Create app.py

app.py

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+import streamlit
+import pickle
+import numpy
+import sklearn
+ # web app  on your desktop local host
+#run  on your pycharm terminal ' streamlit run app.py '
+# if using  command window ensure the path is correct..  c:\users\idom...\pycharm..\machin learing>
+# that is pionting to your python file
+loaded_model = pickle.load(open('MWmodel.sav','rb'))
+#create function to handle predicition
+def microwave_fault_prediction (user_input_data):
+    #convert to array
+    Input_array = numpy.asarray(user_input_data)
+    Input_array_reshaped = Input_array.reshape(1, -1)
+    make_prediction = loaded_model.predict(Input_array_reshaped)
+    print(make_prediction)  # make_prediction= [10],  pos is 0
+
+    if make_prediction== 0:
+        return 'site  is up'
+    elif make_prediction == 1:
+        return'site  is down: fault: 1. inteference 2. misalignment 3. one of the odu is faulty'
+    elif make_prediction == 2:
+        return 'site  is down: fault: 1. NO power at remote site(A),2.ODU offline remote site(A)(check alarm \'IF cable open\') '
+    elif make_prediction == 3:
+        return 'site  is down: fault:1.ODu hunged at remote site(A), reset power at both sites(A,B)'
+    elif make_prediction == 4:
+        return 'site  is down: fault: 1. cascaded cable  faulty at hub  Site (B), 2.  ODU/IDU/If cable offline,at remote end'
+    elif make_prediction == 5:
+        return'site is down: fault: 1 ODU at hub site(B)degraded( reset ODU, reterminate IF cable,check alarm)'
+    elif make_prediction == 6:
+        return 'site  is down: faulty: if power is okay, odu burnt at either remote site (A) OR (B)'
+    else:  # do feature elimination for data irrelevant to outcome
+        return 'case 7: site status  cannot be determined by RSL data'
+
+#construct interface for user data input
+def main():
+    #give a title
+    streamlit.title('microwave fault detection web app')
+    #get input data from user
+    RSLA = streamlit.number_input('Site A Local end: enter RSL of the site, it must be negative number, input zero for no supervision',min_value=-99, max_value=0, value=-30, step=1,key= 'rsla')
+    #key= 'rslb' is to distinguish two similar widgets 'text_input' in streamlit
+    RSLB = streamlit.number_input('Site B Remote end: enter RSL of the Hub site, it  must be negative number, input zero for no supervision',min_value=-99, max_value=0, value=-30, step=1,key= 'rslb')
+    #code for prediction
+    detection =""   #declare this variable to hold result like empty list
+    #mylist = []
+    if streamlit.button('click here for  fault prediction'):
+        detection=microwave_fault_prediction([RSLA,RSLB])
+        #convert inputs into a single parameter using list [1,2]
+        #microwave_fault_prediction ...call the function to process input
+    streamlit.success(detection)
+if __name__ == '__main__':
+    main()
+
+
+ # web app  on your desktop local host
+#run  on your pycharm terminal ' streamlit run microwaveAPP.py '
+# if using  command window ensure the path is correct..  c:\users\idom...\pycharm..\machin learing>
+# that is pionting to your python file
+