lab2-edit

app/evaluate.csv

@@ -1,21 +1,11 @@
 temperature,pressure,diagnosis
-58,129,A
-113,164,P
-62,100,N
-108,187,P
-70,143,A
-81,113,N
-114,98,P
-75,167,P
-90,156,A
-56,121,A
-86,105,N
-109,61,P
-97,173,P
-56,103,A
-101,128,P
-57,109,A
-110,139,P
-71,103,N
-84,156,A
-118,120,P
+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

app/factory_predictor.py

@@ -2,11 +2,10 @@ 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 DecisionTreeClassifier
 
 class FactoryPredictor:
     def __init__(self):
-        self.model = DecisionTreeClassifier(random_state=0)
+        self.model = LogisticRegression()
         self.le_diagnosis = LabelEncoder()
         self.temperature = 0
         self.pressure = 0
@@ -30,7 +29,7 @@ class FactoryPredictor:
         data_test = pd.read_csv(csv_test)
 
         # Encode categorical variables
-        data_test['diagnosis'] = self.le_diagnosis.transform(data_test['diagnosis'])
+        data_test['diagnosis'] = self.le_diagnosis.fit_transform(data_test['diagnosis'])
 
         # Split features and target
         X_test = data_test[
@@ -60,6 +59,10 @@ 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]
+
         return diagnosis

app/server_factory_predictor.py

@@ -17,7 +17,7 @@ app.mount("/app/static", StaticFiles(directory="static"), name="static")
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["http://127.0.0.1:5173","http://localhost:5173","http://127.0.0.1:7860",
-                   "http://localhost:7860","http://0.0.0.0:7860", "https://LucasPinhheiro-factory-ml.hf.space"],
+                   "http://localhost:7860","http://0.0.0.0:7860", "https://santanche-factory-ml.hf.space"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],

app/static/editor/js/editor-rest.js

@@ -6,7 +6,7 @@ Oid.customize('oid:rest', {
   api: {
     oas: {
       paths: {
-        'https://LucasPinhheiro-factory-ml.hf.space/train?train_path={train_path}&test_path={test_path}': {
+        'https://santanche-factory-ml.hf.space/train?train_path={train_path}&test_path={test_path}': {
           'post': {
             operationId: 'train',
             parameters: [
@@ -19,7 +19,7 @@ Oid.customize('oid:rest', {
             ]
           }
         },
-        'https://LucasPinhheiro-factory-ml.hf.space/predict?radius_mean={radius_mean}&texture_mean={texture_mean}&symmetry_mean={symmetry_mean}&fractal_dimension_mean={fractal_dimension_mean}': {
+        'https://santanche-factory-ml.hf.space/predict?radius_mean={radius_mean}&texture_mean={texture_mean}&symmetry_mean={symmetry_mean}&fractal_dimension_mean={fractal_dimension_mean}': {
           'get': {
             operationId: 'predict',
             parameters: [
@@ -48,12 +48,12 @@ Oid.customize('oid:rest', {
   api: {
     oas: {
       paths: {
-        'https://LucasPinhheiro-factory-ml.hf.space/train': {
+        'https://santanche-factory-ml.hf.space/train': {
           'post': {
             operationId: 'train'
           }
         },
-        'https://LucasPinhheiro-factory-ml.hf.space/inform_temperature?value={value}': {
+        'https://santanche-factory-ml.hf.space/inform_temperature?value={value}': {
           'post': {
             operationId: 'temperature',
             parameters: [
@@ -63,7 +63,7 @@ Oid.customize('oid:rest', {
             ]
           }
         },
-        'https://LucasPinhheiro-factory-ml.hf.space/inform_pressure?value={value}': {
+        'https://santanche-factory-ml.hf.space/inform_pressure?value={value}': {
           'post': {
             operationId: 'pressure',
             parameters: [

app/static/gallery/factory-ai.html

@@ -17,7 +17,6 @@
     <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="A" 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

@@ -1,181 +1,11 @@
 temperature,pressure,diagnosis
-58,129,A
-113,164,P
-62,100,N
-108,187,P
-70,143,A
-81,113,N
-114,98,P
-75,167,P
-90,156,A
-56,121,A
-86,105,N
-109,61,P
-97,173,P
-56,103,A
-101,128,P
-57,109,A
-110,139,P
-71,103,N
-84,156,A
-118,120,P
-71,100,N
-73,142,A
-104,198,P
-115,183,P
-101,175,P
-111,86,P
-118,100,P
-68,159,A
-80,122,N
-61,152,A
-76,95,N
-62,148,A
-87,138,N
-109,45,P
-53,119,A
-42,199,P
-66,160,A
-54,92,A
-79,113,N
-52,116,A
-114,166,P
-101,54,P
-52,196,P
-65,118,N
-86,140,N
-77,99,N
-64,103,N
-88,124,N
-115,145,P
-67,90,N
-82,151,A
-53,113,A
-80,157,A
-113,109,P
-111,82,P
-50,130,A
-68,93,N
-120,92,P
-51,96,A
-111,190,P
-80,111,N
-68,92,N
-83,92,N
-82,129,N
-82,144,A
-60,139,N
-77,116,N
-87,130,N
-104,67,P
-62,110,N
-72,172,P
-63,113,N
-70,114,N
-120,63,P
-73,111,N
-68,109,N
-77,120,N
-64,174,P
-51,113,A
-55,126,A
-59,112,A
-112,200,P
-81,91,N
-81,124,N
-120,184,P
-44,161,P
-118,164,P
-96,192,P
-106,99,P
-50,104,A
-82,132,N
-89,138,N
-116,140,P
-112,140,P
-115,57,P
-54,140,A
-62,123,N
-73,118,N
-55,120,A
-82,123,N
-52,135,A
-90,157,A
-79,141,A
-105,199,P
-61,91,N
-108,106,P
-85,129,N
+70,145,N
+88,162,N
+83,158,N
+97,47,P
+53,188,P
+45,200,P
+105,35,P
 65,140,N
-101,131,P
-58,95,A
-79,113,N
-65,101,N
-54,182,P
-80,103,N
-75,195,P
-68,129,N
-79,111,N
-43,172,P
-67,155,A
-120,143,P
-78,107,N
-88,140,N
-52,110,A
-79,132,N
-112,191,P
-84,147,A
-68,158,A
-101,79,P
-79,112,N
-63,153,A
-89,187,P
-55,136,A
-74,103,N
-80,128,N
-80,100,N
-55,94,A
-59,128,A
-52,92,A
-69,164,P
-111,68,P
-56,105,A
-88,126,N
-78,154,A
-87,142,A
-90,151,A
-83,141,A
-75,146,A
-103,91,P
-86,145,A
-84,149,A
-120,199,P
-57,199,P
-77,190,P
-112,177,P
-65,101,N
-55,107,A
-90,124,N
-76,103,N
-70,106,N
-54,132,A
-62,166,P
-58,112,A
-102,164,P
-81,189,P
-87,149,A
-87,134,N
-53,117,A
-81,108,N
-69,182,P
-75,147,A
-88,145,A
-89,115,N
-109,78,P
-63,160,A
-57,104,A
-84,128,N
-68,144,A
-50,130,A
-119,106,P
-63,143,A
+102,42,P
+58,182,P

app/train.csv

@@ -1,901 +1,17 @@
 temperature,pressure,diagnosis
-42,74,P
-62,152,A
-48,192,P
-73,159,A
-80,120,N
-55,106,A
-64,120,N
-106,62,P
-52,65,P
-80,138,N
-69,52,P
-63,141,A
-58,42,P
-76,66,P
-71,42,P
-44,107,P
-86,149,A
-84,131,N
-43,42,P
-62,115,N
-46,96,P
-58,107,A
-51,117,A
-66,52,P
-79,69,P
-85,110,N
-45,98,P
-48,173,P
-57,124,A
-112,40,P
-61,138,N
-42,125,P
-88,103,N
-98,59,P
-112,41,P
-86,138,N
-45,99,P
-89,134,N
-61,122,N
-40,164,P
-103,42,P
-67,157,A
-66,109,N
-72,118,N
-42,43,P
-54,139,A
-80,134,N
-41,148,P
-72,111,N
-53,138,A
-53,140,A
-60,136,N
-88,111,N
-63,136,N
-68,141,A
-62,55,P
-120,55,P
-61,96,N
-53,97,A
-72,160,A
-54,91,A
-52,53,P
-63,93,N
-108,57,P
-77,153,A
-40,73,P
-73,109,N
-84,93,N
-72,145,A
-42,51,P
-68,67,P
-80,57,P
-67,141,A
-40,161,P
-60,146,A
-44,114,P
-90,104,N
-90,144,A
-83,90,N
-57,139,A
-82,139,N
-87,116,N
-45,83,P
-87,136,N
-90,150,A
-40,127,P
-50,123,A
-83,107,N
-55,114,A
-77,111,N
-41,43,P
-59,138,A
-45,68,P
-42,50,P
-67,118,N
-87,110,N
-63,129,N
-47,42,P
-61,108,N
-52,92,A
-62,136,N
-75,130,N
-66,117,N
-83,49,P
-79,154,A
-83,129,N
-72,115,N
-58,138,A
-81,98,N
-50,121,A
-54,130,A
-100,53,P
-85,124,N
-66,159,A
-88,105,N
-62,155,A
-72,114,N
-48,172,P
-84,102,N
-74,109,N
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-72,133,N
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-51,50,P
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-114,60,P
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