Update app.py

app.py

@@ -1,3 +1,292 @@
+# '''Copyright 2024 Ashok Kumar
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.'''
+
+# import os
+# import requests
+# import json
+# import pandas as pd
+# import numpy as np
+# import requests
+# import geopandas as gpd
+# import contextily as ctx
+# import tzlocal
+# import pytz 
+# from PIL import Image
+# from datetime import datetime
+# import matplotlib.pyplot as plt
+# from geopy.exc import GeocoderTimedOut
+# from geopy.geocoders import Nominatim
+# import warnings
+# warnings.filterwarnings('ignore')
+# from plotly.graph_objs import Marker
+# import plotly.express as px
+# import streamlit as st
+# from data import flight_data
+# from huggingface_hub import InferenceApi, login, InferenceClient
+
+
+# hf_token = os.getenv("HF_TOKEN")
+# if hf_token is None:
+#     raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
+# login(hf_token)
+
+
+# API_URL = "https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq"
+# headers = {"Authorization": f"Bearer {hf_token}"}
+
+# def query(payload):
+#     response = requests.post(API_URL, headers=headers, json=payload)
+#     return response.json()
+
+# 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[: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": {
+#             "query": question,
+#             "table": table_data,
+#         }
+#     }
+
+#     # Get the model response
+#     response = query(payload)
+
+#     # Check if 'answer' is in response and return it as a sentence
+#     if 'answer' in response:
+#         answer = response['answer']
+#         return f"The answer to your question '{question}': :orange[{answer}]"
+#     else:
+#         return "The model could not find an answer to your question."
+
+
+# def flight_tracking(flight_view_level, country, local_time_zone, flight_info, airport, color):
+#     geolocator = Nominatim(user_agent="flight_tracker")
+#     loc = geolocator.geocode(country)
+#     loc_box = loc[1]
+#     extend_left =+12*flight_view_level
+#     extend_right =+10*flight_view_level
+#     extend_top =+10*flight_view_level
+#     extend_bottom =+ 18*flight_view_level
+#     lat_min, lat_max = (loc_box[0] - extend_left), loc_box[0]+extend_right
+#     lon_min, lon_max = (loc_box[1] - extend_bottom), loc_box[1]+extend_top
+    
+#     tile_zoom = 8 # zoom of the map loaded by contextily
+#     figsize = (15, 15)
+#     columns = ["icao24","callsign","origin_country","time_position","last_contact","longitude","latitude",
+#             "baro_altitude","on_ground","velocity","true_track","vertical_rate","sensors","geo_altitude",
+#             "squawk","spi","position_source",]
+#     data_url = "https://raw.githubusercontent.com/ashok2216-A/ashok_airport-data/main/data/airports.dat"
+#     column_names = ["Airport ID", "Name", "City", "Country", "IATA/FAA", "ICAO", "Latitude", "Longitude",
+#                     "Altitude", "Timezone", "DST", "Tz database time zone", "Type", "Source"]
+#     airport_df = pd.read_csv(data_url, header=None, names=column_names)
+#     airport_locations = airport_df[["Name", "City", "Country", "IATA/FAA", "Latitude", "Longitude"]]
+#     airport_country_loc = airport_locations[airport_locations['Country'] == str(loc)]
+#     airport_country_loc = airport_country_loc[(airport_country_loc['Country'] == str(loc)) & (airport_country_loc['Latitude'] >= lat_min) &
+#                             (airport_country_loc['Latitude'] <= lat_max) & (airport_country_loc['Longitude'] >= lon_min) &
+#                             (airport_country_loc['Longitude'] <= lon_max)]
+#     def get_traffic_gdf():
+#         url_data = (
+#                 f"https://@opensky-network.org/api/states/all?"
+#                 f"lamin={str(lat_min)}"
+#                 f"&lomin={str(lon_min)}"
+#                 f"&lamax={str(lat_max)}"
+#                 f"&lomax={str(lon_max)}")
+#         json_dict = requests.get(url_data).json()
+
+#         unix_timestamp = int(json_dict["time"])
+#         local_timezone = pytz.timezone(local_time_zone) # get pytz timezone
+#         local_time = datetime.fromtimestamp(unix_timestamp, local_timezone).strftime('%Y-%m-%d %H:%M:%S')
+#         time = []
+#         for i in range(len(json_dict['states'])):
+#             time.append(local_time)
+#         df_time = pd.DataFrame(time,columns=['time'])
+#         state_df = pd.DataFrame(json_dict["states"],columns=columns)
+#         state_df['time'] = df_time
+#         gdf = gpd.GeoDataFrame(
+#                 state_df,
+#                 geometry=gpd.points_from_xy(state_df.longitude, state_df.latitude),
+#                 crs={"init": "epsg:4326"},  # WGS84
+#             )
+#         # banner_image = Image.open('banner.png')
+#         # st.image(banner_image, width=300)
+#         st.title("Live Flight Tracker")
+#         st.subheader('Flight Details', divider='rainbow')
+#         st.write('Location: {0}'.format(loc))
+#         st.write('Current Local Time: {0}-{1}:'.format(local_time, local_time_zone))
+#         st.write("Minimum_latitude is {0} and Maximum_latitude is {1}".format(lat_min, lat_max))
+#         st.write("Minimum_longitude is {0} and Maximum_longitude is {1}".format(lon_min, lon_max))
+#         st.write('Number of Visible Flights: {}'.format(len(json_dict['states'])))
+#         st.write('Plotting the flight: {}'.format(flight_info))
+#         st.subheader('Map Visualization', divider='rainbow')
+#         st.write('****Click ":orange[Update Map]" Button to Refresh the Map****')
+#         return gdf
+
+#     geo_df = get_traffic_gdf()
+#     if airport == 0:
+#         fig = px.scatter_mapbox(geo_df, lat="latitude", lon="longitude",color=flight_info,
+#                             color_continuous_scale=color, zoom=4,width=1200, height=600,opacity=1,
+#                             hover_name ='origin_country',hover_data=['callsign', 'baro_altitude',
+#         'on_ground', 'velocity', 'true_track', 'vertical_rate', 'geo_altitude'], template='plotly_dark')
+#     elif airport == 1:
+#         fig = px.scatter_mapbox(geo_df, lat="latitude", lon="longitude",color=flight_info,
+#                             color_continuous_scale=color, zoom=4,width=1200, height=600,opacity=1,
+#                             hover_name ='origin_country',hover_data=['callsign', 'baro_altitude',
+#         'on_ground', 'velocity', 'true_track', 'vertical_rate', 'geo_altitude'], template='plotly_dark')
+#         fig.add_trace(px.scatter_mapbox(airport_country_loc, lat="Latitude", lon="Longitude",
+#                                         hover_name ='Name', hover_data=["City", "Country", "IATA/FAA"]).data[0])
+#     else: None
+#     fig.update_layout(mapbox_style="carto-darkmatter")
+#     fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
+#     # out = fig.show())
+#     out = st.plotly_chart(fig, theme=None)
+#     return out
+# st.set_page_config(
+#     layout="wide"
+# )
+# image = Image.open('logo.png')
+# add_selectbox = st.sidebar.image(
+#     image, width=150
+# )
+# add_selectbox = st.sidebar.subheader(
+#     "Configure Map",divider='rainbow'
+# )
+# with st.sidebar:
+#     Refresh = st.button('Update Map', key=1)
+#     on = st.toggle('View Airports')
+#     if on:
+#         air_port = 1
+#         st.write(':rainbow[Nice Work Buddy!]')
+#         st.write('Now Airports are Visible')
+#     else:
+#         air_port=0
+#     view = st.slider('Increase Flight Visibility',1,6,2)
+#     st.write("You Selected:", view)
+#     cou = st.text_input('Type Country Name', 'north america')
+#     st.write('The current Country name is', cou)
+#     time = st.text_input('Type Time Zone Name (Ex: America/Toronto, Europe/Berlin)', 'Asia/Kolkata')
+#     st.write('The current Time Zone is', time)
+#     info = st.selectbox(
+#     'Select Flight Information',
+#     ('baro_altitude',
+#         'on_ground', 'velocity',
+#         'geo_altitude'))
+#     st.write('Plotting the data of Flight:', info)
+#     clr = st.radio('Pick A Color for Scatter Plot',["rainbow","ice","hot"])
+#     if clr == "rainbow":
+#         st.write('The current color is', "****:rainbow[Rainbow]****")
+#     elif clr == 'ice':
+#         st.write('The current color is', "****:blue[Ice]****")
+#     elif clr == 'hot':
+#         st.write('The current color is', "****:red[Hot]****")
+#     else: None
+# # with st.spinner('Wait!, We Requesting API Data...'):
+# # try:
+# flight_tracking(flight_view_level=view, country=cou,flight_info=info,
+#             local_time_zone=time, airport=air_port, color=clr)
+# st.subheader('Ask your Questions!', divider='rainbow')
+# st.write("Google's TAPAS base LLM model 🤖")
+# geo_df = flight_data(flight_view_level = view, country= cou, flight_info=info, local_time_zone=time, airport=1)
+# question = st.text_input('Type your questions here', "What is the squawk code for SWR9XD?")
+# result = query_flight_data(geo_df, question)
+# st.markdown(result)
+# # except TypeError:
+# #     st.error(':red[Error: ] Please Re-run this page.', icon="🚨")
+# #     st.button('Re-run', type="primary")
+# # st.snow()
+
+
+
+# # import streamlit as st
+# # from huggingface_hub import InferenceClient
+# # import os
+
+# # hf_token = os.getenv("HF_TOKEN")
+# # # Set up the Hugging Face Inference Client
+# # client = InferenceClient(
+# #     provider="together",  # Replace with the correct provider if needed
+# #     api_key= hf_token  # Replace with your Hugging Face API key
+# # )
+
+# # # Streamlit app title
+# # st.title("🤖 Deepseek R1 Chatbot")
+# # st.write("Chat with the Deepseek R1 model powered by Hugging Face Inference API.")
+
+# # # Initialize session state to store chat history
+# # if "messages" not in st.session_state:
+# #     st.session_state.messages = []
+
+# # # Display chat history
+# # for message in st.session_state.messages:
+# #     with st.chat_message(message["role"]):
+# #         st.markdown(message["content"])
+
+# # # User input
+# # if prompt := st.chat_input("What would you like to ask?"):
+# #     # Add user message to chat history
+# #     st.session_state.messages.append({"role": "user", "content": prompt})
+# #     with st.chat_message("user"):
+# #         st.markdown(prompt)
+
+# #     # Generate response from Deepseek R1 model
+# #     with st.spinner("Thinking..."):
+# #         try:
+# #             # Prepare the messages for the model
+# #             messages = [{"role": m["role"], "content": m["content"]} for m in st.session_state.messages]
+            
+# #             # Call the Hugging Face Inference API
+# #             completion = client.chat.completions.create(
+# #                 model="deepseek-ai/DeepSeek-R1",  # Replace with the correct model name
+# #                 messages=messages,
+# #                 max_tokens=500
+# #             )
+            
+# #             # Extract the model's response
+# #             response = completion.choices[0].message.content
+
+# #             # Add model's response to chat history
+# #             st.session_state.messages.append({"role": "assistant", "content": response})
+# #             with st.chat_message("assistant"):
+# #                 st.markdown(response)
+        
+# #         except Exception as e:
+# #             st.error(f"An error occurred: {e}")
+
+
 '''Copyright 2024 Ashok Kumar
 
 Licensed under the Apache License, Version 2.0 (the "License");
@@ -94,79 +383,35 @@ def fetch_flight_data(lat_min, lat_max, lon_min, lon_max):
         st.error(f"Unexpected error: {str(e)}")
         return {'states': [], 'time': 0}
 
+
+hf_token = os.getenv("HF_TOKEN")
+if hf_token is None:
+    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
+login(hf_token)
+
 # Hugging Face model configuration
-HF_API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large"
-HF_TOKEN = os.getenv("HF_TOKEN")
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+HF_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
+headers = {"Authorization": f"Bearer {hf_token}"}
 
 def query_llm(prompt):
     try:
         payload = {
             "inputs": prompt,
             "parameters": {
-                "max_length": 250,
+                "max_new_tokens": 250,
                 "temperature": 0.1,
                 "top_p": 0.95,
-                "do_sample": False
+                "return_full_text": False
             }
         }
         
         response = requests.post(HF_API_URL, headers=headers, json=payload)
         response.raise_for_status()
         return response.json()[0]['generated_text']
-    except requests.exceptions.HTTPError as e:
-        if e.response.status_code == 403:
-            st.warning("Language model access is currently restricted. Using direct flight data display instead.")
-        else:
-            st.error(f"Error querying language model: {str(e)}")
-        return None
     except Exception as e:
         st.error(f"Error querying language model: {str(e)}")
         return None
 
-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
-        flight_texts = []
-        for _, row in geo_df.iterrows():
-            text = f"Flight {row['callsign']} from {row['origin_country']} "
-            text += f"at altitude {row['baro_altitude']}m, speed {row['velocity']}m/s, "
-            text += f"heading {row['true_track']}°"
-            flight_texts.append(text)
-        
-        # Generate embeddings
-        embeddings = model.encode(flight_texts)
-        return embeddings, flight_texts
-    except Exception as e:
-        st.warning(f"Could not create embeddings: {str(e)}")
-        return None, None
-
-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
-        similar_indices = [i for i, sim in enumerate(similarities) if sim > threshold]
-        if similar_indices:
-            return geo_df.iloc[similar_indices]
-        return None
-    except Exception as e:
-        st.warning(f"Error in semantic search: {str(e)}")
-        return None
-
 def query_flight_data(geo_df, question):
     # Preprocess the question to extract key information
     question = question.lower().strip()
@@ -198,61 +443,17 @@ def query_flight_data(geo_df, question):
     if not identifier:
         return "Please specify a flight identifier (callsign or ICAO code) in your question."
     
-    # Clean and normalize the identifier
-    identifier = identifier.strip().upper()
-    
-    # Try to find the flight by callsign or icao (case-insensitive)
+    # Try to find the flight by callsign or icao
     flight_data = None
+    if identifier in geo_df['callsign'].values:
+        flight_data = geo_df[geo_df['callsign'] == identifier]
+    elif identifier in geo_df['icao24'].values:
+        flight_data = geo_df[geo_df['icao24'] == identifier]
     
-    # First try exact match
-    if identifier in geo_df['callsign'].str.upper().values:
-        flight_data = geo_df[geo_df['callsign'].str.upper() == identifier]
-    elif identifier in geo_df['icao24'].str.upper().values:
-        flight_data = geo_df[geo_df['icao24'].str.upper() == identifier]
-    
-    # If no exact match, try partial match
     if flight_data is None or flight_data.empty:
-        # Try matching without spaces or special characters
-        clean_identifier = ''.join(filter(str.isalnum, identifier))
-        if not geo_df['callsign'].empty:
-            clean_callsigns = geo_df['callsign'].fillna('').apply(lambda x: ''.join(filter(str.isalnum, str(x).upper())))
-            matches = clean_callsigns == clean_identifier
-            if matches.any():
-                flight_data = geo_df[matches]
+        return f"Could not find flight information for {identifier}. Please check the flight identifier and try again."
     
-    # 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:
-                flight_data = geo_df[geo_df['callsign'].str.upper() == close_matches[0]]
-        except:
-            pass
-    
-    # If still no match, try semantic search using RAG
-    if flight_data is None or flight_data.empty:
-        try:
-            # Create embeddings for all flights
-            embeddings, flight_texts = create_flight_embeddings(geo_df)
-            if embeddings is not None:
-                # Try to find similar flights
-                similar_flights = find_similar_flights(identifier, geo_df, embeddings, flight_texts)
-                if similar_flights is not None and not similar_flights.empty:
-                    flight_data = similar_flights
-                    st.info(f"Found similar flight(s) to {identifier}")
-        except Exception as e:
-            st.warning(f"Semantic search failed: {str(e)}")
-    
-    if flight_data is None or flight_data.empty:
-        # If still no match, show available flights
-        available_flights = geo_df['callsign'].dropna().unique()
-        if len(available_flights) > 0:
-            return f"Could not find flight {identifier}. Available flights: {', '.join(available_flights[:10])}..."
-        return f"Could not find flight {identifier}. No flights currently available in the selected area."
-    
-    # Prepare flight data for display
+    # Prepare flight data for the LLM
     flight_info = {}
     for col in flight_data.columns:
         if col in flight_data.columns:
@@ -270,75 +471,33 @@ def query_flight_data(geo_df, question):
                     flight_info[col] = f"{value}° N"
                 elif col == 'longitude':
                     flight_info[col] = f"{value}° E"
-    else:
+                else:
                     flight_info[col] = str(value)
     
     if not flight_info:
         return f"No information available for flight {identifier}."
     
-    # Try to get LLM response, but fall back to direct display if it fails
-    try:
-        # Create a prompt for the LLM
-        prompt = f"""Answer this question about flight {identifier}: {question}
+    # Create a prompt for the LLM
+    prompt = f"""You are a flight information assistant. Answer the following question about flight {identifier} using the provided flight data.
+
+Question: {question}
 
-Available flight data:
+Flight Data:
 {json.dumps(flight_info, indent=2)}
 
-Provide a clear and concise answer focusing on the specific information requested."""
-        
-        llm_response = query_llm(prompt)
-        if llm_response:
-            return llm_response
-    except:
-        pass
-    
-    # Fallback to direct data display
-    response = f"Flight Information for {identifier}:\n"
-    for key, value in flight_info.items():
-        response += f"- {key.replace('_', ' ').title()}: {value}\n"
-    return response
+Please provide a clear and concise answer focusing on the specific information requested in the question. If the question asks for information not available in the data, say so clearly."""
 
-@st.cache_data(ttl=60)  # Cache for 1 minute
-def get_traffic_gdf(lat_min, lat_max, lon_min, lon_max, local_time_zone, loc, flight_info):
-    # Get cached flight data
-    json_dict = fetch_flight_data(lat_min, lat_max, lon_min, lon_max)
+    # Get response from LLM
+    llm_response = query_llm(prompt)
     
-    if not json_dict or not json_dict.get('states'):
-        st.warning("No flight data available for the selected area.")
-        return None
-
-    try:
-        unix_timestamp = int(json_dict["time"])
-        local_timezone = pytz.timezone(local_time_zone)
-        local_time = datetime.fromtimestamp(unix_timestamp, local_timezone).strftime('%Y-%m-%d %H:%M:%S')
-        
-        # Optimize DataFrame creation
-        state_df = pd.DataFrame(json_dict["states"], columns=columns)
-        state_df['time'] = local_time
-        
-        # Create GeoDataFrame more efficiently
-        gdf = gpd.GeoDataFrame(
-            state_df,
-            geometry=gpd.points_from_xy(state_df.longitude, state_df.latitude),
-            crs="EPSG:4326"
-        )
-        
-        # Display information
-        st.title("Live Flight Tracker")
-        st.subheader('Flight Details', divider='rainbow')
-        st.write('Location: {0}'.format(loc))
-        st.write('Current Local Time: {0}-{1}:'.format(local_time, local_time_zone))
-        st.write("Minimum_latitude is {0} and Maximum_latitude is {1}".format(lat_min, lat_max))
-        st.write("Minimum_longitude is {0} and Maximum_longitude is {1}".format(lon_min, lon_max))
-        st.write('Number of Visible Flights: {}'.format(len(json_dict['states'])))
-        st.write('Plotting the flight: {}'.format(flight_info))
-        st.subheader('Map Visualization', divider='rainbow')
-        st.write('****Click ":orange[Update Map]" Button to Refresh the Map****')
-        return gdf
-        
-    except Exception as e:
-        st.error(f"Error processing flight data: {str(e)}")
-        return None
+    if llm_response:
+        return llm_response
+    else:
+        # Fallback to direct data response if LLM fails
+        response = f"Flight Information for {identifier}:\n"
+        for key, value in flight_info.items():
+            response += f"- {key.replace('_', ' ').title()}: {value}\n"
+        return response
 
 def flight_tracking(flight_view_level, country, local_time_zone, flight_info, airport, color):
     # Get cached location data
@@ -367,10 +526,51 @@ def flight_tracking(flight_view_level, country, local_time_zone, flight_info, ai
                                             (airport_country_loc['Latitude'] >= lat_min) &
                                             (airport_country_loc['Latitude'] <= lat_max) & 
                                             (airport_country_loc['Longitude'] >= lon_min) &
-                            (airport_country_loc['Longitude'] <= lon_max)]
+                                            (airport_country_loc['Longitude'] <= lon_max)]
     
+    def get_traffic_gdf():
+        # Get cached flight data
+        json_dict = fetch_flight_data(lat_min, lat_max, lon_min, lon_max)
+        
+        if not json_dict or not json_dict.get('states'):
+            st.warning("No flight data available for the selected area.")
+            return None
+
+        try:
+            unix_timestamp = int(json_dict["time"])
+            local_timezone = pytz.timezone(local_time_zone)
+            local_time = datetime.fromtimestamp(unix_timestamp, local_timezone).strftime('%Y-%m-%d %H:%M:%S')
+            
+            # Optimize DataFrame creation
+            state_df = pd.DataFrame(json_dict["states"], columns=columns)
+            state_df['time'] = local_time
+            
+            # Create GeoDataFrame more efficiently
+            gdf = gpd.GeoDataFrame(
+                state_df,
+                geometry=gpd.points_from_xy(state_df.longitude, state_df.latitude),
+                crs="EPSG:4326"
+            )
+            
+            # Display information
+            st.title("Live Flight Tracker")
+            st.subheader('Flight Details', divider='rainbow')
+            st.write('Location: {0}'.format(loc))
+            st.write('Current Local Time: {0}-{1}:'.format(local_time, local_time_zone))
+            st.write("Minimum_latitude is {0} and Maximum_latitude is {1}".format(lat_min, lat_max))
+            st.write("Minimum_longitude is {0} and Maximum_longitude is {1}".format(lon_min, lon_max))
+            st.write('Number of Visible Flights: {}'.format(len(json_dict['states'])))
+            st.write('Plotting the flight: {}'.format(flight_info))
+            st.subheader('Map Visualization', divider='rainbow')
+            st.write('****Click ":orange[Update Map]" Button to Refresh the Map****')
+            return gdf
+            
+        except Exception as e:
+            st.error(f"Error processing flight data: {str(e)}")
+            return None
+
     # Get traffic data
-    geo_df = get_traffic_gdf(lat_min, lat_max, lon_min, lon_max, local_time_zone, loc, flight_info)
+    geo_df = get_traffic_gdf()
     if geo_df is None:
         return