Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,55 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+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}")