Update main.py

main.py

@@ -1,26 +1,52 @@
-# main.py
-
 import requests
+from bs4 import BeautifulSoup
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.cluster import KMeans
 from sklearn.decomposition import PCA
 from sklearn.metrics import silhouette_score
 import matplotlib.pyplot as plt
-from scraper import scrape_imdb_top_movies  # Import the scraping function
 
-# Scrape IMDb for summaries and titles of 250 top-rated movies
+
+def scrape_imdb_top_movies(num_movies):
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'
+    }
+    request = requests.get('https://www.imdb.com/chart/top/?ref_=login', headers=headers)
+    content = request.content
+    soup = BeautifulSoup(content, 'html.parser')
+    movie_link = soup.find_all('a', {"class": "ipc-title-link-wrapper"})
+
+    hrefs = []
+    movie_titles = []
+    for movie in movie_link:
+        text = movie.text
+        if text[0].isdigit():
+            movie_titles.append(text)
+            hrefs.append(movie.get("href"))
+
+    summaries = []
+    for index in range(num_movies):
+        url = "https://www.imdb.com" + hrefs[index]
+        print(f"Fetching summary for: {movie_titles[index]}")
+        r = requests.get(url, headers=headers)
+        url_soup = BeautifulSoup(r.content, 'html.parser')
+        summary = url_soup.find('span', {'data-testid': 'plot-l'}).text if url_soup.find('span', {'data-testid': 'plot-l'}) else "No summary available"
+        summaries.append(summary)
+
+    return movie_titles[:num_movies], summaries
+
+
 num_movies = 250
 movie_titles, summaries = scrape_imdb_top_movies(num_movies)
 
-# TF-IDF Vectorization
+
 vectorizer = TfidfVectorizer(stop_words='english')
 tfidf_matrix = vectorizer.fit_transform(summaries)
 
-# Dimensionality Reduction with PCA
-pca = PCA(n_components=2)
+
+pca = PCA(n_components=2)  
 tfidf_pca = pca.fit_transform(tfidf_matrix.toarray())
 
-# Finding the Optimal Number of Clusters
 
 # Elbow Method
 sum_of_squared_distances = []
@@ -37,7 +63,7 @@
 plt.title('Elbow Method for Optimal k')
 plt.show()
 
-# Silhouette Score
+
 silhouette_avg = []
 for k in range(2, min(10, num_movies)):  # Adjust the range to be less than or equal to the number of samples
     kmeans = KMeans(n_clusters=k)
@@ -53,8 +79,7 @@
 plt.show()
 
 # Choose the optimal number of clusters
-optimal_k = 5  # Example value; replace with the best k determined from the plots
-
+optimal_k = 5 
 # K-means Clustering with Optimal k
 kmeans = KMeans(n_clusters=optimal_k)
 kmeans.fit(tfidf_pca)