Create app.py

app.py

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+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import gradio as gr
+from groq import Groq
+
+# Step 1: Scrape free courses from Analytics Vidhya
+def fetch_free_courses():
+    url = "https://courses.analyticsvidhya.com/pages/all-free-courses"
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, 'html.parser')
+
+    courses_data = []
+    
+    # Extract course details
+    for card in soup.select('header.course-card__img-container'):
+        image_element = card.find('img', class_='course-card__img')
+        
+        if image_element:
+            title = image_element.get('alt')
+            img_url = image_element.get('src')
+            
+            link = card.find_previous('a')
+            if link:
+                course_link = link.get('href')
+                if not course_link.startswith('http'):
+                    course_link = 'https://courses.analyticsvidhya.com' + course_link
+
+                courses_data.append({
+                    'title': title,
+                    'image_url': img_url,
+                    'course_link': course_link
+                })
+    return courses_data
+
+courses = fetch_free_courses()
+
+# Step 2: Load data into a DataFrame
+df = pd.DataFrame(courses)
+
+client = Groq()
+
+# Course search function using Groq
+def course_recommendation(query):
+    try:
+        print(f"Search query: {query}")
+        print(f"Total available courses: {len(df)}")
+
+        # Prompt construction for Groq
+        prompt = f"""
+        Based on the query: "{query}",
+        Rank the courses below based on relevance (0 to 1), with 1 being highly relevant.
+        Filter out courses with relevance scores below 0.5.
+        
+        Courses:
+        {df['title'].to_string(index=False)}
+        """
+
+        print("Sending query to Groq for recommendation...")
+        # Sending the request to Groq for results
+        response = client.chat.completions.create(
+            model="mixtral-8x7b-32768",
+            messages=[
+                {"role": "system", "content": "You are a course recommendation assistant."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.3,
+            max_tokens=800
+        )
+        print("Response received from Groq.")
+
+        # Parse the Groq response
+        recommended_courses = []
+        content = response.choices[0].message.content
+        print("Groq's response:\n", content)
+
+        for line in content.split('\n'):
+            if line.startswith('Title:'):
+                course_title = line.split('Title:')[1].strip()
+            elif line.startswith('Relevance:'):
+                score = float(line.split('Relevance:')[1].strip())
+                if score >= 0.5:
+                    matching_course = df[df['title'] == course_title]
+                    if not matching_course.empty:
+                        course_data = matching_course.iloc[0]
+                        recommended_courses.append({
+                            'title': course_title,
+                            'image_url': course_data['image_url'],
+                            'course_link': course_data['course_link'],
+                            'score': score
+                        })
+        
+        return sorted(recommended_courses, key=lambda x: x['score'], reverse=True)[:10]
+
+    except Exception as e:
+        print(f"Error during course search: {e}")
+        return []
+
+# Gradio function to search and display courses
+def gradio_search_interface(query):
+    results = course_recommendation(query)
+    
+    if results:
+        html_output = '<div class="results-section">'
+        for course in results:
+            html_output += f"""
+            <div class="course-item">
+                <img src="{course['image_url']}" alt="{course['title']}" class="course-thumbnail"/>
+                <div class="course-details">
+                    <h4>{course['title']}</h4>
+                    <p>Relevance: {round(course['score'] * 100, 2)}%</p>
+                    <a href="{course['course_link']}" target="_blank" class="course-link-button">Explore Course</a>
+                </div>
+            </div>"""
+        html_output += '</div>'
+        return html_output
+    else:
+        return '<p class="no-courses-message">No matching courses found. Try another search.</p>'
+
+# Custom CSS to make the interface attractive
+custom_css = """
+body {
+    background-color: #eaeef3;
+    font-family: 'Montserrat', sans-serif;
+}
+.results-section {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 20px;
+}
+.course-item {
+    background-color: white;
+    border-radius: 12px;
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    overflow: hidden;
+    width: 48%;
+    transition: transform 0.3s ease;
+}
+.course-item:hover {
+    transform: translateY(-10px);
+}
+.course-thumbnail {
+    width: 100%;
+    height: 160px;
+    object-fit: cover;
+}
+.course-details {
+    padding: 15px;
+    text-align: center;
+}
+.course-details h4 {
+    font-size: 18px;
+    color: #333;
+    margin: 10px 0;
+}
+.course-details p {
+    color: #555;
+    font-size: 14px;
+}
+.course-link-button {
+    display: inline-block;
+    background-color: #ff5733;
+    color: white;
+    padding: 8px 16px;
+    text-decoration: none;
+    border-radius: 6px;
+    margin-top: 10px;
+}
+.course-link-button:hover {
+    background-color: #c44524;
+}
+.no-courses-message {
+    text-align: center;
+    color: #777;
+    font-size: 16px;
+}
+"""
+
+# Setting up the Gradio interface
+iface = gr.Interface(
+    fn=gradio_search_interface,
+    inputs=gr.Textbox(label="Search for a course", placeholder="e.g., Python for data analysis, ML basics"),
+    outputs=gr.HTML(label="Course Results"),
+    title="Analytics Vidhya Course Finder",
+    description="Discover the best free courses from Analytics Vidhya tailored to your query.",
+    theme="compact",
+    css=custom_css,
+    examples=[
+        ["Data Science for Beginners"],
+        ["Python Programming"],
+        ["Advanced Machine Learning"],
+        ["Business Analytics"],
+    ]
+)
+
+# Run the Gradio interface
+if __name__ == "__main__":
+    iface.launch()