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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
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")
@st.cache_resource
def load_model():
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()
@st.cache_data
def load_assets():
try:
df_movies = pd.read_csv(MOVIES_PATH)
except FileNotFoundError:
st.error("❌ movies.csv not found.")
st.stop()
try:
user_map, movie_map = joblib.load(ENCODINGS_PATH)
except FileNotFoundError:
st.error("❌ encodings.pkl not found.")
st.stop()
return df_movies, user_map, movie_map
# Load model and assets
model = load_model()
movies_df, user2idx, movie2idx = load_assets()
reverse_movie_map = {v: k for k, v in movie2idx.items()}
# App UI
st.title("🎬 TensorFlow Movie Recommender")
st.write("Select some movies you've liked to get personalized recommendations:")
# Movie selection UI
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))
# Create ratings dictionary
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()
# Get embedding averages and 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 N 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
# Display recommendations
st.subheader("🍿 Top 10 Recommendations")
for title, score in recommended:
st.write(f"**{title}** — Score: `{score:.3f}`")
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