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import streamlit as st |
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import difflib |
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import pandas as pd |
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lpi_df = pd.read_csv('https://huggingface.co/spaces/kmrmanish/LPI_Course_Recommendation_System/blob/f273238053ba214e906f36c6e31c047cf5ed6011/Learning_Pathway_Index.csv') |
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st.title('Course Recommendation App') |
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user_input = st.text_input('Enter What You Want to Learn : ') |
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if user_input: |
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list_of_all_titles = lpi_df['Module'].tolist() |
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find_close_match = difflib.get_close_matches(user_input, list_of_all_titles) |
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if find_close_match: |
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close_match = find_close_match[0] |
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index_of_the_course = lpi_df[lpi_df.Module == close_match].index.values[0] |
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similarity_score = list(enumerate(similarity[index_of_the_course])) |
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sorted_similar_course = sorted(similarity_score, key=lambda x: x[1], reverse=True) |
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st.write('Courses suggested for you :') |
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i = 1 |
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for course in sorted_similar_course: |
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index = course[0] |
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title_from_index = lpi_df[lpi_df.index == index]['Module'].values[0] |
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if i < 30: |
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st.write(f"{i}. {title_from_index}") |
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i += 1 |
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if i == 1: |
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st.write('No close matches found.') |
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else: |
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st.write('No close matches found.') |
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