Update app.py

app.py

@@ -29,7 +29,7 @@ def generate_graphs(new_story):
     new_story_vector = model.encode([new_story])[0]
 
     # Calculate the similarity with all existing stories in the knowledge base
-    knowledge_base_vectors = encoded_df.iloc[:, :-1].values  # Exclude 'likesCount'
+    knowledge_base_vectors = encoded_df.iloc[:, :-7].values  # Exclude 'likesCount'
     print(f"New Story Vector Shape: {new_story_vector.shape}")
     print(f"Knowledge Base Vector Shape: {knowledge_base_vectors.shape}")
     similarities = cosine_similarity([new_story_vector], knowledge_base_vectors)
@@ -47,7 +47,7 @@ def generate_graphs(new_story):
     sns.kdeplot([new_story_vector], shade=False, label="New Story", color='blue')
 
     for i in top_5_indices:
-        most_similar_vector = encoded_df.iloc[i, :-1].values
+        most_similar_vector = encoded_df.iloc[i, :-7].values
         sns.kdeplot(most_similar_vector, shade=False, label=f"Most Similar Story: {top_5_indices.tolist().index(i)+1}", alpha=0.5)
 
     plt.title("Similarity Distribution of New Story and Top 5 Similar Stories", fontsize=14)