Update app.py

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+from search_utils import SemanticSearch
+
+@st.cache_resource
+def init_search_system():
+    search_system = SemanticSearch(shard_dir="compressed_shards")
+    search_system.initialize_system()
+    return search_system
+
+st.set_page_config(page_title="Research Paper Semantic Search with FAISS", layout="wide")
+search_system = init_search_system()
+
+st.title("🔍 Research Paper Semantic Search")
+col1, col2 = st.columns([3, 1])
+
+with col1:
+    query = st.text_input("Search query:", placeholder="Enter your search...")
+
+with col2:
+    top_k = st.slider("Results count:", 1, 20, 5)
+    threshold = st.slider("Similarity threshold:", 0.0, 1.0, 0.6)
+
+if query:
+    with st.spinner("Searching through documents..."):
+        base_results = search_system.search(query, top_k)
+        threshold_results = search_system.search_with_threshold(query, top_k, threshold)
+        
+    st.subheader("Top Matches")
+    st.dataframe(
+        base_results.style.format({'similarity': "{:.2%}"}),
+        use_container_width=True,
+        hide_index=True
+    )
+    
+    st.subheader(f"Filtered (>{threshold:.0%} similarity)")
+    if not threshold_results.empty:
+        st.dataframe(
+            threshold_results.style.format({'similarity': "{:.2%}"}),
+            use_container_width=True,
+            hide_index=True
+        )
+    else:
+        st.warning("No results meet the similarity threshold")
+
+with st.sidebar:
+    st.header("System Info")
+    st.markdown(f"""
+    - **Loaded Shards:** {len(search_system.index_shards)}
+    - **Embedding Model:** `all-MiniLM-L6-v2`
+    """)
+    
+    if st.button("Clear Cache"):
+        st.cache_resource.clear()
+        st.rerun()