Create app.py

app.py

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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
+
+# Sample user-item rating data for collaborative filtering
+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)
+
+# 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:")
+for item_id, rating in predicted_ratings:
+    for rec in recommendations[item_id]:
+        st.write(f"{rec} - Predicted Rating: {rating:.2f} ⭐")
+
+# Provide visual feedback for actions
+if predicted_ratings:
+    st.button("👍 Like", key="like_button")
+    st.button("👎 Dislike", key="dislike_button")
+else:
+    st.write("No recommendations available for your selected genre.")