Upload 5 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+recommender_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,55 @@
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import joblib
+
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model("recommender_model.keras")
+
+@st.cache_data
+def load_assets():
+    df_movies = pd.read_csv("movies.csv")
+    user_map, movie_map = joblib.load("encodings.pkl")
+    return df_movies, user_map, movie_map
+
+model = load_model()
+movies_df, user2idx, movie2idx = load_assets()
+reverse_movie_map = {v: k for k, v in movie2idx.items()}
+
+st.title("TensorFlow Movie Recommender")
+st.write("Select some movies you've liked to get recommendations:")
+
+movie_titles = movies_df.set_index("movieId")["title"].to_dict()
+movie_choices = [movie_titles[mid] for mid in movie2idx.keys() if mid in movie_titles]
+selected_titles = st.multiselect("Liked movies", sorted(movie_choices))
+
+user_ratings = {}
+for title in selected_titles:
+    movie_id = [k for k, v in movie_titles.items() if v == title][0]
+    user_ratings[movie_id] = 5.0
+
+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]
+        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]
+
+        recommended = []
+        for idx in top_indices:
+            mid = reverse_movie_map[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 Recommendations")
+        for title, score in recommended:
+            st.write(f"{title} — Score: {score:.3f}")

encodings.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e5618ac303f871d6fd0f81aa96848b0b52d722566b829b3dcbdd6a0d83ee771
+size 5069676

recommender_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d08c6ecc96d18ac1f75494bcae2ef6fba657bd3ee02175dc7c05176ba1cc143
+size 4121755

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+tensorflow
+pandas
+numpy
+joblib