import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from fastapi import FastAPI
import joblib

def show_recommendations(product):
    Y = vectorizer.transform([product])
    prediction = model.predict(Y)
    return prediction

def print_cluster(i):
    for ind in order_centroids[i, :10]:
        print(' %s' % terms[ind]),

def get_cluster_terms(cluster_index):
    cluster_terms = [terms[ind] for ind in order_centroids[cluster_index, :10]]
    return cluster_terms

model = joblib.load("./model.pkl")
vectorizer = joblib.load("./vectorizer.pkl")

order_centroids = model.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names_out()

st.title("Product Recommendation System")

# Input for product description
product_input = st.text_input("Enter a product description:", "")

# Button to trigger recommendation
if st.button("Get Recommendations"):
    if product_input:
        # Get cluster for the input product
        cluster_index = show_recommendations(product_input)[0]
        
        # Display the cluster number
        st.write(f"The product belongs to cluster: {cluster_index}")
        
        # Display the top terms in the cluster
        cluster_terms = get_cluster_terms(cluster_index)
        st.write("Top terms in this cluster:")
        st.write(", ".join(cluster_terms))
    else:
        st.write("Please enter a product description.")