Upload app.py

app.py

@@ -1,4 +1,4 @@
-### app_refactored_openmeteo_strings.py ###
+### app_fixed.py ###
 
 import gradio as gr
 import pandas as pd
@@ -37,10 +37,45 @@ DEFAULT_LAT, DEFAULT_LON = 49.6116, 6.1319
 # --- UTILS ---
 
 def compute_bbox(lat, lon, dist_km):
-	origin = geopy.Point(lat, lon)
-	dest_sw = distance.distance(kilometers=dist_km).destination(origin, bearing=225)
-	dest_ne = distance.distance(kilometers=dist_km).destination(origin, bearing=45)
-	return f"{dest_sw.latitude},{dest_sw.longitude},{dest_ne.latitude},{dest_ne.longitude}"
+	"""Compute bounding box more reliably for any location."""
+	# Convert km to degrees (rough approximation)
+	# At equator: 1 degree ≈ 111 km
+	lat_delta = dist_km / 111.0
+	lon_delta = dist_km / (111.0 * np.cos(np.radians(lat)))
+	
+	south = lat - lat_delta
+	north = lat + lat_delta
+	west = lon - lon_delta
+	east = lon + lon_delta
+	
+	# Ensure longitude wraps properly
+	if west < -180:
+		west += 360
+	if east > 180:
+		east -= 360
+	
+	# Ensure latitude stays in valid range
+	south = max(south, -90)
+	north = min(north, 90)
+	
+	return f"{south},{west},{north},{east}"
+
+
+def get_elevation_from_srtm(lat, lon):
+	"""Get elevation from SRTM if within coverage area."""
+	if lat is None or lon is None:
+		return None
+	
+	# SRTM coverage: 60°N to 56°S
+	if -56 <= lat <= 60:
+		try:
+			alt = elevation_data.get_elevation(lat, lon)
+			if alt is not None and alt > 0:
+				return alt
+		except Exception as ex:
+			print(f"SRTM error for {lat},{lon}: {ex}")
+	
+	return None
 
 
 def get_peaks_from_overpass(lat, lon, dist_km):
@@ -63,28 +98,56 @@ def get_peaks_from_overpass(lat, lon, dist_km):
 		return pd.DataFrame()
 
 	peaks = {"name": [], "latitude": [], "longitude": [], "altitude": []}
+	skipped = 0
+	processed = 0
+	max_peaks = 100  # Limit processing to avoid slowdowns
+	
 	for e in data.get("elements", []):
+		# Stop if we've processed enough peaks
+		if processed >= max_peaks:
+			break
+			
 		lat_e, lon_e = e.get("lat"), e.get("lon")
+		
+		# Skip elements without valid coordinates
+		if lat_e is None or lon_e is None:
+			skipped += 1
+			continue
+			
 		tags = e.get("tags", {})
+		alt = None
+		
+		# Strategy 1: Try to get elevation from OSM tag first
+		ele = tags.get("ele")
+		if ele and str(ele).replace(".", "").replace("-", "").isnumeric():
+			alt = float(ele)
+		
+		# Strategy 2: If no OSM elevation, try SRTM as fallback
+		if alt is None or alt <= 10:
+			alt = get_elevation_from_srtm(lat_e, lon_e)
+		
+		# Skip peaks if both strategies failed to produce valid elevation
+		if alt is None or alt <= 10:
+			skipped += 1
+			continue
+		
 		peaks["latitude"].append(lat_e)
 		peaks["longitude"].append(lon_e)
 		peaks["name"].append(tags.get("name", "Unnamed Peak/Hill"))
-
-		ele = tags.get("ele")
-		if ele and str(ele).replace(".", "").replace("-", "").isnumeric():
-			peaks["altitude"].append(float(ele))
-		else:
-			try:
-				alt = elevation_data.get_elevation(lat_e, lon_e)
-				peaks["altitude"].append(alt if alt is not None else 0)
-			except Exception:
-				peaks["altitude"].append(0)
+		peaks["altitude"].append(alt)
+		processed += 1
+	
+	if skipped > 0:
+		print(f"Skipped {skipped} peaks without complete data (coordinates or elevation)")
+	if processed >= max_peaks:
+		print(f"Reached limit of {max_peaks} peaks processed")
 
 	if not peaks["latitude"]:
 		return pd.DataFrame()
 
 	df = pd.DataFrame(peaks)
 	df["altitude"] = df["altitude"].round(0).astype(int)
+	
 	df["distance_m"] = df.apply(
 		lambda r: distance.distance((r["latitude"], r["longitude"]), (lat, lon)).m, axis=1
 	)
@@ -115,21 +178,15 @@ def get_weather_for_peaks_iteratively(df_peaks, min_snow_cm, max_results=20, max
 		}
 
 		try:
-			# FIX: Get the list of responses and access the first one
 			responses = openmeteo.weather_api(url, params=params)
-			if not responses:  # Check if the list is empty
+			if not responses:
 				continue
-			response = responses[0]  # This is the critical fix
+			response = responses[0]
 			
-			# Now proceed with your existing processing
 			hourly = response.Hourly()
 			if hourly is None:
 				continue
 
-			#times = hourly.Time()
-			#now_utc = datetime.now(timezone.utc)
-			#times_utc = datetime.fromtimestamp(times, tz=timezone.utc)
-			#idx = int(np.argmin([abs((t - now_utc).total_seconds()) for t in times_utc]))
 			idx = 0
 
 			temp_c = float(hourly.Variables(0).ValuesAsNumpy()[idx])
@@ -149,7 +206,7 @@ def get_weather_for_peaks_iteratively(df_peaks, min_snow_cm, max_results=20, max
 				})
 
 		except Exception as e:
-			print(f"Error fetching weather for {row['name']}: {e}")
+			print(f"Error fetching weather for {row['name']} at {row['latitude']},{row['longitude']}: {e}")
 
 		requests_made += 1
 
@@ -166,9 +223,13 @@ def format_weather_data(df):
 		code = str(int(row["weather_code"]))
 		tod = "day" if row["is_day"] == 1 else "night"
 		info = icons.get(code, {}).get(tod, {})
-		return ICON_URL + info.get("icon", ""), info.get("description", "Unknown")
+		icon_filename = info.get("icon", "")
+		description = info.get("description", "Unknown")
+		return ICON_URL + icon_filename, description, icon_filename
 
-	df[["weather_icon", "weather_desc"]] = df.apply(icon_mapper, axis=1, result_type="expand")
+	df[["weather_icon_url", "weather_desc", "weather_icon_name"]] = df.apply(
+		icon_mapper, axis=1, result_type="expand"
+	)
 	df["distance_km"] = (df["distance_m"] / 1000).round(1)
 	df["temp_c_str"] = df["temp_c"].round(0).astype(int).astype(str) + "°C"
 	return df
@@ -238,7 +299,7 @@ def create_map_with_center(lat, lon):
 			lon=[lon],
 			mode="markers",
 			marker=dict(size=24, color="white", opacity=0.8),
-			hoverinfo=None,
+			hoverinfo="skip",
 		)
 	)
 	fig.add_trace(
@@ -246,7 +307,7 @@ def create_map_with_center(lat, lon):
 			lat=[lat],
 			lon=[lon],
 			mode="markers",
-			marker=dict(size=12, color="white", symbol="landmark"),
+			marker=dict(size=12, color="red"),
 			text=["Search Center"],
 			hoverinfo="text",
 		)
@@ -262,23 +323,28 @@ def create_map_with_center(lat, lon):
 
 def create_map_with_results(lat, lon, df_final):
 	fig = go.Figure()
+	
+	# Add white halos for peaks
 	fig.add_trace(
 		go.Scattermap(
 			lat=df_final["latitude"],
 			lon=df_final["longitude"],
 			mode="markers",
 			marker=dict(size=24, color="white", opacity=0.8),
-			hoverinfo=None,
+			hoverinfo="skip",
 		)
 	)
+	
+	# Add peak markers with weather info in hover (no HTML icons)
 	fig.add_trace(
 		go.Scattermap(
 			lat=df_final["latitude"],
 			lon=df_final["longitude"],
 			mode="markers",
-			marker=dict(size=12, color="white", symbol="mountain"),
+			marker=dict(size=12, color="blue"),
 			customdata=df_final[
-				["name", "altitude", "distance_km", "snow_depth_cm", "weather_desc", "temp_c_str"]
+				["name", "altitude", "distance_km", "snow_depth_cm", "weather_desc", 
+				 "temp_c_str"]
 			],
 			hovertemplate=(
 				"<b>%{customdata[0]}</b><br>"
@@ -290,13 +356,15 @@ def create_map_with_results(lat, lon, df_final):
 			),
 		)
 	)
+	
+	# Add search center with halo
 	fig.add_trace(
 		go.Scattermap(
 			lat=[lat],
 			lon=[lon],
 			mode="markers",
 			marker=dict(size=24, color="white", opacity=0.8),
-			hoverinfo=None,
+			hoverinfo="skip",
 		)
 	)
 	fig.add_trace(
@@ -304,11 +372,12 @@ def create_map_with_results(lat, lon, df_final):
 			lat=[lat],
 			lon=[lon],
 			mode="markers",
-			marker=dict(size=12, color="white", symbol="landmark"),
+			marker=dict(size=12, color="red"),
 			text=["Search Center"],
 			hoverinfo="text",
 		)
 	)
+	
 	fig.update_layout(
 		map=dict(style="open-street-map", center={"lat": lat, "lon": lon}, zoom=9),
 		margin={"r": 0, "t": 40, "l": 0, "b": 0},