diff --git a/.idea/misc.xml b/.idea/misc.xml
index acd589d..40e984a 100644
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+++ b/.idea/misc.xml
@@ -4,7 +4,4 @@
     <option name="sdkName" value="Python 3.10 (Madra_smieciarka)" />
   </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (Madra_smieciarka)" project-jdk-type="Python SDK" />
-  <component name="PyCharmProfessionalAdvertiser">
-    <option name="shown" value="true" />
-  </component>
 </project>
\ No newline at end of file
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diff --git a/classes/Garbagetruck.py b/classes/Garbagetruck.py
index 3111bba..9c1cb51 100644
--- a/classes/Garbagetruck.py
+++ b/classes/Garbagetruck.py
@@ -1,12 +1,10 @@
 import math
 import heapq
-import numpy as np
 from joblib import load
 import pygame
 from classes.Household import *
 from classes.Node import *
 from classes.Trashcan import Trashcan
-from tensorflow.keras.models import load_model
 
 
 class Garbagetruck:
@@ -26,30 +24,21 @@ class Garbagetruck:
         self.position = [3, 3]
         self.houses: list = []
         self.trashcans: list = []
-        self.state = None  # używane
+        self.state = None
         self.segregation = {"Papier": "paper",
                             "MetalPlastik": "metals_and_plastics",
                             "Mixed": "mixed",
                             "Bio": "bio_waste",
                             "Szklo": "glass"}
-        self.route = None  # jeszcze nie
-        self.scanner = load_model("./classes/best_model_newD.h5")
-        self.planner = None  # jeszcze nie
+        self.route = None
+        self.scanner = None
+        self.planner = None
         self.driver = load("./classes/drzewo.joblib")
         self.orientation = 3  # Niech numery będą tak: N - 0, W - 1, S - 2, E - 3  -- po prostu odwrotnie do zegara
         self.runningtime = 0
         self.movesequence = []
-        self.target = None  # używane
-        self.analising = False  # używane
-
-    def identify(self, trash):
-        obraz = trash.getImage()
-        wynik = self.scanner.predict(obraz)
-        wynik = np.argmax(wynik)
-        typelist = ["paper", "bio_waste", "mixed", "glass", "metals_and_plastics"]
-        wynik = typelist[wynik]
-        trash.setTtype(wynik)
-        print(f"Prawdziwy typ śmieci: {trash.getRealtype()}\nZidentyfikowany typ śmieci: {trash.getTtype()}")
+        self.target = None
+        self.analising = False
 
     def getAnalising(self):
         return self.analising
@@ -76,9 +65,9 @@ class Garbagetruck:
         can = house.getGarbage()
         trashlist = can.getContent()
         for trash in trashlist:
-            self.identify(trash)
             if self.trashweight + trash.getWaga() <= self.capacity:
                 decision = self.decision(house, trash, can)
+                print(f"{trash.getTtype()} - decyzja: {decision}")
                 if decision:
                     self.addTrash(trash)
                     can.removeContent(trash)
@@ -242,11 +231,10 @@ class Garbagetruck:
         x, y = self.getPosition()
         return self.mult*x, self.mult*y
 
-    def throwGarbage(self):
-        for item in self.trash:
-            if item.getTtype() == self.state.getTrashtype():
-                self.addTrashweight(item.getWeight * (-1))
-                self.trash.remove(item)
+    def throwGarbage(self, trash):
+        if self.segregation[trash.getTtype()] == self.state.getTrashtype():
+            self.addTrashweight(trash.getWeight * (-1))
+            self.trash.remove(trash)
 
     def rotateImage(self, arg):
         self.image = pygame.transform.rotate(self.image, 90*arg)
@@ -388,3 +376,9 @@ class Garbagetruck:
         #     wybor2 = random.choice(self.trashcans)
         wybor2.switchFinal()
         # print(wybor2)
+
+    def classifyTrash(self):
+        pass
+#     Tutaj jest plan żeby dopiero napisać funkcję jak już będzie klasyfikator
+#     ogólnie to myślałem żeby po prostu zklasyfikować śmieć i zmienić mu trashtype na rozpoznany,
+#     żeby śmieciarka go tak posegreowała
diff --git a/classes/NeuralNetwork.py b/classes/NeuralNetwork.py
deleted file mode 100644
index b9abae5..0000000
--- a/classes/NeuralNetwork.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Ten plik był odpalany na remote serwerze jupyter notebook, stąd dziwne komentarze i cała baza danych w zipie
-# Skuteczność sieci to mniej więcej 80% na zbiorze walidacyjnym, ale jest przeuczona, bo nie augmentowaliśmy danych
-
-
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-from tensorflow.keras import Sequential
-from tensorflow.keras.callbacks import EarlyStopping
-from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Input
-from tensorflow.keras.callbacks import ModelCheckpoint
-
-save_best = ModelCheckpoint('./best_model_newD.h5', monitor='val_loss', save_best_only=True)
-es = EarlyStopping(monitor="val_loss", mode="min", patience=5)
-
-EPOCHS = 100
-
-
-# In[2]:
-
-
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-
-root_path = "./Database/"
-
-gen = ImageDataGenerator(
-    rescale=1./255,
-    validation_split=0.2
-)
-
-# Load Data
-train_dataset = gen.flow_from_directory(
-    root_path,
-    class_mode="categorical",
-    classes=os.listdir(root_path),
-    shuffle=True,
-    batch_size=32,
-    target_size=(128,128),
-    subset="training",
-    color_mode='grayscale'
-)
-
-validation_dataset = gen.flow_from_directory(
-    root_path,
-    class_mode="categorical",
-    classes=os.listdir(root_path),
-    shuffle=True,
-    batch_size=32,
-    target_size=(128,128),
-    subset="validation",
-    color_mode='grayscale'
-)
-
-
-# In[4]:
-
-model = Sequential()
-model.add(Input(shape=(128,128)))
-model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation='relu', input_shape=(128, 128, 1)))
-model.add(MaxPooling2D((2, 2)))
-model.add(Conv2D(filters = 32, kernel_size = (3, 3), activation='relu', input_shape=(64, 64, 1)))
-model.add(MaxPooling2D((2, 2)))
-model.add(Conv2D(filters = 16, kernel_size = (3, 3), activation='relu', input_shape=(32, 32, 1)))
-model.add(MaxPooling2D((2, 2)))
-model.add(Flatten())
-model.add(Dense(128*128, activation='relu'))
-model.add(Dense(1000, activation='relu'))
-# model.add(Dropout(0.2))
-model.add(Dense(25, activation='relu'))
-# model.add(Dropout(0.2))
-model.add(Dense(5, activation='softmax'))
-
-model.compile(optimizer='adam',
-              loss='categorical_crossentropy',
-              metrics=['accuracy'])
-model.summary()
-
-
-# In[5]:
-
-
-history = model.fit(train_dataset, epochs = EPOCHS, validation_data=(validation_dataset), callbacks = [es, save_best])
-
-
-# In[ ]:
-
-
-
-
diff --git a/classes/Trash.py b/classes/Trash.py
index 72f184f..6cacaff 100644
--- a/classes/Trash.py
+++ b/classes/Trash.py
@@ -1,30 +1,13 @@
-from random import choice as rd
-
-import numpy as np
-
-from classes.trashFunctions import generatePicList
+typelist = ["paper", "metals_and_plastics", "mixed", "bio_waste", "glass"]
 
 
 class Trash:
-    def __init__(self):
-        self.tree_type = 0
-        self.ttype = "Trash"
-        self.real_type = "Trash"
-        self.scan_id = 0
-        self.waga = 1
-        self.image = None
-
-    def getRealtype(self):
-        return self.real_type
-
-    def getScanid(self):
-        return self.scan_id
 
     def getTreetype(self):
-        return self.tree_type
+        return self.treetype
 
     def setTreetype(self, treetype):
-        self.tree_type = id
+        self.treetype = id
         return self
 
     def getTtype(self):
@@ -50,70 +33,40 @@ class Trash:
 
 
 class Papier(Trash):
-    lista_obrazow = generatePicList("paper")
-
     def __init__(self):
-        super().__init__()
-        self.tree_type = 1
+        self.treetype = 1
         self.ttype = "Papier"
-        self.real_type = "Papier"
-        self.scan_id = 0
         self.waga = 2
         self.image = None
-        self.setImage(rd(self.lista_obrazow))
 
 
 class MetalPlastik(Trash):
-    lista_obrazow = generatePicList("plastic")
-
     def __init__(self):
-        super().__init__()
-        self.tree_type = 2
+        self.treetype = 2
         self.ttype = "MetalPlastik"
-        self.real_type = "MetalPlastik"
-        self.scan_id = 4
         self.waga = 3
         self.image = None
-        self.setImage(rd(self.lista_obrazow))
 
 
 class Mixed(Trash):
-    lista_obrazow = generatePicList("mixed")
-
     def __init__(self):
-        super().__init__()
-        self.tree_type = 4
+        self.treetype = 4
         self.ttype = "Mixed"
-        self.real_type = "Mixed"
-        self.scan_id = 2
         self.waga = 1
         self.image = None
-        self.setImage(rd(self.lista_obrazow))
 
 
 class Bio(Trash):
-    lista_obrazow = generatePicList("bio")
-
     def __init__(self):
-        super().__init__()
-        self.tree_type = 0
+        self.treetype = 0
         self.ttype = "Bio"
-        self.real_type = "Bio"
-        self.scan_id = 1
         self.waga = 2
         self.image = None
-        self.setImage(rd(self.lista_obrazow))
 
 
 class Szklo(Trash):
-    lista_obrazow = generatePicList("glass")
-
     def __init__(self):
-        super().__init__()
-        self.tree_type = 3
+        self.treetype = 3
         self.ttype = "Szklo"
-        self.real_type = "Szklo"
-        self.scan_id = 3
         self.waga = 5
         self.image = None
-        self.setImage(rd(self.lista_obrazow))
diff --git a/classes/best_model_newD.h5 b/classes/best_model_newD.h5
deleted file mode 100644
index ac6e44b..0000000
Binary files a/classes/best_model_newD.h5 and /dev/null differ
diff --git a/classes/trashFunctions.py b/classes/trashFunctions.py
deleted file mode 100644
index 6a37b18..0000000
--- a/classes/trashFunctions.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import tensorflow as tf
-import os
-import numpy as np
-
-
-def load_and_preprocess_image(file_path, target_size):
-    img = tf.io.read_file(file_path)
-    img = tf.image.decode_png(img, channels=1)
-    img = tf.image.resize(img, target_size)
-    img = img / 255.0
-    return img
-
-
-def generatePicList(category):
-    path = f"./Tpics/{category}"
-    dircs = os.listdir(path)
-    result = []
-    for img in dircs:
-        image = f"{path}/" + img
-        target_size = (128, 128)
-        image_tensor = load_and_preprocess_image(image, target_size)
-        image_tensor = np.array(image_tensor)
-        image_tensor = np.expand_dims(image_tensor, axis=0)
-        result.append(image_tensor)
-    return result
diff --git a/main.py b/main.py
index 26f7e11..3a1ef78 100644
--- a/main.py
+++ b/main.py
@@ -15,9 +15,9 @@ trashcans = trashcanGenerator(MULT)
 houses = householdGenerator(MULT)
 garbagetruck = Garbagetruck(MULT).setHouses(houses).setTrashcans(trashcans)
 
-# print("Kolejność danych do drzewa:")
-# print("Pora roku - Pora dnia - Typ śmieci - Zapełnienie kosza - Zapełnienie śmieciarki - Zapłacone - Ostatnio zabrane "
-#       "- Pogoda")
+print("Kolejność danych do drzewa:")
+print("Pora roku - Pora dnia - Typ śmieci - Zapełnienie kosza - Zapełnienie śmieciarki - Zapłacone - Ostatnio zabrane "
+      "- Pogoda")
 
 running = True
 while running:
@@ -48,7 +48,7 @@ while running:
         garbagetruck.setMovesequence(moves)
     if state:
         if state.getFinal():
-            # print([trash.getTtype() for trash in state.getGarbage().getContent()])
+            print([trash.getTtype() for trash in state.getGarbage().getContent()])
             garbagetruck.switchAnalising()
             garbagetruck.getState().switchFinal()
         elif not garbagetruck.movesequence: