Update app.py

app.py

@@ -7,7 +7,7 @@ from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE
 model = gensim.downloader.load("word2vec-google-news-300") #glove-wiki-gigaword-50
 
-cache = "/home/user/app/d.tif"
+cache = "/home/user/app/d.png"
 
 # Function to reduce dimensions
 def reduce_dimensions(data, method='PCA'):
@@ -29,7 +29,7 @@ def plot_reduced_data(reduced_data, labels, title):
     plt.xlabel('Component 1')
     plt.ylabel('Component 2')
     plt.grid(True)
-    plt.savefig(cache, dpi=300)
+    plt.savefig(cache) #, dpi=300)
 
 description = """
 ### Word Embedding Demo App