Update app.py

app.py

@@ -37,17 +37,10 @@ def generate_graphs(new_story):
     # Find the indices of the 5 most similar stories
     top_5_indices = np.argsort(similarities[0])[::-1][:5]  # Sort similarities and get top 5
 
-    # Retrieve the LikesCount for the top 5 most similar stories
-    likes_distribution = encoded_df.iloc[top_5_indices]['likesCount'].values
-    # sorted_likes_distribution = sorted(likes_distribution, reverse=True)
-    
-    # Create a bar graph for the distribution of the 5 most similar stories
-    plt.figure(figsize=(10, 6))
-    sns.barplot(x=[f"Story {i+1}" for i in range(5)], y=likes_distribution, palette="viridis")
-    plt.title("LikesCount Distribution for the 5 Most Similar Stories", fontsize=14)
-    plt.xlabel("Story Similarity (Most Similar to Least)", fontsize=12)
-    plt.ylabel("LikesCount", fontsize=12)
-    likes_dist_plot = plt.gcf()
+    likes_distribution=[]
+    for i in top_5_indices:
+        print(f"Row {i+1}: Similarity = {similarities[0][i]:.4f}, LikesCount = {encoded_df.iloc[i]['likesCount']}")
+        likes_distribution.append(encoded_df.iloc[i]['likesCount'].astype(int))
 
     # Plot the similarity distribution for the 5 most similar stories
     plt.figure(figsize=(10, 6))
@@ -62,6 +55,17 @@ def generate_graphs(new_story):
     plt.ylabel("Density", fontsize=12)
     plt.legend(title="Stories")
     sim_dist_plot = plt.gcf()
+        
+    # Create a bar graph for the distribution of the 5 most similar stories
+    # top_5_stories = [0,1,2,3,4]
+    plt.figure(figsize=(10, 6))
+    sns.barplot(x=[f"Story {i+1}" for i in range(5)], y=likes_distribution, palette="viridis")
+    plt.title("LikesCount Distribution for the 5 Most Similar Stories", fontsize=14)
+    plt.xlabel("Story Similarity (Most Similar to Least)", fontsize=12)
+    plt.ylabel("LikesCount", fontsize=12)
+    likes_dist_plot = plt.gcf()
+
+
 
     return sim_dist_plot,likes_dist_plot