Update app.py

app.py

@@ -1,292 +1,3 @@
-# '''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");
@@ -383,15 +94,10 @@ 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/mistralai/Mistral-7B-Instruct-v0.2"
-headers = {"Authorization": f"Bearer {hf_token}"}
+HF_TOKEN = os.getenv("HF_TOKEN")
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
 
 def query_llm(prompt):
     try: