'''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()