Upload 3 files

app.py

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+import streamlit as st
+import numpy as np
+import joblib
+
+html_temp = """
+    <div style="background-color:black;padding:10px">
+    <h2 style="color:white;text-align:center;">Customer Clustering App </h2>
+    </div>
+    """
+st.markdown(html_temp, unsafe_allow_html=True)
+
+image_url="https://tse4.mm.bing.net/th?id=OIP.Sri9wahqIBdLwFTc85-tvgHaEK&pid=Api&P=0&h=180"
+
+st.image(image_url, caption="Image from URL", use_container_width=True)
+
+km_model=joblib.load("coffe_customer_prediction_model.joblib")
+
+def clustering(monetary, frequency):
+    new_customer = np.array([[monetary, frequency]])
+    predicted_cluster = km_model.predict(new_customer)
+    
+    if predicted_cluster[0]==1:
+        return "Daily"
+    else:
+        return "Weekely"
+    
+monetary=float(st.slider("Slide Size of the monetary",min_value=1,max_value=459,value=1))
+
+frequency=float(st.slider("Slide Size of the frequency",min_value=1,max_value=25,value=1))
+
+if st.button("Submit"):
+    predicted_cluster = clustering(monetary, frequency)
+
+    st.write(f'New Customer assigned to Cluster: {predicted_cluster}')

coffe_customer_prediction_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:980a5db97ce427442ac3b5a8ab752d6aca07127e26e286965f5506a97bd7249a
+size 10083

requirements.txt

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+joblib==1.2.0
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+numpy==1.26.4
+pandas==1.5.3
+scikit-learn==1.6.0
+streamlit==1.41.1