import streamlit as st
import numpy as np
import pickle
import math
def haversine(pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude):
    R = 6371.0
    
    phi1 = math.radians(pickup_latitude)
    phi2 = math.radians(dropoff_latitude)
    delta_phi = math.radians(dropoff_latitude - pickup_latitude)
    delta_lambda = math.radians(dropoff_longitude - pickup_longitude)
    
    a = math.sin(delta_phi / 2.0)**2 + math.cos(phi1) * math.cos(phi2) * math.sin(delta_lambda / 2.0)**2
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    
    distance = R * c
    return distance
p_lo=st.number_input("Pickup Longitude")
p_la=st.number_input("Pickup Latitude")
d_lo=st.number_input("Dropoff Longitude")
d_la=st.number_input("Dropoff Latitude")
passenger_count=st.number_input("Passenger Count")
distance=haversine(p_lo,p_la,d_lo,d_la)
file_path = 'newyorkcity_model.pkl'
with open(file_path, 'rb') as file:
    model = pickle.load(file)
data=[distance,passenger_count]
if st.button("Predict"):
    data=np.array(data)
    if len(data.shape) == 1:
       data = np.expand_dims(data, axis=0) 
    prediction=model.predict(data)
    st.write(prediction)