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import streamlit as st
import difflib
import pandas as pd
import numpy as np
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Download NLTK stopwords if not already done
nltk.download('stopwords')
# Read the data
lpi_df = pd.read_csv('Learning Pathway Index.csv')
# Rename columns
lpi_df.rename(columns={
"Course / Learning material": "Course_Learning_Material",
"Course Level": "Course_Level",
"Type (Free or Paid)": "Type",
"Module / Sub-module \nDifficulty level": "Difficulty_Level",
"Keywords / Tags / Skills / Interests / Categories": "Keywords"
}, inplace=True)
# Combine features
lpi_df['combined_features'] = lpi_df['Course_Learning_Material'] + ' ' + lpi_df['Source'] + ' ' + lpi_df['Course_Level'] + ' ' + lpi_df['Type'] + ' ' + lpi_df['Module'] + ' ' + lpi_df['Difficulty_Level'] + ' ' + lpi_df['Keywords']
# Text preprocessing
combined_features = lpi_df['combined_features']
porter_stemmer = PorterStemmer()
def stemming(content):
stemmed_content = re.sub('[^a-zA-Z]', ' ', content)
stemmed_content = stemmed_content.lower()
stemmed_content = stemmed_content.split()
stemmed_content = [porter_stemmer.stem(word) for word in stemmed_content if not word in stopwords.words('english')]
stemmed_content = ' '.join(stemmed_content)
return stemmed_content
combined_features = combined_features.apply(stemming)
# TF-IDF and similarity
vectorizer = TfidfVectorizer()
vectorizer.fit(combined_features)
combined_features = vectorizer.transform(combined_features)
similarity = cosine_similarity(combined_features)
# Streamlit app
st.set_page_config(
page_title="Course Recommendation App",
page_icon="✅",
layout="wide",
)
st.title('Learning Pathway Index Course Recommendation')
user_input = st.text_input('Enter What You Want to Learn : ')
if user_input:
list_of_all_titles = lpi_df['Module'].tolist()
find_close_match = difflib.get_close_matches(user_input, list_of_all_titles)
if find_close_match:
close_match = find_close_match[0]
index_of_the_course = lpi_df[lpi_df.Module == close_match].index.values[0]
similarity_score = list(enumerate(similarity[index_of_the_course]))
sorted_similar_course = sorted(similarity_score, key=lambda x: x[1], reverse=True)
st.subheader('Courses suggested for you:')
with st.beta_container():
col1, col2 = st.beta_columns(2)
for i, course in enumerate(sorted_similar_course[:30], start=1):
index = course[0]
title_from_index = lpi_df.loc[index, 'Module']
if i % 2 == 0:
with col2:
st.write(f"{i}. {title_from_index}")
else:
with col1:
st.write(f"{i}. {title_from_index}")
if len(sorted_similar_course) == 0:
st.warning('No close matches found.')
else:
st.warning('No close matches found.')
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