Update app.py

app.py

@@ -54,26 +54,27 @@ def query_flight_data(geo_df, question):
 
     table_data = {
         "icao24": geo_df["icao24"].astype(str).iloc[:100].tolist(),
-        "callsign": geo_df["callsign"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:].tolist(),
-        "origin_country": geo_df["origin_country"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:].tolist(),
-        "time_position": geo_df["time_position"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "last_contact": geo_df["last_contact"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "longitude": geo_df["longitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "latitude": geo_df["latitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "baro_altitude": geo_df["baro_altitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "on_ground": geo_df["on_ground"].astype(str).iloc[:].tolist(),  # Assuming on_ground is boolean or categorical
-        "velocity": geo_df["velocity"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "true_track": geo_df["true_track"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "vertical_rate": geo_df["vertical_rate"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "sensors": geo_df["sensors"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:].tolist(), # Assuming sensors can be None
-        "geo_altitude": geo_df["geo_altitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "squawk": geo_df["squawk"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:].tolist(), # Assuming squawk can be None
-        "spi": geo_df["spi"].astype(str).iloc[:].tolist(),  # Assuming spi is boolean or categorical
-        "position_source": geo_df["position_source"].astype(str).iloc[:].tolist(),  # Assuming position_source is categorical
-        "time": geo_df["time"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:].tolist(),
-        "geometry": geo_df["geometry"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:].tolist() # Assuming geometry can be None
+        "callsign": geo_df["callsign"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:100].tolist(),
+        "origin_country": geo_df["origin_country"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:100].tolist(),
+        "time_position": geo_df["time_position"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "last_contact": geo_df["last_contact"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "longitude": geo_df["longitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "latitude": geo_df["latitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "baro_altitude": geo_df["baro_altitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "on_ground": geo_df["on_ground"].astype(str).iloc[:100].tolist(),  # Assuming on_ground is boolean or categorical
+        "velocity": geo_df["velocity"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "true_track": geo_df["true_track"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "vertical_rate": geo_df["vertical_rate"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "sensors": geo_df["sensors"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:100].tolist(), # Assuming sensors can be None
+        "geo_altitude": geo_df["geo_altitude"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "squawk": geo_df["squawk"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:100].tolist(), # Assuming squawk can be None
+        "spi": geo_df["spi"].astype(str).iloc[:100].tolist(),  # Assuming spi is boolean or categorical
+        "position_source": geo_df["position_source"].astype(str).iloc[:100].tolist(),  # Assuming position_source is categorical
+        "time": geo_df["time"].astype(str).replace({np.nan: '0', np.inf: '0'}).iloc[:100].tolist(),
+        "geometry": geo_df["geometry"].astype(str).replace({np.nan: None, np.inf: '0'}).iloc[:100].tolist() # Assuming geometry can be None
     }
 
+
     # Construct the payload
     payload = {
         "inputs": {