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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import tensorflow as tf | |
| import joblib | |
| import os | |
| # Define file paths (assuming all files are in the same folder as this script) | |
| BASE_DIR = os.path.dirname(__file__) | |
| KERAS_MODEL_PATH = os.path.join(BASE_DIR, "recommender_model.keras") | |
| MOVIES_PATH = os.path.join(BASE_DIR, "movies.csv") | |
| ENCODINGS_PATH = os.path.join(BASE_DIR, "encodings.pkl") | |
| def load_model(): | |
| if not os.path.exists(KERAS_MODEL_PATH): | |
| st.error(f"❌ Model file not found at: {KERAS_MODEL_PATH}") | |
| st.stop() | |
| try: | |
| return tf.keras.models.load_model(KERAS_MODEL_PATH) | |
| except Exception as e: | |
| st.error(f"❌ Failed to load model:\n\n{e}") | |
| st.stop() | |
| def load_assets(): | |
| if not os.path.exists(MOVIES_PATH): | |
| st.error("❌ movies.csv not found.") | |
| st.stop() | |
| if not os.path.exists(ENCODINGS_PATH): | |
| st.error("❌ encodings.pkl not found.") | |
| st.stop() | |
| try: | |
| df_movies = pd.read_csv(MOVIES_PATH) | |
| user_map, movie_map = joblib.load(ENCODINGS_PATH) | |
| return df_movies, user_map, movie_map | |
| except Exception as e: | |
| st.error(f"❌ Failed to load assets:\n\n{e}") | |
| st.stop() | |
| # Load model and assets | |
| model = load_model() | |
| movies_df, user2idx, movie2idx = load_assets() | |
| reverse_movie_map = {v: k for k, v in movie2idx.items()} | |
| # UI | |
| st.title("🎬 TensorFlow Movie Recommender") | |
| st.write("Select some movies you've liked to get personalized recommendations:") | |
| # Movie title selection | |
| movie_titles = movies_df.set_index("movieId")["title"].to_dict() | |
| movie_choices = [movie_titles[mid] for mid in movie2idx if mid in movie_titles] | |
| selected_titles = st.multiselect("🎞️ Liked movies", sorted(movie_choices)) | |
| # User ratings dict | |
| user_ratings = {} | |
| for title in selected_titles: | |
| movie_id = next((k for k, v in movie_titles.items() if v == title), None) | |
| if movie_id: | |
| user_ratings[movie_id] = 5.0 | |
| # Generate recommendations | |
| if st.button("🎯 Get Recommendations"): | |
| if not user_ratings: | |
| st.warning("Please select at least one movie.") | |
| else: | |
| liked_indices = [movie2idx[m] for m in user_ratings if m in movie2idx] | |
| if not liked_indices: | |
| st.error("⚠️ No valid movie encodings found.") | |
| st.stop() | |
| try: | |
| # Calculate average embedding and similarity scores | |
| avg_embedding = tf.reduce_mean(model.layers[2](tf.constant(liked_indices)), axis=0, keepdims=True) | |
| all_movie_indices = tf.range(len(movie2idx)) | |
| movie_embeddings = model.layers[3](all_movie_indices) | |
| scores = tf.reduce_sum(avg_embedding * movie_embeddings, axis=1).numpy() | |
| top_indices = np.argsort(scores)[::-1] | |
| # Top 10 recommendations excluding already liked | |
| recommended = [] | |
| for idx in top_indices: | |
| mid = reverse_movie_map.get(idx) | |
| if mid not in user_ratings and mid in movie_titles: | |
| recommended.append((movie_titles[mid], scores[idx])) | |
| if len(recommended) >= 10: | |
| break | |
| st.subheader("🍿 Top 10 Recommendations") | |
| for title, score in recommended: | |
| st.write(f"**{title}** — Score: `{score:.3f}`") | |
| except Exception as e: | |
| st.error(f"❌ Error generating recommendations:\n\n{e}") | |