Update app.py

app.py

@@ -1,18 +1,15 @@
 import csv
 import os
 import gradio as gr
+import pandas as pd
+import plotly.express as px
 
 CSV_PATH = "zones.csv"
 
 def load_zones(csv_path: str = CSV_PATH):
-    """
-    Loads zones from a CSV with columns:
-    zone_id,zone_name,latitude,longitude,venue_density,foot_traffic_index,
-    nightlife_score,daytime_score,primary_vibe,notes
-    """
     if not os.path.exists(csv_path):
         raise FileNotFoundError(
-            f"Can't find {csv_path}. Put zones.csv in the same folder as app.py (Files tab)."
+            f"Can't find {csv_path}. Add zones.csv in the Space Files tab (same folder as app.py)."
         )
 
     zones = []
@@ -32,28 +29,25 @@ def load_zones(csv_path: str = CSV_PATH):
         }
         missing = required - set(reader.fieldnames or [])
         if missing:
-            raise ValueError(
-                "zones.csv is missing columns: " + ", ".join(sorted(missing))
-            )
+            raise ValueError("zones.csv is missing columns: " + ", ".join(sorted(missing)))
 
-        for row in reader:
-            # Convert numeric fields safely
-            def to_float(key, default=0.0):
-                try:
-                    return float(row.get(key, default))
-                except (TypeError, ValueError):
-                    return default
+        def to_float(val, default=0.0):
+            try:
+                return float(val)
+            except (TypeError, ValueError):
+                return default
 
+        for row in reader:
             zones.append(
                 {
-                    "zone_id": row["zone_id"].strip(),
-                    "zone_name": row["zone_name"].strip(),
-                    "lat": to_float("latitude"),
-                    "lon": to_float("longitude"),
-                    "venue_density": to_float("venue_density"),
-                    "foot_traffic": to_float("foot_traffic_index"),
-                    "nightlife": to_float("nightlife_score"),
-                    "daytime": to_float("daytime_score"),
+                    "zone_id": (row.get("zone_id") or "").strip(),
+                    "zone_name": (row.get("zone_name") or "").strip(),
+                    "lat": to_float(row.get("latitude")),
+                    "lon": to_float(row.get("longitude")),
+                    "venue_density": to_float(row.get("venue_density")),
+                    "foot_traffic": to_float(row.get("foot_traffic_index")),
+                    "nightlife": to_float(row.get("nightlife_score")),
+                    "daytime": to_float(row.get("daytime_score")),
                     "vibe": (row.get("primary_vibe") or "").strip(),
                     "notes": (row.get("notes") or "").strip(),
                 }
@@ -61,12 +55,6 @@ def load_zones(csv_path: str = CSV_PATH):
     return zones
 
 def score_zone(zone, time_of_day: str) -> float:
-    """
-    Simple, explainable scoring:
-      - foot_traffic is the main "mobility" signal
-      - time_of_day picks nightlife/daytime emphasis
-      - venue_density boosts places that have lots of options nearby
-    """
     ft = zone["foot_traffic"]
     vd = zone["venue_density"]
     nl = zone["nightlife"]
@@ -75,34 +63,52 @@ def score_zone(zone, time_of_day: str) -> float:
     if time_of_day == "Tonight":
         time_factor = nl
     elif time_of_day == "Weekend":
-        time_factor = 0.6 * nl + 0.4 * dt  # weekends are mixed
-    else:  # "Now"
+        time_factor = 0.6 * nl + 0.4 * dt
+    else:  # Now
         time_factor = 0.5 * nl + 0.5 * dt
 
-    # Weighted blend (kept simple and stable)
     return (0.55 * ft) + (0.30 * time_factor) + (0.15 * vd)
 
-def explain_zone(zone, time_of_day: str) -> str:
-    if time_of_day == "Tonight":
-        angle = "nightlife score"
-    elif time_of_day == "Weekend":
-        angle = "weekend mix (night + day)"
-    else:
-        angle = "balanced (day + night)"
-
-    return (
-        f"Why: mobility/foot-traffic + {angle} + venue density. "
-        f"{zone['notes']}".strip()
+def build_map(df: pd.DataFrame, time_of_day: str):
+    if df.empty:
+        return None
+
+    # Center roughly around Sydney
+    center_lat = df["lat"].mean()
+    center_lon = df["lon"].mean()
+
+    fig = px.scatter_mapbox(
+        df,
+        lat="lat",
+        lon="lon",
+        hover_name="zone_name",
+        hover_data={
+            "score": ":.2f",
+            "vibe": True,
+            "lat": False,
+            "lon": False,
+        },
+        size="score",
+        size_max=18,
+        zoom=10,
+        center={"lat": center_lat, "lon": center_lon},
+        title=f"Hot Zones Map ({time_of_day})",
+    )
+    fig.update_layout(
+        mapbox_style="open-street-map",
+        margin={"l": 0, "r": 0, "t": 40, "b": 0},
+        height=520,
     )
+    return fig
 
 def find_hot_places(time_of_day: str, top_k: int):
     try:
         zones = load_zones(CSV_PATH)
     except Exception as e:
-        return f"⚠️ {e}"
+        return f"⚠️ {e}", None
 
     if not zones:
-        return "No zones found in zones.csv."
+        return "No zones found in zones.csv.", None
 
     ranked = []
     for z in zones:
@@ -112,16 +118,30 @@ def find_hot_places(time_of_day: str, top_k: int):
     ranked.sort(key=lambda x: x[0], reverse=True)
     ranked = ranked[: max(1, int(top_k))]
 
+    rows = []
     lines = []
     for i, (s, z) in enumerate(ranked, start=1):
+        rows.append(
+            {
+                "rank": i,
+                "zone_name": z["zone_name"],
+                "lat": z["lat"],
+                "lon": z["lon"],
+                "score": s,
+                "vibe": z["vibe"],
+                "notes": z["notes"],
+            }
+        )
+
         lines.append(
             f"{i}. 📍 {z['zone_name']} (score: {s:.2f})\n"
             f"Vibe: {z['vibe']}\n"
-            f"{explain_zone(z, time_of_day)}\n"
-            f"Coords: {z['lat']:.4f}, {z['lon']:.4f}"
+            f"Why: {z['notes']}"
         )
 
-    return "\n\n---\n\n".join(lines)
+    df = pd.DataFrame(rows)
+    fig = build_map(df, time_of_day)
+    return "\n\n---\n\n".join(lines), fig
 
 with gr.Blocks() as demo:
     gr.Markdown(
@@ -134,16 +154,23 @@ with gr.Blocks() as demo:
         time_input = gr.Dropdown(
             choices=["Now", "Tonight", "Weekend"],
             value="Now",
-            label="When are you going out?"
+            label="When are you going out?",
         )
         top_k = gr.Slider(
-            minimum=1, maximum=10, value=4, step=1,
-            label="How many zones to show?"
+            minimum=1,
+            maximum=12,
+            value=6,
+            step=1,
+            label="How many zones to show?",
         )
 
-    output = gr.Textbox(label="Hot Zones", lines=16)
-
     btn = gr.Button("Find hot places")
-    btn.click(fn=find_hot_places, inputs=[time_input, top_k], outputs=output)
+
+    # On desktop this shows side-by-side; on phone it stacks nicely.
+    with gr.Row():
+        output_text = gr.Textbox(label="Ranked Zones", lines=16)
+        output_map = gr.Plot(label="Map")
+
+    btn.click(fn=find_hot_places, inputs=[time_input, top_k], outputs=[output_text, output_map])
 
 demo.launch()