Delete app.py

app.py

@@ -1,46 +0,0 @@
-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.")