edit to lab2 exercise

app/evaluate.csv

@@ -1,11 +1,11 @@
 temperature,pressure,diagnosis
-77,153,N
-93,170,N
-87,43,P
-49,198,P
-110,30,P
-68,142,N
-104,38,P
-55,175,P
-82,157,N
-96,55,P
+11,177,P
+11,193,P
+63,176,P
+96,119,P
+10,176,P
+28,177,P
+11,125,A
+62,81,N
+53,190,P
+99,197,P

app/factory_predictor.py

@@ -2,10 +2,11 @@ import pandas as pd
 from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import LabelEncoder
 from sklearn.metrics import accuracy_score
+from sklearn.tree import DecisionTreeRegressor
 
 class FactoryPredictor:
     def __init__(self):
-        self.model = LogisticRegression()
+        self.model = DecisionTreeRegressor()
         self.le_diagnosis = LabelEncoder()
         self.temperature = 0
         self.pressure = 0
@@ -59,10 +60,6 @@ class FactoryPredictor:
             [[temperature, pressure]],
             columns=['temperature', 'pressure'])
 
-        # Make prediction
         prediction = self.model.predict(input_data)
-
-        # Decode prediction
-        diagnosis = self.le_diagnosis.inverse_transform(prediction)[0]
-
+        diagnosis = self.le_diagnosis.inverse_transform([int(round(prediction[0]))])[0]
         return diagnosis

app/static/gallery/factory-ai.html

@@ -17,6 +17,7 @@
     <state-pane-oid initial="N" subscribe="update/alert~state">
       <image-oid id="N" source="assets:images/light-green.svg"></image-oid>
       <image-oid id="P" source="assets:images/light-red.svg"></image-oid>
+      <image-oid id="Y" source="assets:images/light-yellow.svg"></image-oid>
     </state-pane-oid>
   </span>
   <span id="train" style="width: 20%; display: inline-block; vertical-align: middle">

app/test.csv

@@ -1,11 +1,31 @@
 temperature,pressure,diagnosis
-70,145,N
-88,162,N
-83,158,N
-97,47,P
-53,188,P
-45,200,P
-105,35,P
-65,140,N
-102,42,P
-58,182,P
+11,177,P
+11,193,P
+63,176,P
+96,119,P
+10,176,P
+28,177,P
+11,125,A
+62,81,N
+53,190,P
+99,197,P
+41,157,P
+79,68,N
+41,111,N
+77,133,A
+64,158,P
+84,40,A
+65,180,P
+26,188,P
+47,71,N
+33,128,A
+78,36,P
+79,173,P
+95,119,P
+20,141,A
+25,89,N
+82,142,A
+68,31,P
+79,158,P
+89,77,A
+12,169,P

app/train.csv

@@ -1,17 +1,101 @@
 temperature,pressure,diagnosis
-75,150,N
-80,155,N
-72,148,N
-85,160,N
-78,152,N
-90,165,N
-95,50,P
-100,45,P
-85,40,P
-92,48,P
-98,52,P
-50,180,P
-55,190,P
-48,195,P
-60,185,P
-52,192,P
+61,94,N
+24,118,N
+81,100,A
+70,38,P
+30,117,N
+92,158,P
+96,165,P
+84,92,A
+84,168,A
+97,110,P
+33,165,P
+12,192,P
+31,192,P
+62,62,N
+11,152,P
+97,34,P
+39,70,N
+47,57,N
+11,164,P
+73,101,N
+69,41,A
+30,191,P
+42,62,N
+85,77,A
+67,180,P
+31,91,N
+98,66,P
+58,128,A
+100,133,A
+68,64,N
+51,130,A
+69,160,P
+89,30,A
+24,34,P
+71,171,P
+71,132,A
+56,56,N
+71,166,P
+60,44,A
+64,119,N
+73,71,N
+12,153,P
+60,92,N
+16,125,A
+30,81,N
+82,125,A
+48,161,P
+27,180,P
+13,172,P
+98,200,P
+69,58,N
+23,65,N
+18,42,A
+99,189,P
+62,100,N
+11,115,P
+93,57,P
+69,95,N
+80,199,P
+53,74,N
+17,91,A
+56,163,P
+44,57,N
+87,57,A
+90,137,A
+45,73,N
+59,113,N
+13,59,P
+11,104,P
+15,157,A
+63,121,A
+13,158,P
+63,150,A
+72,56,N
+27,150,A
+99,145,A
+53,32,P
+43,132,A
+83,184,A
+71,166,P
+23,91,N
+57,194,P
+24,80,N
+81,181,A
+87,88,A
+96,147,A
+71,189,P
+49,125,A
+94,142,A
+89,91,A
+91,81,P
+62,41,A
+33,68,N
+35,159,P
+98,160,P
+69,142,A
+50,130,A
+38,142,A
+24,110,N
+54,142,A