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import os
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer, util
import streamlit as st
from preprocess import preprocess
# File paths
csv_file_path = 'courses_with_embeddings.csv'
embeddings_file_path = 'course_embeddings.npy'
# Function to check if data files exist and contain data
def check_files_exist():
if os.path.exists(csv_file_path) and os.path.exists(embeddings_file_path):
# Check if CSV and Numpy files have data
if os.path.getsize(csv_file_path) > 0 and os.path.getsize(embeddings_file_path) > 0:
return True
return False
# Run preprocess if files don't exist or are empty
if not check_files_exist():
preprocess()
# Load data and embeddings
df = pd.read_csv(csv_file_path)
embeddings = np.load(embeddings_file_path)
# Load the pre-trained model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Search function
def search_courses(query, top_k=5):
query_embedding = model.encode(query, convert_to_tensor=True)
similarities = util.pytorch_cos_sim(query_embedding, embeddings)[0]
top_results = similarities.topk(k=top_k)
results = [df.iloc[idx.item()] for idx in top_results.indices]
return results
def main():
# Streamlit interface
st.title("Smart Course Search")
st.markdown(
"""
### Find the Most Relevant Free Courses on Analytics Vidhya
Welcome to **Smart Course Search**! Simply type in your area of interest, and we'll show you the best courses available.
"""
)
# User input for the search query
query = st.text_input("Enter your search query:")
if query:
st.markdown(f"### Showing results for: *'{query}'*")
results = search_courses(query)
for result in results:
st.markdown("---")
st.markdown(f"## {result['title']}")
st.markdown(f"**Description:**")
st.markdown(result['description'])
# Course Curriculum Section
if 'Course curriculum' in result:
st.markdown("### Course Curriculum:")
st.markdown(result['Course curriculum'])
# About the Instructor Section
if 'About the Instructor' in result:
st.markdown("### About the Instructor:")
st.write(result['About the Instructor'])
# Adding a button to enroll or learn more about the course
if 'url' in result:
st.markdown("**[Learn More and Enroll Here](%s)**" % result['url'])
if __name__ == "__main__":
main() |