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import pickle
import streamlit as st
from streamlit_extras.let_it_rain import rain
import numpy as np
import pandas as pd
# <==== Code starts here ====>
courses_list = pickle.load(open('courses.pkl','rb'))
similarity = pickle.load(open('similarity.pkl','rb'))
rain(
emoji="🕮",
font_size=30, # the size of emoji
falling_speed=6, # speed of raining
animation_length= 2, # for how much time the animation will happen
)
def recommend(course):
index = courses_list[courses_list['course_name'] == course].index[0]
dist1 = (similarity[index])
distances = sorted(list(enumerate(dist1)), reverse=True, key=lambda x: x[1])
dist = pd.DataFrame(dist1)
for i in distances[1:5]:
st.write("Name of the Course :memo: : ", courses_list.iloc[i[0]].course_name.upper())
st.write("Course URL :link: : ", courses_list.iloc[i[0]].course_url)
st.write("Platform :medal: : ", courses_list.iloc[i[0]].Platform.upper())
#st.write("Score :large_blue_diamond: : ", dist.iloc[i[0]].to_string(index = False))
st.write ("Reason for Recommendation (SKILLS YOU MAY GET) : ")
st.write(courses_list.iloc[i[0]].Skills)
st.write("-"* 90)
def main():
# To write title
st.markdown("<h5 style = 'text-align: Left; color: white;'>Hello! I'm here to help you find the perfect course over 5000 courses from dataset. Let's get Started !🙂</h5>",unsafe_allow_html=True)
st.markdown("<h5 style='text-align: Left ; color: blue;'> What field or topic are you interested in? </h5>", unsafe_allow_html=True)
course_list = courses_list['course_name'].values
selected_course = st.selectbox("Select here :point_down:",
courses_list
)
if st.button(' Recommend '):
st.success("Recommending courses similar to " + selected_course)
recommend(selected_course)
if __name__ == "__main__":
main()
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