Update app.py

app.py

@@ -5,8 +5,6 @@ from transformers import CLIPProcessor, CLIPModel
 import pygeohash as pgh
 import plotly.graph_objects as go
 import torch.nn as nn
-import reverse_geocoder as rg
-import pycountry  # for full country names
 
 EXPORT_DIR = "."
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
@@ -54,16 +52,6 @@ def haversine(lat1, lon1, lat2, lon2):
     a = np.sin(dphi/2)**2 + np.cos(phi1)*np.cos(phi2)*np.sin(dlambda/2)**2
     return 2*R*np.arctan2(np.sqrt(a), np.sqrt(1-a))
 
-# ---------------- Country lookup ----------------
-def latlon_to_country(lat, lon):
-    try:
-        res = rg.search((lat, lon))[0]
-        cc = res['cc']
-        country = pycountry.countries.get(alpha_2=cc)
-        return country.name if country else cc, cc
-    except:
-        return "Unknown", "??"
-
 # ---------------- Prediction + Map ----------------
 def predict_geohash_map(img: Image.Image, true_lat=None, true_lon=None, top_k=5):
     c_in = clip_processor(images=img, return_tensors="pt").to(DEVICE)
@@ -78,8 +66,8 @@ def predict_geohash_map(img: Image.Image, true_lat=None, true_lon=None, top_k=5)
     lats, lons = [], []
 
     # Markdown table header
-    preds_text = ["| Rank | Country | Code | Latitude | Longitude | Distance |",
-                  "|------|---------|------|----------|-----------|----------|"]
+    preds_text = ["| Rank | Latitude | Longitude | Distance |",
+                  "|------|----------|-----------|----------|"]
 
     for rank, i in enumerate(topk_idx, 1):
         geoh = id2geoh[i]
@@ -87,8 +75,6 @@ def predict_geohash_map(img: Image.Image, true_lat=None, true_lon=None, top_k=5)
         lat_pred = lat_base + out_offset_np[0] * cell_lat
         lon_pred = lon_base + out_offset_np[1] * cell_lon
 
-        country_name, country_code = latlon_to_country(lat_pred, lon_pred)
-
         dist_str = ""
         if true_lat is not None and true_lon is not None:
             dist = haversine(true_lat, true_lon, lat_pred, lon_pred)
@@ -100,7 +86,7 @@ def predict_geohash_map(img: Image.Image, true_lat=None, true_lon=None, top_k=5)
                 dist_str = f"🔴 {dist:.1f} km"
 
         preds_text.append(
-            f"| {rank} | {country_name} | {country_code} | {lat_pred:.5f} | {lon_pred:.5f} | {dist_str} |"
+            f"| {rank} | {lat_pred:.5f} | {lon_pred:.5f} | {dist_str} |"
         )
 
         lats.append(lat_pred)