Update app.py

app.py

@@ -1,28 +1,59 @@
 import streamlit as st
-from pymongo import MongoClient
+from pymongo import MongoClient, errors
 import pandas as pd
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# Load embedding model
+# =========================
+# ⚙️ CONFIGURATION
+# =========================
+
+MONGO_URI = (
+    "mongodb+srv://muthurajlingam788:VHassOJn4N4niYqg@cluster0.vgobnxf.mongodb.net/"
+    "?retryWrites=true&w=majority&tls=true"
+)
+
+DB_NAME = "shopping_mall"
+COLLECTION_NAME = "customers"
+
+# =========================
+# 🔐 CONNECT TO MONGODB
+# =========================
+
+def connect_to_mongo():
+    try:
+        client = MongoClient(MONGO_URI, serverSelectionTimeoutMS=20000)
+        client.admin.command("ping")  # Test connection
+        return client
+    except errors.ServerSelectionTimeoutError as err:
+        st.error("❌ Could not connect to MongoDB Atlas.")
+        st.stop()
+
+client = connect_to_mongo()
+db = client[DB_NAME]
+collection = db[COLLECTION_NAME]
+
+# =========================
+# 🤖 LOAD EMBEDDING MODEL
+# =========================
+
 @st.cache_resource
 def load_model():
     return SentenceTransformer("all-MiniLM-L6-v2")
 
 model = load_model()
 
-# MongoDB connection
-MONGO_URI = "mongodb+srv://muthurajlingam788:VHassOJn4N4niYqg@cluster0.vgobnxf.mongodb.net/mydatabase?retryWrites=true&w=majority"
-client = MongoClient(MONGO_URI)
-db = client["shopping_mall"]
-collection = db["customers"]
+# =========================
+# 🧠 STREAMLIT INTERFACE
+# =========================
 
-# Streamlit UI
 st.title("🛍️ Shopping Mall Customer Management")
-
 menu = st.sidebar.radio("Menu", ["Add Customer", "View Customers", "Ask AI"])
 
-# Insert new customer
+# =========================
+# ➕ ADD CUSTOMER
+# =========================
+
 if menu == "Add Customer":
     st.header("Add Customer Details")
     name = st.text_input("Name")
@@ -32,20 +63,26 @@ if menu == "Add Customer":
     amount = st.number_input("Total Amount Spent (₹)", min_value=0.0)
 
     if st.button("Submit"):
-        customer = {
-            "name": name,
-            "age": age,
-            "gender": gender,
-            "items": [item.strip() for item in items.split(",")],
-            "amount": amount
-        }
-        collection.insert_one(customer)
-        st.success("Customer added successfully!")
-
-# View customers
+        if name and items:
+            customer = {
+                "name": name,
+                "age": age,
+                "gender": gender,
+                "items": [item.strip() for item in items.split(",")],
+                "amount": amount
+            }
+            collection.insert_one(customer)
+            st.success("✅ Customer added successfully!")
+        else:
+            st.warning("Please enter at least Name and Items.")
+
+# =========================
+# 📋 VIEW CUSTOMERS
+# =========================
+
 elif menu == "View Customers":
     st.header("Customer List")
-    data = list(collection.find({}, {"_id": 0}))  # Exclude _id field
+    data = list(collection.find({}, {"_id": 0}))  # Exclude _id
 
     if data:
         df = pd.DataFrame(data)
@@ -56,23 +93,24 @@ elif menu == "View Customers":
     else:
         st.info("No customer records found.")
 
-# AI Query Assistant
+# =========================
+# 🧠 AI QUERY ASSISTANT
+# =========================
+
 elif menu == "Ask AI":
     st.header("🧠 Ask Questions About Customers")
     question = st.text_input("Enter your question")
 
-    # Load and format data
     data = list(collection.find({}, {"_id": 0}))
+
     if not data:
         st.warning("No data available to query.")
-    else:
-        context_texts = []
-        for c in data:
-            context_texts.append(
-                f"{c['name']} is a {c['age']}-year-old {c['gender']} who bought {', '.join(c['items'])} and spent ₹{c['amount']}."
-            )
+    elif question:
+        context_texts = [
+            f"{c['name']} is a {c['age']}-year-old {c['gender']} who bought {', '.join(c['items'])} and spent ₹{c['amount']}."
+            for c in data
+        ]
 
-        # Encode and search best match
         question_embedding = model.encode([question])
         context_embeddings = model.encode(context_texts)
 
@@ -80,6 +118,5 @@ elif menu == "Ask AI":
         best_idx = scores.argmax()
         response = context_texts[best_idx]
 
-        if question:
-            st.write("📌 Most Relevant Info:")
-            st.success(response)
+        st.write("📌 Most Relevant Info:")
+        st.success(response)