app.py

import streamlit as st
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 = {
    'user_id': [1, 1, 1, 2, 2, 3, 3, 4, 4],
    'item_id': [1, 2, 3, 1, 4, 2, 3, 1, 4],
    'rating': [5, 3, 4, 4, 5, 5, 2, 4, 4]
}
df = pd.DataFrame(data)
reader = Reader(rating_scale=(1, 5))
dataset = Dataset.load_from_df(df[['user_id', 'item_id', 'rating']], reader)
trainset, testset = train_test_split(dataset, test_size=0.25)

algo = SVD()
algo.fit(trainset)

# 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):
    movie_ratings[movie].append(rating)
    save_ratings(movie_ratings)

# UI Elements
st.title("🌟 Personalized Entertainment Recommendations")

# Input for user genre preference
user_genre = st.selectbox(
    "Choose your favorite genre", 
    ["Action 🎬", "Drama 🎭", "Comedy 😂", "Sci-Fi 🚀"]
)

# Define genre mapping
genre_mapping = {
    "Action 🎬": 1,
    "Drama 🎭": 2,
    "Comedy 😂": 3,
    "Sci-Fi 🚀": 4
}

# Example mapping of genre to item_id
recommendations = {
    1: ["Die Hard", "Mad Max"], 
    2: ["The Shawshank Redemption", "Forrest Gump"],
    3: ["Superbad", "Step Brothers"], 
    4: ["Inception", "Interstellar"]
}

# Provide recommendation based on collaborative filtering
selected_genre_id = genre_mapping[user_genre]

# Predict ratings for the selected genre items
predicted_ratings = [(id, algo.predict(5, id).est) for id in [1, 2, 3, 4] if id == selected_genre_id]

# Sort recommendations by predicted ratings
predicted_ratings.sort(key=lambda x: x[1], reverse=True)

st.subheader("🎯 Recommendations for you:")

# Collect average scores
all_movies = []
for item_id, rating in predicted_ratings:
    for rec in recommendations[item_id]:
        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} ⭐ (Votes: {vote_count})")

        # Rating slider from 1 to 10
        user_rating = st.slider(f"Rate {rec}", 1, 10, 5, key=f"slider_{rec}")
        if st.button(f"Submit Rating for {rec}", key=f"button_{rec}"):
            update_ratings(rec, user_rating)
            st.experimental_rerun()  # Rerun to immediately reflect the rating submission

        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, 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):", movie_ratings)