Delete app.py

app.py

@@ -1,89 +0,0 @@
-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
-
-# 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.")