Implement IntelliFlix: Semantic Movie Recommender app with Streamlit and model loading

app.py

@@ -51,7 +51,7 @@ def load_model_and_data():
         # Load files from Hugging Face Hub
         with st.spinner("Loading movie data..."):
             csv_path = hf_hub_download(
-                repo_id=REPO_ID, 
+                repo_id=REPO_ID,
                 filename="data/tmdb_movies_dataset_processed.csv",
                 repo_type="dataset",
                 cache_dir='/tmp/huggingface'
@@ -60,7 +60,7 @@ def load_model_and_data():
 
         with st.spinner("Loading embeddings..."):
             embeddings_path = hf_hub_download(
-                repo_id=REPO_ID, 
+                repo_id=REPO_ID,
                 filename="embeddings/movie_ovierview_embeddings.npy",
                 repo_type="dataset",
                 cache_dir='/tmp/huggingface'
@@ -69,7 +69,7 @@ def load_model_and_data():
 
         with st.spinner("Loading FAISS index..."):
             faiss_path = hf_hub_download(
-                repo_id=REPO_ID, 
+                repo_id=REPO_ID,
                 filename="indexes/movie_overview_index.faiss",
                 repo_type="dataset",
                 cache_dir='/tmp/huggingface'
@@ -139,6 +139,6 @@ if query:
 # Footer
 st.markdown("---")
 st.markdown("""
-    **Built with** ❤️ **by [Arghya Ghosh](https://arghya.dev)**  
+    **Built with** ❤️ **by [Arghya Ghosh](https://arghya.dev)**
     *Technologies used: FAISS + Sentence Transformers + Streamlit*
 """)