Create app.py

app.py

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+from fastapi import FastAPI, HTTPException, Query, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from sklearn.neighbors import KDTree
+import pandas as pd
+import numpy as np
+from geopy.distance import geodesic
+import logging
+
+app = FastAPI()
+
+# 允許所有來源的跨域請求（可以根據需要進行限制）
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # 可以根據需要限制來源
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 設置日誌
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+poi_data = None
+trees = {}
+
+# 構建KD-tree（不保存到磁碟）
+def build_kdtrees(poi_data):
+    trees = {}
+    for poi_type, group in poi_data.groupby('poi_type'):
+        coords = np.array(list(group['coordinates']))
+        tree = KDTree(coords, leaf_size=2)
+        trees[poi_type] = tree
+    return trees
+
+@app.post("/upload-poi")
+async def upload_poi(file: UploadFile = File(...)):
+    global poi_data, trees
+    try:
+        poi_data = pd.read_csv(file.file)
+        poi_data['coordinates'] = list(zip(poi_data.lat, poi_data.lng))
+        trees = build_kdtrees(poi_data)
+        return {"message": "POI data uploaded and KD-trees built successfully"}
+    except Exception as e:
+        logger.error(f"An error occurred while processing the uploaded POI data: {e}")
+        raise HTTPException(status_code=500, detail="An error occurred while processing the uploaded POI data")
+
+@app.get("/poi/nearest")
+def get_nearest_poi(lat: float, lng: float, poi_type: str = Query(...)):
+    global poi_data, trees
+    if poi_data is None or trees == {}:
+        raise HTTPException(status_code=400, detail="POI data not uploaded")
+
+    coords = np.array([[lat, lng]])
+
+    if poi_type == "all":
+        all_pois = []
+
+        # 遍歷所有KD-tree並找出最近的POI
+        for tree_poi_type, tree in trees.items():
+            dist, inds = tree.query(coords, k=10)
+            for i, distance in enumerate(dist[0]):
+                poi_candidate = poi_data[poi_data['poi_type'] == tree_poi_type].iloc[inds[0][i]]
+                candidate_distance = geodesic((lat, lng), (poi_candidate["lat"], poi_candidate["lng"])).meters
+                all_pois.append({
+                    "name": poi_candidate["name"],
+                    "poi_type": tree_poi_type,
+                    "distance": round(candidate_distance, 2),
+                    "latitude": poi_candidate["lat"],
+                    "longitude": poi_candidate["lng"]
+                })
+
+        # 排序所有POI並取前10個
+        all_pois = sorted(all_pois, key=lambda x: x["distance"])[:10]
+
+        if not all_pois:
+            raise HTTPException(status_code=404, detail="No POI found")
+
+        return all_pois
+    else:
+        if poi_type not in trees:
+            raise HTTPException(status_code=404, detail="Model type not found")
+
+        tree = trees[poi_type]
+        dist, inds = tree.query(coords, k=10)
+        nearest_pois = []
+
+        for i, distance in enumerate(dist[0]):
+            nearest_poi = poi_data[poi_data['poi_type'] == poi_type].iloc[inds[0][i]]
+            distance_m = geodesic((lat, lng), (nearest_poi["lat"], nearest_poi["lng"])).meters
+            nearest_pois.append({
+                "name": nearest_poi["name"],
+                "poi_type": poi_type,
+                "distance": round(distance_m, 2),
+                "latitude": nearest_poi["lat"],
+                "longitude": nearest_poi["lng"]
+            })
+
+        return nearest_pois
+
+@app.post("/clear-kdtrees")
+def clear_kdtrees():
+    global poi_data, trees
+    poi_data = None
+    trees = {}
+    return {"message": "KD-trees and POI data cleared successfully"}
+
+# 運行應用
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)