app.py

import requests
from bs4 import BeautifulSoup
import pandas as pd
import gradio as gr
import os
from groq import Groq
import creds  # Ensure creds.py contains `api_key` as `creds.api_key`

# Step 1: Scrape free courses from Analytics Vidhya
def scrape_courses():
    url = "https://courses.analyticsvidhya.com/pages/all-free-courses"
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')
    courses = []

    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 pd.DataFrame(courses)

# Initialize course DataFrame
df = scrape_courses()

# Step 2: Initialize the Groq client and set the API key
client = Groq(api_key=creds.api_key)

def search_courses(query):
    try:
        print(f"Searching for: {query}")
        prompt = f"""Given the query: "{query}"
        Rank the following courses based on relevance to the query, prioritizing Analytics Vidhya courses.
        Provide a relevance score (0-1) for each course, returning only those with a score >= 0.5.
        
        Courses:
        {df['title'].to_string(index=False)}
        """

        response = client.chat.completions.create(
            model="mixtral-8x7b-32768",
            messages=[{"role": "system", "content": "You are an AI assistant specialized in course recommendations."},
                      {"role": "user", "content": prompt}],
            temperature=0.2,
            max_tokens=1000
        )

        results = []
        for line in response.choices[0].message.content.split('\n'):
            if line.startswith('Title:'):
                title = line.split('Title:')[1].strip()
            elif line.startswith('Relevance:'):
                relevance = float(line.split('Relevance:')[1].strip())
                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
                        })

        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 = '<div class="results-container">'
        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'''
            <div class="course-card">
                <img src="{course_image}" alt="{course_title}" class="course-image"/>
                <div class="course-info">
                    <h3>{course_title}</h3>
                    <p>Relevance: {relevance_score}%</p>
                    <a href="{course_link}" target="_blank" class="course-link">View Course</a>
                </div>
            </div>'''
        html_output += '</div>'
        return html_output
    else:
        return '<p class="no-results">No results found. Please try a different query.</p>'

# Custom CSS for the Gradio interface
custom_css = """
body { font-family: Arial, sans-serif; background-color: #f0f2f5; }
h1, h2, p, .container .examples { color: #333; }
.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()