Upload 3 files

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+
+st.title("Machine Failure")
+model = pickle.load(open("machine.pkl", "rb"))
+
+Type = st.number_input("Type(L:3, M:2, H:1)")
+Airtemperature = st.number_input("Air temperature [K]")
+Processtemperature = st.number_input("Process temperature [K]")
+Rotationalspeed = st.number_input("Rotational speed [rpm]")
+Torque = st.number_input("Torque [Nm]")
+Toolwear = st.number_input("Tool wear [min]")
+TWF = st.number_input("TWF")
+HDF = st.number_input("HDF")
+PWF = st.number_input("PWF")
+OSF = st.number_input("OSF")
+RNF = st.number_input("RNF")
+
+
+if st.button("Tahmin Et"):
+    tahmin = model.predict([[Type,Airtemperature,Processtemperature,Rotationalspeed,Torque,Toolwear,
+                            TWF,HDF,PWF,OSF,RNF]])
+    st.write("Tahmin Sonucu:", tahmin[0])  # İlk tahmin sonucunu yazdır

machine.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:049ece9e5052f3b6ccda5a9e578804191d01f89c8cc37567c27d3660f7d19ec3
+size 24892796

requirements.txt

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+streamlit
+scikit-learn