| # '''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}") |