Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,27 +1,13 @@
 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)
+# Define file paths (assuming 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")
 
-@st.cache_resource
-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()
-
 @st.cache_data
 def load_assets():
     if not os.path.exists(MOVIES_PATH):
@@ -38,56 +24,25 @@ def load_assets():
         st.error(f"❌ Failed to load assets:\n\n{e}")
         st.stop()
 
-# Load model and assets
-model = load_model()
+# Load only static data
 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:")
+st.title("🎬 TensorFlow Movie Recommender (Mock Version)")
+st.write("This is a dummy version of the app without model logic. Select movies to simulate 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:
+# Simulate recommendations
+if st.button("🎯 Get Recommendations (Dummy)"):
+    if not selected_titles:
         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}")
+        st.subheader("🍿 Mock Recommendations")
+        st.write("Here are some movies we 'think' you might like... 😉")
+        mock_recommendations = sorted(set(movie_choices) - set(selected_titles))[:10]
+        for i, title in enumerate(mock_recommendations, 1):
+            st.write(f"**#{i}** — {title}")