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| import streamlit as st | |
| from sentence_transformers import SentenceTransformer, util | |
| import pandas as pd | |
| import numpy as np | |
| # Load pre-trained model | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| # Load the course data from CSV | |
| df = pd.read_csv('analytics.csv') | |
| # Generate embeddings for course titles and descriptions | |
| course_embeddings = model.encode(df['Title'] + ' ' + df['Description'], convert_to_tensor=True) | |
| # Function to search for courses based on a query | |
| def search_courses(query, top_n=5): | |
| if top_n <= 0: | |
| raise ValueError("top_n must be greater than zero") | |
| query_embedding = model.encode(query, convert_to_tensor=True) | |
| similarities = util.pytorch_cos_sim(query_embedding, course_embeddings)[0].numpy() | |
| if np.any(np.isnan(similarities)) or np.any(np.isinf(similarities)): | |
| raise ValueError("Similarity scores contain NaN or infinity values") | |
| top_results = np.argsort(similarities)[::-1][:top_n] | |
| results = [] | |
| for idx in top_results: | |
| course_info = { | |
| 'Title': df.iloc[idx]['Title'], | |
| 'Description': df.iloc[idx]['Description'], | |
| 'Link': df.iloc[idx]['Link'], | |
| 'Relevance Score': similarities[idx].item() | |
| } | |
| results.append(course_info) | |
| return results | |
| # Streamlit app configuration | |
| st.set_page_config(page_title="Smart Course Finder", layout="centered") | |
| # App title and description | |
| st.title("🔍 Smart Course Finder for Analytics Vidhya") | |
| st.markdown(""" | |
| Find the best free courses that match your learning interests. Enter a keyword or topic to discover the most relevant courses available. | |
| """) | |
| # Input field for the query | |
| query = st.text_input("Enter your search query:", placeholder="e.g., Machine Learning, Data Science, Generative AI") | |
| # Search button and display results | |
| if st.button("Search") and query: | |
| with st.spinner("Searching for courses..."): | |
| try: | |
| top_courses = search_courses(query) | |
| if top_courses: | |
| st.success("Top courses found:") | |
| for course in top_courses: | |
| st.subheader(f"📘 {course['Title']}") | |
| st.write(f"**Description**: {course['Description']}") | |
| st.write(f"**Relevance Score**: {course['Relevance Score']:.2f}") | |
| st.markdown(f"[🔗 View Course]({course['Link']})", unsafe_allow_html=True) | |
| st.markdown("---") | |
| else: | |
| st.warning("No courses found for the given query. Try different keywords.") | |
| except ValueError as e: | |
| st.error(f"Error: {e}") | |
| # Footer with credits | |
| st.markdown(""" | |
| --- | |
| *Built with ❤️ using Streamlit and SentenceTransformers* | |
| """) | |