Update src/app.py

src/app.py

@@ -32,7 +32,7 @@ st.set_page_config(page_title="Multi Search Engine", layout="wide")
 st.title("🔍 Advanced Multi-Search Product Engine")
 
 # ==============================
-# LOAD MODEL (CACHED)
+# LOAD MODEL
 # ==============================
 @st.cache_resource
 def load_model():
@@ -62,25 +62,19 @@ search_info = {
 }
 
 # ==============================
-# DATA SOURCE OPTION
+# DATA SOURCE
 # ==============================
-data_option = st.radio("📂 Choose Data Source", ["Sample Data", "Upload CSV"])
-
-if data_option == "Upload CSV":
-    uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
-
-    if uploaded_file is not None:
-        try:
-            df = pd.read_csv(uploaded_file)
-            st.success("✅ File uploaded successfully")
-        except Exception as e:
-            st.error(f"Error reading file: {e}")
-            st.stop()
-    else:
-        st.warning("⚠️ Upload a file or switch to Sample Data")
-        st.stop()
+data_option = st.radio("📂 Choose Data Source", ["Sample Data", "Default CSV (from repo)"])
+
+if data_option == "Default CSV (from repo)":
+    try:
+        df = pd.read_csv("products_sample.csv")
+        st.success("✅ Loaded dataset from repository")
+    except:
+        st.error("❌ products_sample.csv not found. Using sample data instead.")
+        data_option = "Sample Data"
 
-else:
+if data_option == "Sample Data":
     df = pd.DataFrame({
         "product_name": [
             "iPhone 14 Pro",
@@ -102,10 +96,18 @@ else:
     st.info("Using sample dataset")
 
 # ==============================
-# DATA PREVIEW
+# DATA PREVIEW (ROW CONTROL)
 # ==============================
 st.subheader("📄 Data Preview")
-st.dataframe(df.head())
+
+row_limit = st.selectbox(
+    "Select number of rows to view",
+    [5, 10, 20, 30, 50, 100],
+    index=1
+)
+
+st.caption(f"Showing top {row_limit} rows")
+st.dataframe(df.head(row_limit), use_container_width=True)
 
 # ==============================
 # COMBINE TEXT
@@ -120,7 +122,7 @@ df["combined"] = (
 products = df["combined"].tolist()
 
 # ==============================
-# CACHE PREPROCESSING
+# PREPROCESSING
 # ==============================
 @st.cache_resource
 def preprocess_data(products):
@@ -149,9 +151,6 @@ def get_synonyms(word):
             synonyms.add(lemma.name())
     return synonyms
 
-# ==============================
-# PREPROCESS
-# ==============================
 with st.spinner("⚙️ Processing data..."):
     tfidf, tfidf_matrix, embeddings, index, bm25 = preprocess_data(products)
 
@@ -245,7 +244,7 @@ def ensemble_search(q):
     return sorted(results.items(), key=lambda x: x[1], reverse=True)[:10]
 
 # ==============================
-# UI
+# SEARCH UI
 # ==============================
 search_type = st.selectbox("🔎 Select Search Type", list(search_info.keys()))
 
@@ -294,4 +293,4 @@ if st.button("Search"):
         result_df["Score"] = [score for _, score in results]
 
         st.subheader("🔎 Results")
-        st.dataframe(result_df)
+        st.dataframe(result_df, use_container_width=True)