Upload 3 files

app.py

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+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)

newyorkcity_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a7c5f23114b276ab69ca9128c84bf87df154e14874398f9e33f1106761ba6940
+size 298060

requirements.txt

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+streamlit
+numpy
+pickle
+math