Upload app.py

app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+import pandas as pd
+import pickle
+
+loaded_model = pickle.load(open("finalized_model.sav", 'rb'))
+
+
+def main():
+    st.image('img.jpg')
+    st.title("⚙️🔩 Engine prediction ⚙️🔩")
+    st.warning("Our Machine Learning algorithm predicts whether the elements of a machine work consistently\n\n")
+
+    with st.form(key='columns_in_form'):
+        c1, c2, c3 = st.beta_columns(3)
+        with c1:
+            airTemperature = st.slider("Air temperature [K]", 0, 1500, 750)
+        with c2:
+            processTemperatire = st.slider(
+                "Process temperature [K]", 0, 1500, 750)
+        with c3:
+            rotationSpeed = st.slider(
+                "Rotational speed [rpm]", 0, 1500, 750)
+            submitButton1 = st.form_submit_button(label='Save')
+    with st.form(key='columns_in_form2'):
+        c1, c2, c3, c4 = st.beta_columns(4)
+        with c1:
+            toolWear = st.slider("Tool wear [min]", 0, 1500, 750)
+        with c2:
+            typeL = st.select_slider('Type_L', options=[0, 1])
+        with c3:
+            typeM = st.select_slider('Type_M', options=[0, 1])
+        with c4:
+            torqueNm = st.select_slider('Torque [Nm]', options=[0, 300])
+            submitButton2 = st.form_submit_button(label='Calculate')
+    if (submitButton2):
+        d = {'Air temperature [K]': airTemperature, 'Process temperature [K]': processTemperatire,
+             'Rotational speed [rpm]': rotationSpeed, "Torque [Nm]": torqueNm, "Tool wear [min]": toolWear, "Type_L": typeL, "Type_M": typeM}
+        ser = pd.Series(data=d, index=['Air temperature [K]', 'Process temperature [K]',
+                        'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]', 'Type_L', 'Type_M'])
+        
+        res = loaded_model.predict([ser])
+        if (res[0] == 0):
+            st.success("The machine is in good condition")
+        else:
+            st.error("The machine seems to have problems")
+
+
+
+if __name__ == '__main__':
+    main()