Update app.py

app.py

@@ -1,200 +1,200 @@
-import requests
-import sys
-import math
-
-import numpy as np
-from shapely.geometry import Point, MultiPoint
-from sklearn.cluster import DBSCAN
-import geopandas as gpd
-import gradio as gr
-import geopy.distance
-import folium
-
-
-
-def load_countries():
-    countries_dict = {}
-    countries = gpd.read_file("https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_50m_admin_0_countries.geojson")
-    countries = countries[['name', 'geometry']]
-    for index, row in countries.iterrows():
-        centroid = row['geometry'].centroid
-        # store as (lat, lon)
-        countries_dict[row['name']] = (centroid.y, centroid.x)
-
-    return countries_dict
-
-def find_closest_country(coordinate, countries):
-    closest_country = None
-    smallest_distance = None
-
-    for country in countries:
-        distance = geopy.distance.geodesic(coordinate, countries[country]).km
-        if smallest_distance is None or distance < smallest_distance:
-            smallest_distance = distance
-            closest_country = country
-
-    return closest_country, smallest_distance
-
-def track_balloon(index):
-    index = int(index)
-    global countries
-
-    points = []
-    annotations = []
-    for i in range(24):
-        url = f"https://a.windbornesystems.com/treasure/{i:02}.json"
-        try:
-            response = requests.get(url, timeout=8)
-            if response.status_code != 200:
-                print("failed to fetch from url")
-                continue
-            data = response.json()
-        except Exception as e:
-            print("Failed to retrieve data.", e)
-            continue
-        balloon = data[index - 1]
-
-        lat, lon = balloon[:2]
-        points.append((lat, lon))
-
-        country, distance_km = find_closest_country((lat, lon), countries)
-        description = f"Hour offset: {i} — Closest country: {country} ({distance_km:.1f} km)"
-        annotations.append((lat, lon, description))
-
-
-    m = folium.Map(location=points[-1])
-    folium.PolyLine(locations=points, weight=3, opacity=1).add_to(m)
-    for idx, (lat, lon, desc) in enumerate(annotations):
-        popup_text = desc
-        if idx == 0:
-            folium.CircleMarker(location=(lat, lon),
-                                radius=10,
-                                popup=popup_text,
-                                tooltip="Last known location",
-                                fill=True).add_to(m)
-        else:
-            folium.Marker(location=(lat, lon),
-                          popup=popup_text,
-                          tooltip=f"Hour {idx}").add_to(m)
-
-    return m._repr_html_()
-
-def cluster_balloons(hour, eps_km, samples, show_hulls):
-    earth_radius = 6371
-
-    points = []
-    url = f"https://a.windbornesystems.com/treasure/{hour:02}.json"
-    r = requests.get(url, timeout=8)
-    if r.status_code != 200:
-        return f"Error: Problem querying {url}"
-    data = r.json()
-
-    for balloon in data:
-        lat, lon = balloon[:2]
-        points.append((lat, lon))
-
-    if not points:
-        return "Error: no points found"
-
-    coords = np.array(points)
-    coords_rad = np.radians(coords)
-    eps_rad = eps_km /earth_radius
-
-    labels = DBSCAN(eps=eps_rad, min_samples=samples, metric='haversine').fit_predict(coords_rad)
-    # print(labels)
-    unique_labels = sorted(set(labels))
-    # print(unique_labels)
-    n_clusters = len(unique_labels)
-    if -1 in unique_labels:
-        n_clusters -=1
-
-    base_colors = [
-        "#000ddd", "#929203", "#2ca02c", "#d62728", "#5b3181", "#5e403a",
-        "#e377c2", "#796060", "#ffff00", "#00e5ff", "#3a657c", "#2b4217"
-    ]
-
-    def color_for_label(label):
-        if label == -1:
-            return "#000000"
-        return base_colors[label % len(base_colors)]
-
-    map = folium.Map(location=coords.mean(axis=0).tolist())
-    for (lat, lon), label in zip(coords, labels):
-        folium.CircleMarker(
-            location=(lat, lon),
-            radius=3,
-            color=color_for_label(label),
-            fill=True,
-            fill_opacity=1,
-            popup=f"Cluster: {label}" if label > -1 else "Noise"
-        ).add_to(map)
-
-    # draw border around clusters
-    if show_hulls:
-        for label in unique_labels:
-            if label == -1:
-                continue
-            mask = (labels == label)
-            curr_cluster = coords[mask]
-            if len(curr_cluster) == 0:
-                continue
-
-            hull_points = []
-            for point in curr_cluster:
-                hull_points.append(Point(point[1], point[0])) # change lattitude, longitude order hull calculation
-            mp = MultiPoint(hull_points)
-
-            hull = mp.convex_hull
-            hull_coords = []
-            for lon, lat in hull.exterior.coords:
-                hull_coords.append((lat, lon)) # change back for folium map again
-            l_color = color_for_label(label)
-            folium.Polygon(
-                locations=hull_coords,
-                color=l_color,
-                weight=2,
-                fill=True,
-                fill_color=l_color,
-                fill_opacity=0.2,
-                popup=f"Cluster {label}, size: {len(curr_cluster)}"
-            ).add_to(map)
-
-    title_for_html = f"""
-         <div style="position: fixed; 
-                     top: 10px; left: 10px; width: auto; height: auto; 
-                     z-index: 9999; font-size: 15px;
-                     background-color: white; padding: 8px; border: 2px solid #000;">
-            <b>Clusters (data from {hour} hours ago)</b><br/>
-            DBSCAN eps = {eps_km} km <br/>
-            clusters found = {n_clusters}, total points = {len(coords)}
-         </div>
-         """
-    map.get_root().html.add_child(folium.Element(title_for_html))
-
-    return map._repr_html_()
-
-countries = load_countries()
-
-with gr.Blocks() as demo:
-    gr.Markdown("Cluster Visualization + Balloon Tracker")
-    with gr.Tabs():
-        with gr.TabItem("Cluster balloons (all balloons)"):
-                with gr.Row():
-                    hours_slider = gr.Slider(label="Hours ago", minimum=0, maximum=23, step=1, value=0)
-                    num_samples = gr.Number(label="DBSCAN min_samples", value=5, precision=0)
-                    eps_km = gr.Number(label="DBSCAN eps (km)", value=750, precision=1)
-                with gr.Row():
-                    hull_checkbox = gr.Checkbox(label="Draw hull around clusters", value=True)
-                    cluster_btn = gr.Button("Show Clusters")
-                clusters_map_html = gr.HTML()
-                cluster_btn.click(fn=cluster_balloons, inputs=[hours_slider, eps_km, num_samples, hull_checkbox], outputs=[clusters_map_html])
-        with gr.TabItem("Track balloon"):
-            with gr.Row():
-                balloon = gr.Number(label="Balloon index", minimum=1, maximum=1000, value=1)
-                track_btn = gr.Button("Track")
-            map_html = gr.HTML()
-            track_btn.click(fn=track_balloon, inputs=[balloon], outputs=[map_html])
-
-        
-
-demo.launch(share=True)
+import requests
+import sys
+import math
+
+import numpy as np
+from shapely.geometry import Point, MultiPoint
+from sklearn.cluster import DBSCAN
+import geopandas as gpd
+import gradio as gr
+import geopy.distance
+import folium
+
+
+
+def load_countries():
+    countries_dict = {}
+    countries = gpd.read_file("https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_50m_admin_0_countries.geojson")
+    countries = countries[['name', 'geometry']]
+    for index, row in countries.iterrows():
+        centroid = row['geometry'].centroid
+        # store as (lat, lon)
+        countries_dict[row['name']] = (centroid.y, centroid.x)
+
+    return countries_dict
+
+def find_closest_country(coordinate, countries):
+    closest_country = None
+    smallest_distance = None
+
+    for country in countries:
+        distance = geopy.distance.geodesic(coordinate, countries[country]).km
+        if smallest_distance is None or distance < smallest_distance:
+            smallest_distance = distance
+            closest_country = country
+
+    return closest_country, smallest_distance
+
+def track_balloon(index):
+    index = int(index)
+    global countries
+
+    points = []
+    annotations = []
+    for i in range(24):
+        url = f"https://a.windbornesystems.com/treasure/{i:02}.json"
+        try:
+            response = requests.get(url, timeout=8)
+            if response.status_code != 200:
+                print("failed to fetch from url")
+                continue
+            data = response.json()
+        except Exception as e:
+            print("Failed to retrieve data.", e)
+            continue
+        balloon = data[index - 1]
+
+        lat, lon = balloon[:2]
+        points.append((lat, lon))
+
+        country, distance_km = find_closest_country((lat, lon), countries)
+        description = f"Hour offset: {i} — Closest country: {country} ({distance_km:.1f} km)"
+        annotations.append((lat, lon, description))
+
+
+    m = folium.Map(location=points[-1])
+    folium.PolyLine(locations=points, weight=3, opacity=1).add_to(m)
+    for idx, (lat, lon, desc) in enumerate(annotations):
+        popup_text = desc
+        if idx == 0:
+            folium.CircleMarker(location=(lat, lon),
+                                radius=10,
+                                popup=popup_text,
+                                tooltip="Last known location",
+                                fill=True).add_to(m)
+        else:
+            folium.Marker(location=(lat, lon),
+                          popup=popup_text,
+                          tooltip=f"Hour {idx}").add_to(m)
+
+    return m._repr_html_()
+
+def cluster_balloons(hour, eps_km, samples, show_hulls):
+    earth_radius = 6371
+
+    points = []
+    url = f"https://a.windbornesystems.com/treasure/{hour:02}.json"
+    r = requests.get(url, timeout=8)
+    if r.status_code != 200:
+        return f"Error: Problem querying {url}"
+    data = r.json()
+
+    for balloon in data:
+        lat, lon = balloon[:2]
+        points.append((lat, lon))
+
+    if not points:
+        return "Error: no points found"
+
+    coords = np.array(points)
+    coords_rad = np.radians(coords)
+    eps_rad = eps_km /earth_radius
+
+    labels = DBSCAN(eps=eps_rad, min_samples=samples, metric='haversine').fit_predict(coords_rad)
+    # print(labels)
+    unique_labels = sorted(set(labels))
+    # print(unique_labels)
+    n_clusters = len(unique_labels)
+    if -1 in unique_labels:
+        n_clusters -=1
+
+    base_colors = [
+        "#000ddd", "#929203", "#2ca02c", "#d62728", "#5b3181", "#5e403a",
+        "#e377c2", "#796060", "#ffff00", "#00e5ff", "#3a657c", "#2b4217"
+    ]
+
+    def color_for_label(label):
+        if label == -1:
+            return "#000000"
+        return base_colors[label % len(base_colors)]
+
+    map = folium.Map(location=coords.mean(axis=0).tolist(), zoom_start=3)
+    for (lat, lon), label in zip(coords, labels):
+        folium.CircleMarker(
+            location=(lat, lon),
+            radius=3,
+            color=color_for_label(label),
+            fill=True,
+            fill_opacity=1,
+            popup=f"Cluster: {label}" if label > -1 else "Noise"
+        ).add_to(map)
+
+    # draw border around clusters
+    if show_hulls:
+        for label in unique_labels:
+            if label == -1:
+                continue
+            mask = (labels == label)
+            curr_cluster = coords[mask]
+            if len(curr_cluster) == 0:
+                continue
+
+            hull_points = []
+            for point in curr_cluster:
+                hull_points.append(Point(point[1], point[0])) # change lattitude, longitude order hull calculation
+            mp = MultiPoint(hull_points)
+
+            hull = mp.convex_hull
+            hull_coords = []
+            for lon, lat in hull.exterior.coords:
+                hull_coords.append((lat, lon)) # change back for folium map again
+            l_color = color_for_label(label)
+            folium.Polygon(
+                locations=hull_coords,
+                color=l_color,
+                weight=2,
+                fill=True,
+                fill_color=l_color,
+                fill_opacity=0.2,
+                popup=f"Cluster {label}, size: {len(curr_cluster)}"
+            ).add_to(map)
+
+    title_for_html = f"""
+         <div style="position: fixed; 
+                     top: 10px; left: 10px; width: auto; height: auto; 
+                     z-index: 9999; font-size: 15px;
+                     background-color: white; padding: 8px; border: 2px solid #000;">
+            <b>Clusters (data from {hour} hours ago)</b><br/>
+            DBSCAN eps = {eps_km} km <br/>
+            clusters found = {n_clusters}, total points = {len(coords)}
+         </div>
+         """
+    map.get_root().html.add_child(folium.Element(title_for_html))
+
+    return map._repr_html_()
+
+countries = load_countries()
+
+with gr.Blocks() as demo:
+    gr.Markdown("Cluster Visualization + Balloon Tracker")
+    with gr.Tabs():
+        with gr.TabItem("Cluster balloons (all balloons)"):
+                with gr.Row():
+                    hours_slider = gr.Slider(label="Hours ago", minimum=0, maximum=23, step=1, value=0)
+                    num_samples = gr.Number(label="DBSCAN min_samples", value=5, precision=0)
+                    eps_km = gr.Number(label="DBSCAN eps (km)", value=750, precision=1)
+                with gr.Row():
+                    hull_checkbox = gr.Checkbox(label="Draw hull around clusters", value=True)
+                    cluster_btn = gr.Button("Show Clusters")
+                clusters_map_html = gr.HTML()
+                cluster_btn.click(fn=cluster_balloons, inputs=[hours_slider, eps_km, num_samples, hull_checkbox], outputs=[clusters_map_html])
+        with gr.TabItem("Track balloon"):
+            with gr.Row():
+                balloon = gr.Number(label="Balloon index", minimum=1, maximum=1000, value=1)
+                track_btn = gr.Button("Track")
+            map_html = gr.HTML()
+            track_btn.click(fn=track_balloon, inputs=[balloon], outputs=[map_html])
+
+        
+
+demo.launch(share=True)