import streamlit as st
from pymongo import MongoClient, errors
import pandas as pd
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# =========================
# ⚙️ 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()

# =========================
# 🧠 STREAMLIT INTERFACE
# =========================

st.title("🛍️ Shopping Mall Customer Management")
menu = st.sidebar.radio("Menu", ["Add Customer", "View Customers", "Ask AI"])

# =========================
# ➕ ADD CUSTOMER
# =========================

if menu == "Add Customer":
    st.header("Add Customer Details")
    name = st.text_input("Name")
    age = st.number_input("Age", min_value=0)
    gender = st.selectbox("Gender", ["Male", "Female", "Other"])
    items = st.text_input("Items Purchased (comma-separated)")
    amount = st.number_input("Total Amount Spent (₹)", min_value=0.0)

    if st.button("Submit"):
        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

    if data:
        df = pd.DataFrame(data)
        search = st.text_input("Search by Name")
        if search:
            df = df[df["name"].str.contains(search, case=False)]
        st.dataframe(df)
    else:
        st.info("No customer records found.")

# =========================
# 🧠 AI QUERY ASSISTANT
# =========================

elif menu == "Ask AI":
    st.header("🧠 Ask Questions About Customers")
    question = st.text_input("Enter your question")

    data = list(collection.find({}, {"_id": 0}))

    if not data:
        st.warning("No data available to query.")
    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
        ]

        question_embedding = model.encode([question])
        context_embeddings = model.encode(context_texts)

        scores = cosine_similarity(question_embedding, context_embeddings)[0]
        best_idx = scores.argmax()
        response = context_texts[best_idx]

        st.write("📌 Most Relevant Info:")
        st.success(response)