Update app.py

app.py

@@ -3,6 +3,11 @@ from surprise import Dataset, Reader, SVD
 from surprise.model_selection import train_test_split
 import pandas as pd
 import numpy as np
+import json
+import os
+
+# Path to the JSON file to persist movie ratings
+RATINGS_FILE = 'movie_ratings.json'
 
 # Initialize sample user-item rating data
 data = {
@@ -18,13 +23,26 @@ trainset, testset = train_test_split(dataset, test_size=0.25)
 algo = SVD()
 algo.fit(trainset)
 
-# Initialize session state for storing user ratings
-if 'movie_ratings' not in st.session_state:
-    st.session_state.movie_ratings = {'Die Hard': [], 'Mad Max': [], 'The Shawshank Redemption': [], 'Forrest Gump': [],
-                                      'Superbad': [], 'Step Brothers': [], 'Inception': [], 'Interstellar': []}
+# Function to load ratings from a JSON file
+def load_ratings():
+    if os.path.exists(RATINGS_FILE):
+        with open(RATINGS_FILE, 'r') as file:
+            return json.load(file)
+    else:
+        return {movie: [] for movie in ['Die Hard', 'Mad Max', 'The Shawshank Redemption', 'Forrest Gump',
+                                        'Superbad', 'Step Brothers', 'Inception', 'Interstellar']}
+
+# Function to save ratings to a JSON file
+def save_ratings(ratings):
+    with open(RATINGS_FILE, 'w') as file:
+        json.dump(ratings, file)
+
+# Load ratings from file
+movie_ratings = load_ratings()
 
 def update_ratings(movie, rating):
-    st.session_state.movie_ratings[movie].append(rating)
+    movie_ratings[movie].append(rating)
+    save_ratings(movie_ratings)
 
 # UI Elements
 st.title("🌟 Personalized Entertainment Recommendations")
@@ -66,10 +84,11 @@ st.subheader("🎯 Recommendations for you:")
 all_movies = []
 for item_id, rating in predicted_ratings:
     for rec in recommendations[item_id]:
-        current_ratings = st.session_state.movie_ratings[rec]
+        current_ratings = movie_ratings[rec]
         avg_score = np.mean(current_ratings) if current_ratings else rating
+        vote_count = len(current_ratings)
 
-        st.write(f"{rec} - Average Score: {avg_score:.2f} ⭐")
+        st.write(f"{rec} - Average Score: {avg_score:.2f} ⭐ (Votes: {vote_count})")
 
         # Rating slider from 1 to 10
         user_rating = st.slider(f"Rate {rec}", 1, 10, 5, key=f"slider_{rec}")
@@ -77,15 +96,15 @@ for item_id, rating in predicted_ratings:
             update_ratings(rec, user_rating)
             st.experimental_rerun()  # Rerun to immediately reflect the rating submission
 
-        all_movies.append((rec, avg_score))
+        all_movies.append((rec, avg_score, vote_count))
 
 # Sort movies by average score in descending order
 sorted_movies = sorted(all_movies, key=lambda x: x[1], reverse=True)
 
 # Display scoreboard
 st.subheader("🏆 Scoreboard:")
-for movie, avg_score in sorted_movies:
-    st.write(f"{movie}: {avg_score:.2f} ⭐")
+for movie, avg_score, vote_count in sorted_movies:
+    st.write(f"{movie}: {avg_score:.2f} ⭐ (Votes: {vote_count})")
 
 # Display current session state (for debugging purposes)
-# st.write("Session State (User Ratings):", st.session_state.movie_ratings)
+# st.write("Session State (User Ratings):", movie_ratings)