Delete app.py

app.py

@@ -1,205 +0,0 @@
-from shiny import App, ui, render
-import requests
-from bs4 import BeautifulSoup
-import pandas as pd
-import torch
-from transformers import BertTokenizer, BertModel
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-
-# 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')
-
-# Shiny UI and Server
-app_ui = ui.page_fluid(
-    ui.tags.style(
-        """
-        @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;500;700&display=swap');
-
-        body {
-            font-family: 'Poppins', sans-serif;
-            background-color: #f4f6f9;
-        }
-
-        .container {
-            padding: 20px;
-        }
-        
-        h2 {
-            color: #ff6f61;
-            font-weight: 700;
-            text-align: center;
-        }
-
-        .result-container {
-            display: flex;
-            flex-wrap: wrap;
-            gap: 20px;
-            justify-content: center;
-        }
-        
-        .course-card {
-            background-color: #ffffff;
-            border-radius: 12px;
-            box-shadow: 0 4px 10px rgba(0, 0, 0, 0.15);
-            overflow: hidden;
-            width: calc(50% - 10px);
-            transition: transform 0.3s, box-shadow 0.3s;
-        }
-        
-        .course-card:hover {
-            transform: scale(1.05);
-            box-shadow: 0 6px 20px rgba(0, 0, 0, 0.2);
-        }
-
-        .course-image {
-            width: 100%;
-            height: 180px;
-            object-fit: cover;
-            border-top-left-radius: 12px;
-            border-top-right-radius: 12px;
-        }
-
-        .course-info {
-            padding: 15px;
-        }
-        
-        .course-info h3 {
-            font-size: 20px;
-            color: #333;
-            margin-top: 0;
-        }
-
-        .course-info p {
-            color: #666;
-            font-size: 16px;
-            margin-bottom: 10px;
-        }
-
-        .course-link {
-            background-color: #ff6f61;
-            color: white;
-            padding: 8px 12px;
-            text-decoration: none;
-            border-radius: 6px;
-            font-size: 15px;
-            display: inline-block;
-            margin-top: 10px;
-            transition: background-color 0.2s;
-        }
-
-        .course-link:hover {
-            background-color: #e85a50;
-        }
-
-        .no-results {
-            text-align: center;
-            color: #888;
-            font-style: italic;
-        }
-        """
-    ),
-    ui.h2("Analytics Vidhya Smart Course Search"),
-    ui.input_text("query", "Enter your search query", placeholder="e.g., machine learning, data science, python"),
-    ui.output_text("search_info"),
-    ui.output_ui("results")
-)
-
-def server(input, output, session):
-    @output
-    @render.ui
-    def results():
-        if not input.query():
-            return ui.p("Enter a search query to get started!", class_="no-results")
-        
-        # Perform the search
-        query = input.query()
-        results = search_courses(query)
-        
-        if results:
-            result_ui = []
-            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)
-                
-                # Create course card UI
-                result_ui.append(
-                    ui.div(
-                        ui.img(src=course_image, class_="course-image"),
-                        ui.div(
-                            ui.h3(course_title),
-                            ui.p(f"Relevance: {relevance_score}%"),
-                            ui.a("View Course", href=course_link, target="_blank", class_="course-link"),
-                            class_="course-info"
-                        ),
-                        class_="course-card"
-                    )
-                )
-            return ui.div(*result_ui, class_="result-container")
-        else:
-            return ui.p("No results found.", class_="no-results")
-    
-    @output
-    @render.text
-    def search_info():
-        return f"Results for '{input.query()}'" if input.query() else "Search for courses by typing a query above."
-
-app = App(app_ui, server)