrap=True, interactive=False, label="Results")
+            results = gr.Dataframe(row_count=(12,"dynamic"), wrap=True, interactive=False, label="Results")
             with gr.Row():
                 count_box = gr.Markdown("", elem_classes="stat")
                 avg_ovr_box = gr.Markdown("", elem_classes="stat")
@@ -209,6 +201,7 @@ with gr.Blocks(theme=THEME, css=CSS) as demo:
         csv_bytes = to_csv_bytes(df)
         return df, f"**Players:** {count}", f"**Avg OVR:** {avg_ovr}", f"**Avg Age:** {avg_age}", csv_bytes
 
+    # Show data immediately on load (no empty screen)
     demo.load(
         _run,
         inputs=[pos,nat,clu,min_ovr,min_pot,max_age,min_h,max_h,min_w,max_w,max_val,max_wage,query],