import requests
from bs4 import BeautifulSoup
import pandas as pd
import gradio as gr
from groq import Groq
import creds # Assuming creds.py holds your API key as creds.api_key
# Step 1: Scrape the free courses from Analytics Vidhya
def scrape_courses():
url = "https://courses.analyticsvidhya.com/pages/all-free-courses"
try:
response = requests.get(url)
response.raise_for_status() # Raise an error for bad status codes
except requests.RequestException as e:
print(f"Error fetching data: {e}")
return []
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
})
return courses
# Step 2: Create DataFrame
df = pd.DataFrame(scrape_courses())
# Step 3: Initialize the Groq client and set the API key
client = Groq(api_key=creds.api_key) # Properly passing the API key
def search_courses(query):
try:
print(f"Searching for: {query}")
print(f"Number of courses in database: {len(df)}")
# Prepare the prompt for Groq
prompt = f"""Given the following query: "{query}"
Please analyze the query and rank the following courses based on their relevance to the query.
Prioritize courses from Analytics Vidhya. Provide a relevance score from 0 to 1 for each course.
Only return courses with a relevance score of 0.5 or higher.
Return the results in the following format:
Title: [Course Title]
Relevance: [Score]
Courses:
{df['title'].to_string(index=False)}
"""
print("Sending request to Groq...")
# Get response from Groq
response = client.chat.completions.create(
model="mixtral-8x7b-32768", # Use the appropriate model
messages=[{"role": "system", "content": "You are an AI assistant specialized in course recommendations."},
{"role": "user", "content": prompt}],
temperature=0.2,
max_tokens=1000
)
print("Received response from Groq")
# Parse Groq's response
results = []
print("Groq response content:")
print(response.choices[0].message.content)
for line in response.choices[0].message.content.split('\n'):
if line.startswith('Title:'):
title = line.split('Title:')[1].strip()
print(f"Found title: {title}")
elif line.startswith('Relevance:'):
relevance = float(line.split('Relevance:')[1].strip())
print(f"Relevance for {title}: {relevance}")
if relevance >= 0.5:
matching_courses = df[df['title'] == title]
if not matching_courses.empty:
course = matching_courses.iloc[0]
results.append({
'title': title,
'image_url': course['image_url'],
'course_link': course['course_link'],
'score': relevance
})
print(f"Added course: {title}")
else:
print(f"Warning: Course not found in database: {title}")
print(f"Number of results found: {len(results)}")
return sorted(results, key=lambda x: x['score'], reverse=True)[:10] # Return top 10 results
except Exception as e:
print(f"An error occurred in search_courses: {str(e)}")
return []
def gradio_search(query):
result_list = search_courses(query)
if result_list:
html_output = ''
for item in result_list:
course_title = item['title']
course_image = item['image_url']
course_link = item['course_link']
relevance_score = round(item['score'] * 100, 2)
html_output += f'''
{course_title}
Relevance: {relevance_score}%
View Course
'''
html_output += '
No results found. Please try a different query.
'
# Custom CSS for the Gradio interface
custom_css = """
body {
font-family: Arial, sans-serif;
background-color: #000000; /* Set background to black */
color: #ffffff; /* Set text color to white for contrast */
}
.container {
max-width: 800px;
margin: 0 auto;
padding: 20px;
}
.results-container {
display: flex;
flex-wrap: wrap;
justify-content: space-between;
}
.course-card {
background-color: white;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
margin-bottom: 20px;
overflow: hidden;
width: 48%;
transition: transform 0.2s;
}
.course-card:hover {
transform: translateY(-5px);
}
.course-image {
width: 100%;
height: 150px;
object-fit: cover;
}
.course-info {
padding: 15px;
}
.course-info h3 {
margin-top: 0;
font-size: 18px;
color: #333;
}
.course-info p {
color: #666;
font-size: 14px;
margin-bottom: 10px;
}
.course-link {
display: inline-block;
background-color: #007bff;
color: white;
padding: 8px 12px;
text-decoration: none;
border-radius: 4px;
font-size: 14px;
transition: background-color 0.2s;
}
.course-link:hover {
background-color: #0056b3;
}
.no-results {
text-align: center;
color: #666;
font-style: italic;
}
"""
# Gradio interface
iface = gr.Interface(
fn=gradio_search,
inputs=gr.Textbox(label="Enter your search query", placeholder="e.g., machine learning, data science, python"),
outputs=gr.HTML(label="Search Results"),
title="Analytics Vidhya Smart Course Search",
description="Find the most relevant courses from Analytics Vidhya based on your query.",
theme="huggingface",
css=custom_css,
examples=[["machine learning for beginners"],
["advanced data visualization techniques"],
["python programming basics"],
["Business Analytics"]]
)
if __name__ == "__main__":
iface.launch()