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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
# 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)
# 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': []}
def update_ratings(movie, rating):
st.session_state.movie_ratings[movie].append(rating)
# 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 = st.session_state.movie_ratings[rec]
avg_score = np.mean(current_ratings) if current_ratings else rating
st.write(f"{rec} - Average Score: {avg_score:.2f} ⭐")
# 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))
# 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} ⭐")
# Display current session state (for debugging purposes)
# st.write("Session State (User Ratings):", st.session_state.movie_ratings) |