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Browse files![Telecom Machine learning app.png](https://s3.amazonaws.com/moonup/production/uploads/1675423852049-63dcdf5322cc06e76a84d6c6.png)
- RSL_copy.csv +379 -0
- app.py +59 -0
- trained_lrmodel.sav +0 -0
RSL_copy.csv
ADDED
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1 |
+
terminal _A_ site_ RSl,Hub_B_site_RSL,outcome
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+
-32,-30,0
|
327 |
+
-22,-39,5
|
328 |
+
0,0,4
|
329 |
+
-78,-76,1
|
330 |
+
0,0,4
|
331 |
+
-41,-71,1
|
332 |
+
-49,-56,1
|
333 |
+
-59,-75,1
|
334 |
+
0,0,4
|
335 |
+
-32,-31,0
|
336 |
+
-51,-42,1
|
337 |
+
-44,-72,1
|
338 |
+
-22,-34,5
|
339 |
+
-76,-71,1
|
340 |
+
-64,-57,1
|
341 |
+
0,-75,2
|
342 |
+
0,0,4
|
343 |
+
0,-84,2
|
344 |
+
0,0,4
|
345 |
+
-76,-47,1
|
346 |
+
-68,-78,1
|
347 |
+
-68,-48,1
|
348 |
+
-77,-63,1
|
349 |
+
-54,-70,1
|
350 |
+
0,-83,2
|
351 |
+
-75,-70,1
|
352 |
+
-34,-39,0
|
353 |
+
-32,-39,0
|
354 |
+
-70,-54,1
|
355 |
+
-54,-42,1
|
356 |
+
-72,-44,1
|
357 |
+
-48,-72,1
|
358 |
+
-63,-48,1
|
359 |
+
-76,-70,1
|
360 |
+
0,0,4
|
361 |
+
-67,-43,1
|
362 |
+
0,-84,2
|
363 |
+
0,0,4
|
364 |
+
-43,-55,1
|
365 |
+
-37,-38,0
|
366 |
+
-31,-37,0
|
367 |
+
-64,-45,1
|
368 |
+
-80,-61,1
|
369 |
+
0,-81,2
|
370 |
+
-51,-56,1
|
371 |
+
-71,-73,1
|
372 |
+
-53,-44,1
|
373 |
+
-56,-60,1
|
374 |
+
-56,-61,1
|
375 |
+
-48,-54,1
|
376 |
+
-38,-33,0
|
377 |
+
0,-33,3
|
378 |
+
0,-82,2
|
379 |
+
-69,-71,1
|
app.py
ADDED
@@ -0,0 +1,59 @@
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|
|
1 |
+
import streamlit
|
2 |
+
import pickle
|
3 |
+
import numpy
|
4 |
+
import sklearn
|
5 |
+
# web app on your desktop local host
|
6 |
+
#run on your pycharm terminal ' streamlit run app.py '
|
7 |
+
# if using command window ensure the path is correct.. c:\users\idom...\pycharm..\machin learing>
|
8 |
+
# that is pionting to your python file
|
9 |
+
loaded_model = pickle.load(open('trained_lrmodel.sav','rb'))
|
10 |
+
#create function to handle predicition
|
11 |
+
def microwave_fault_prediction (user_input_data):
|
12 |
+
#convert to array
|
13 |
+
Input_array = numpy.asarray(user_input_data)
|
14 |
+
Input_array_reshaped = Input_array.reshape(1, -1)
|
15 |
+
make_prediction = loaded_model.predict(Input_array_reshaped)
|
16 |
+
print(make_prediction) # make_prediction= [10], pos is 0
|
17 |
+
|
18 |
+
if make_prediction== 0:
|
19 |
+
return 'site is up'
|
20 |
+
elif make_prediction == 1:
|
21 |
+
return'site is down: fault: 1. inteference 2. misalignment 3. one of the odu is faulty'
|
22 |
+
elif make_prediction == 2:
|
23 |
+
return 'site is down: fault: 1. NO power at remote site(A),2.ODU offline remote site(A)(check alarm \'IF cable open\') '
|
24 |
+
elif make_prediction == 3:
|
25 |
+
return 'site is down: fault:1.ODu hunged at remote site(A), reset power at both sites(A,B)'
|
26 |
+
elif make_prediction == 4:
|
27 |
+
return 'site is down: fault: 1. cascaded cable faulty at hub Site (B), 2. ODU/IDU/If cable offline,at remote end'
|
28 |
+
elif make_prediction == 5:
|
29 |
+
return'site is down: fault: 1 ODU at hub site(B)degraded( reset ODU, reterminate IF cable,check alarm)'
|
30 |
+
elif make_prediction == 6:
|
31 |
+
return 'site is down: faulty: if power is okay, odu burnt at either remote site (A) OR (B)'
|
32 |
+
else: # do feature elimination for data irrelevant to outcome
|
33 |
+
return 'case 7: site status cannot be determined by RSL data'
|
34 |
+
|
35 |
+
#construct interface for user data input
|
36 |
+
def main():
|
37 |
+
#give a title
|
38 |
+
streamlit.title('microwave fault detection web app')
|
39 |
+
#get input data from user
|
40 |
+
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')
|
41 |
+
#key= 'rslb' is to distinguish two similar widgets 'text_input' in streamlit
|
42 |
+
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')
|
43 |
+
#code for prediction
|
44 |
+
detection ="" #declare this variable to hold result like empty list
|
45 |
+
#mylist = []
|
46 |
+
if streamlit.button('click here for fault prediction'):
|
47 |
+
detection=microwave_fault_prediction([RSLA,RSLB])
|
48 |
+
#convert inputs into a single parameter using list [1,2]
|
49 |
+
#microwave_fault_prediction ...call the function to process input
|
50 |
+
streamlit.success(detection)
|
51 |
+
if __name__ == '__main__':
|
52 |
+
main()
|
53 |
+
|
54 |
+
|
55 |
+
# web app on your desktop local host
|
56 |
+
#run on your pycharm terminal ' streamlit run microwaveAPP.py '
|
57 |
+
# if using command window ensure the path is correct.. c:\users\idom...\pycharm..\machin learing>
|
58 |
+
# that is pionting to your python file
|
59 |
+
|
trained_lrmodel.sav
ADDED
Binary file (913 Bytes). View file
|
|