Update app.py

app.py

@@ -16,26 +16,25 @@ import os
 import requests
 import json
 import pandas as pd
+import requests
 import geopandas as gpd
 import tzlocal
-import pytz
+import pytz 
 from PIL import Image
-import contextily as ctx
 from datetime import datetime
+import matplotlib.pyplot as plt
 from geopy.exc import GeocoderTimedOut
 from geopy.geocoders import Nominatim
+import warnings
+warnings.filterwarnings('ignore')
 import folium
 from folium import plugins
 import streamlit as st
 import streamlit_folium as st_folium
 from data import flight_data
-from huggingface_hub import InferenceClient
+from huggingface_hub import InferenceApi, login, InferenceClient
 import branca.colormap as cm
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-from difflib import get_close_matches
-import warnings
-warnings.filterwarnings('ignore')
+from functools import lru_cache
 import time
 
 # Cache the airport data to avoid reloading it every time
@@ -126,6 +125,7 @@ def query_llm(prompt):
 def create_flight_embeddings(geo_df):
     """Create embeddings for flight data to enable semantic search"""
     try:
+        from sentence_transformers import SentenceTransformer
         model = SentenceTransformer('all-MiniLM-L6-v2')
         
         # Create text representations of flight data
@@ -146,12 +146,14 @@ def create_flight_embeddings(geo_df):
 def find_similar_flights(identifier, geo_df, embeddings, flight_texts, threshold=0.7):
     """Find similar flights using semantic search"""
     try:
+        from sentence_transformers import SentenceTransformer
         model = SentenceTransformer('all-MiniLM-L6-v2')
         
         # Create query embedding
         query_embedding = model.encode([identifier])
         
         # Calculate similarities
+        from sklearn.metrics.pairwise import cosine_similarity
         similarities = cosine_similarity(query_embedding, embeddings)[0]
         
         # Find similar flights
@@ -219,6 +221,7 @@ def query_flight_data(geo_df, question):
     # If still no match, try fuzzy matching
     if flight_data is None or flight_data.empty:
         try:
+            from difflib import get_close_matches
             all_callsigns = geo_df['callsign'].fillna('').str.upper().unique()
             close_matches = get_close_matches(identifier, all_callsigns, n=1, cutoff=0.8)
             if close_matches: