Update app.py

app.py

@@ -0,0 +1,109 @@
+import streamlit as st
+import pandas as pd
+from config.db import get_collection
+from utils.text_cleaner import clean_text
+from utils.search import rank_results
+
+st.set_page_config(page_title="Expense Search System", layout="wide")
+st.title("🔍 Expense Search Dashboard")
+
+# --- 1️⃣ Input/Search Layer ---
+user_input = st.text_input("Search (Date / Company / Remark / Description / Amount)")
+
+if user_input:
+    clean_input = clean_text(user_input)
+    collection = get_collection()
+
+    # --- Build query WITHOUT $text to avoid MongoDB error ---
+    query = {
+        "$or": [
+            {"date": {"$regex": clean_input, "$options": "i"}},  # Date search
+            {"home company": {"$regex": clean_input, "$options": "i"}},  # Company search
+            {"description": {"$regex": clean_input, "$options": "i"}},  # Description search
+            {"remarks": {"$regex": clean_input, "$options": "i"}},  # Remarks search
+            {"description_clean": {"$regex": clean_input, "$options": "i"}},  # Cleaned description
+        ]
+    }
+    
+    # Try to match amount if input is numeric
+    try:
+        amount_value = float(clean_input.replace(",", ""))
+        query["$or"].append({"amount": amount_value})
+    except ValueError:
+        pass  # Not a number, skip amount matching
+
+    # --- Fetch ALL matching records from MongoDB ---
+    docs = list(collection.find(
+        query,
+        {
+            "date": 1,
+            "description": 1,
+            "remarks": 1,
+            "amount": 1,
+            "home company": 1,
+            "description_clean": 1
+        }
+    ))  # Removed limit to fetch ALL records
+
+    if docs:
+        # --- Rank using fuzzy search ---
+        ranked = rank_results(clean_input, docs)
+        df = pd.DataFrame(ranked)
+        df = df[['date', 'description', 'remarks', 'home company', 'amount']]
+        df.rename(columns={"home company": "Company"}, inplace=True)
+
+        # --- 2️⃣ Quick Summary Metrics ---
+        total_transactions = len(df)
+        total_amount = df['amount'].sum()
+        inward_total = df[df['remarks'].str.lower().str.contains("inward", na=False)]['amount'].sum()
+        outward_total = df[df['remarks'].str.lower().str.contains("outward", na=False)]['amount'].sum()
+        net_total = inward_total - outward_total
+
+        st.subheader("📊 Quick Summary")
+        c1, c2, c3, c4, c5 = st.columns(5)
+        c1.metric("Transactions", total_transactions)
+        c2.metric("Total Amount", f"{total_amount:,.2f}")
+        c3.metric("Inward", f"{inward_total:,.2f}")
+        c4.metric("Outward", f"{outward_total:,.2f}")
+        c5.metric("Net Total", f"{net_total:,.2f}")
+
+        # --- 3️⃣ Matched Transactions Table ---
+        st.subheader("🧾 Matched Transactions")
+        st.dataframe(df, use_container_width=True)
+
+        # --- 4️⃣ Remarks Breakup ---
+        st.subheader("🏷️ Remarks Breakup")
+        remarks_summary = df.groupby('remarks')['amount'].agg(['count', 'sum']).reset_index()
+        remarks_summary.rename(columns={"count": "Transactions", "sum": "Total Amount", "remarks": "Remark"}, inplace=True)
+        st.table(remarks_summary)
+
+        # --- 5️⃣ Company Involvement ---
+        st.subheader("🏢 Company Summary")
+        company_summary = df.groupby('Company')['amount'].agg(['count', 'sum']).reset_index()
+        company_summary.rename(columns={"count": "Transactions", "sum": "Total Amount"}, inplace=True)
+        st.table(company_summary)
+
+        # --- 6️⃣ Date Summary (if multiple dates present) ---
+        st.subheader("📅 Date-wise Summary")
+        date_summary = df.groupby('date')['amount'].agg(['count', 'sum']).reset_index()
+        date_summary.rename(columns={"count": "Transactions", "sum": "Total Amount", "date": "Date"}, inplace=True)
+        st.table(date_summary)
+
+        # --- 7️⃣ Optional Visuals ---
+        st.subheader("📊 Visual Insights")
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.markdown("**🥧 Inward vs Outward Pie Chart**")
+            pie_data = pd.DataFrame({
+                "Type": ["Inward", "Outward"],
+                "Amount": [inward_total, outward_total]
+            })
+            st.bar_chart(pie_data.set_index("Type"))
+
+        with col2:
+            st.markdown("**📊 Amount by Company**")
+            st.bar_chart(company_summary.set_index("Company")["Total Amount"])
+
+    else:
+        st.warning("No confident match found")