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import requests
from bs4 import BeautifulSoup
import pandas as pd
import streamlit as st
import torch
from transformers import BertTokenizer, BertModel
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import sys
import subprocess
try:
from bs4 import BeautifulSoup
except ModuleNotFoundError:
subprocess.check_call([sys.executable, "-m", "pip", "install", "beautifulsoup4"])
from bs4 import BeautifulSoup
# Step 1: Scrape the free courses from Analytics Vidhya
url = "https://courses.analyticsvidhya.com/pages/all-free-courses"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
courses = []
# Extracting course title, image, and course link
for course_card in soup.find_all('header', class_='course-card__img-container'):
img_tag = course_card.find('img', class_='course-card__img')
if img_tag:
title = img_tag.get('alt')
image_url = img_tag.get('src')
link_tag = course_card.find_previous('a')
if link_tag:
course_link = link_tag.get('href')
if not course_link.startswith('http'):
course_link = 'https://courses.analyticsvidhya.com' + course_link
courses.append({
'title': title,
'image_url': image_url,
'course_link': course_link
})
# Step 2: Create DataFrame
df = pd.DataFrame(courses)
# Load pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
# Function to generate embeddings using BERT
def get_bert_embedding(text):
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
return outputs.last_hidden_state.mean(dim=1).numpy()
# Create embeddings for course titles
df['embedding'] = df['title'].apply(lambda x: get_bert_embedding(x))
# Function to perform search using BERT-based similarity
def search_courses(query):
query_embedding = get_bert_embedding(query)
course_embeddings = np.vstack(df['embedding'].values)
# Compute cosine similarity between query embedding and course embeddings
similarities = cosine_similarity(query_embedding, course_embeddings).flatten()
# Add the similarity scores to the DataFrame
df['score'] = similarities
# Sort by similarity score in descending order and return top results
top_results = df.sort_values(by='score', ascending=False).head(10)
return top_results[['title', 'image_url', 'course_link', 'score']].to_dict(orient='records')
# Streamlit Interface
st.title("Analytics Vidhya Smart Course Search")
st.write("Find the most relevant courses from Analytics Vidhya based on your query.")
query = st.text_input("Enter your search query", placeholder="e.g., machine learning, data science, python")
if query:
results = search_courses(query)
if results:
for item in results:
course_title = item['title']
course_image = item['image_url']
course_link = item['course_link']
relevance_score = round(item['score'] * 100, 2)
st.image(course_image, width=300)
st.markdown(f"### [{course_title}]({course_link})")
st.write(f"Relevance: {relevance_score}%")
st.markdown("---")
else:
st.write("No results found.")
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