Upload files to "ai-wozek"

ai-wozek/.idea/aws.xml

@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="accountSettings">
-    <option name="activeRegion" value="us-east-1" />
-    <option name="recentlyUsedRegions">
-      <list>
-        <option value="us-east-1" />
-      </list>
-    </option>
-  </component>
-</project>

ai-wozek/.idea/misc.xml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="Python 3.11 (ai-wozek) (2)" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (ai-wozek)" project-jdk-type="Python SDK" />
-</project>

ai-wozek/.idea/runConfigurations/wozek.xml

@@ -1,24 +1,18 @@
 <component name="ProjectRunConfigurationManager">
   <configuration default="false" name="wozek" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
     <module name="wozek" />
-    <option name="ENV_FILES" value="" />
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
       <env name="PYTHONUNBUFFERED" value="1" />
     </envs>
     <option name="SDK_HOME" value="" />
-    <option name="SDK_NAME" value="Python 3.11 (ai-wozek) (2)" />
+    <option name="SDK_NAME" value="Python 3.10" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-    <EXTENSION ID="software.aws.toolkits.jetbrains.core.execution.PythonAwsConnectionExtension">
-      <option name="credential" />
-      <option name="region" />
-      <option name="useCurrentConnection" value="false" />
-    </EXTENSION>
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/wozek.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />

ai-wozek/.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
-  </component>
-</project>

ai-wozek/.idea/wozek.iml

@@ -1,10 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
+    <content url="file://$MODULE_DIR$" />
-      <excludeFolder url="file://$MODULE_DIR$/venv" />
-    </content>
-    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

ai-wozek/.~lock.paczki.csv#

@@ -1 +0,0 @@
-,DESKTOP-REF7T1M/abc,DESKTOP-REF7T1M,16.06.2024 22:58,file:///C:/Users/abc/AppData/Roaming/LibreOffice/4;

ai-wozek/classes.py

@@ -1,31 +1,12 @@
-import random
+
-from glob import glob
-import numpy as np
 
 class Cargo:
-    def __init__(self, height,width,depth,weight,damage,label_state,content,value,position):
+    def __init__(self, name, category, size, weight):
-        self.height=height
+        self.name = name
-        self.width=width
+        self.size = size
-        self.depth=depth
         self.weight = weight
-        self.damage=damage
+        self.category = category
-        self.label_state=label_state
+
-        self.content=content
-        self.value=value
-        self.position=position
-    def contentSplit(self):
-        if self.content=='fruits':
-            test=glob('./siec/train/fragile/*')
-            self.image=np.random.choice(test)
-        elif self.content=='nuclear_waste':
-            test=glob('./siec/train/toxic/*')
-            self.image = np.random.choice(test)
-        elif self.content=='clothes':
-            test = glob('./siec/train/flammable/*')
-            self.image = np.random.choice(test)
-        else:
-            self.image='./fruit.png'
-        print(self.image)
 
 class Clothes(Cargo):
     def __init__(self, name, size, weight, fragility):

ai-wozek/decision_tree

@@ -1,74 +0,0 @@
-digraph {
-	root [label=root]
-	Label_State [label=Label_State]
-	no [label=no]
-	Label_State -> no [label=""]
-	Height [label=Height]
-	no -> Height [label=""]
-	no [label=no shape=box]
-	Height -> no [label=medium]
-	no [label=no shape=box]
-	Height -> no [label=big]
-	small [label=small]
-	Height -> small [label=""]
-	Width [label=Width]
-	small -> Width [label=""]
-	small [label=small]
-	Width -> small [label=""]
-	Depth [label=Depth]
-	small -> Depth [label=""]
-	no [label=no shape=box]
-	Depth -> no [label=big]
-	yes [label=yes shape=box]
-	Depth -> yes [label=medium]
-	no [label=no shape=box]
-	Width -> no [label=big]
-	medium [label=medium]
-	Width -> medium [label=""]
-	Depth [label=Depth]
-	medium -> Depth [label=""]
-	no [label=no shape=box]
-	Depth -> no [label=big]
-	yes [label=yes shape=box]
-	Depth -> yes [label=medium]
-	yes [label=yes shape=box]
-	Depth -> yes [label=small]
-	yes [label=yes]
-	Label_State -> yes [label=""]
-	Damage [label=Damage]
-	yes -> Damage [label=""]
-	yes [label=yes shape=box]
-	Damage -> yes [label=no]
-	yes [label=yes]
-	Damage -> yes [label=""]
-	Height [label=Height]
-	yes -> Height [label=""]
-	no [label=no shape=box]
-	Height -> no [label=medium]
-	no [label=no shape=box]
-	Height -> no [label=big]
-	small [label=small]
-	Height -> small [label=""]
-	Width [label=Width]
-	small -> Width [label=""]
-	no [label=no shape=box]
-	Width -> no [label=big]
-	small [label=small]
-	Width -> small [label=""]
-	Depth [label=Depth]
-	small -> Depth [label=""]
-	yes [label=yes shape=box]
-	Depth -> yes [label=medium]
-	no [label=no shape=box]
-	Depth -> no [label=big]
-	yes [label=yes shape=box]
-	Depth -> yes [label=small]
-	medium [label=medium]
-	Width -> medium [label=""]
-	Value [label=Value]
-	medium -> Value [label=""]
-	yes [label=yes shape=box]
-	Value -> yes [label=cheap]
-	no [label=no shape=box]
-	Value -> no [label=expensive]
-}

ai-wozek/siec/dataset.py

@@ -1,40 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import Dataset, DataLoader
-import torchvision
-import torchvision.transforms as transforms
-from torchvision.datasets import ImageFolder
-import timm
-
-import matplotlib.pyplot as plt # For data viz
-import pandas as pd
-import numpy as np
-import sys
-
-class LabelDataset(Dataset):
-    def __init__(self, dataDirectory,transform=None):
-        self.data=ImageFolder(dataDirectory,transform=transform)
-    def __len__(self):
-        return len(self.data)
-    def __getitem__(self, idx):
-        return self.data[idx]
-
-    @property
-    def classes(self):
-        return self.data.classes
-
-classname={v:k for k,v in ImageFolder('./train').class_to_idx.items()}
-
-transform=transforms.Compose([
-    transforms.Resize((128,128)),
-    transforms.ToTensor(),
-])
-
-dataset=LabelDataset('./train',transform)
-dataloader=DataLoader(dataset,batch_size=4, shuffle=True)
-valset=LabelDataset('./val',transform)
-valloader=DataLoader(valset,batch_size=4,shuffle=False)
-
-for images,labels in dataloader:
-    break

ai-wozek/siec/model.py

@@ -1,114 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import Dataset, DataLoader
-import torchvision
-import torchvision.transforms as transforms
-from torchvision.datasets import ImageFolder
-import timm
-from PIL import Image
-
-import matplotlib.pyplot as plt # For data viz
-import pandas as pd
-import numpy as np
-import sys
-from siec import dataset
-from glob import glob
-
-class LabelClassifier(nn.Module):
-    def __init__(self,num_classes=3):
-        super(LabelClassifier, self).__init__()
-        self.model=timm.create_model('efficientnet_b0',pretrained=True)
-        self.features = nn.Sequential(*list(self.model.children())[:-1])
-        out_size=1280
-        self.classifier=nn.Linear(out_size,num_classes)
-    def forward(self,x):
-        x=self.features(x)
-        output=self.classifier(x)
-        return output
-model=torch.load("./model")
-
-criterion=nn.CrossEntropyLoss()
-optimizer=optim.Adam(model.parameters(),lr=0.0005)
-print(criterion(model(dataset.images),dataset.labels))
-
-num_epochs=0
-trainLosses=[]
-valLosses=[]
-
-device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-for epoch in range(num_epochs):
-    model.train()
-    runningLoss=0.0
-    for images,labels in dataset.dataloader:
-        images, labels=images.to(device), labels.to(device)
-        optimizer.zero_grad()
-        outputs=model(images)
-        loss=criterion(outputs,labels)
-        loss.backward()
-        optimizer.step()
-        runningLoss+=loss.item()*images.size(0)
-    trainLoss=runningLoss/len(dataset.dataloader.dataset)
-    trainLosses.append(trainLoss)
-
-    model.eval()
-    runningLoss=0.0
-    with torch.no_grad():
-        for images, labels in dataset.valloader:
-            images, labels = images.to(device), labels.to(device)
-            outputs=model(images)
-            loss=criterion(outputs,labels)
-            runningLoss+=loss.item()*images.size(0)
-    valLoss=runningLoss/len(dataset.valloader.dataset)
-    valLosses.append(valLoss)
-    print(f"Epoch {epoch + 1}/{num_epochs} - Train loss: {trainLoss}, Validation loss: {valLoss}")
-modell=torch.jit.script(model)
-modell.save('model.pt')
-
-
-def preprocess_image(image_path, transform):
-    image = Image.open(image_path).convert("RGB")
-    return image, transform(image).unsqueeze(0)
-
-# Predict using the model
-def predict(model, image_tensor, device):
-    model.eval()
-    with torch.no_grad():
-        image_tensor = image_tensor.to(device)
-        outputs = model(image_tensor)
-        probabilities = torch.nn.functional.softmax(outputs, dim=1)
-        print(outputs)
-    return probabilities.cpu().numpy().flatten()
-
-# Visualization
-def visualize_predictions(original_image, probabilities, class_names):
-    fig, axarr = plt.subplots(1, 2, figsize=(14, 7))
-    axarr[0].imshow(original_image)
-    axarr[0].axis("off")
-
-    # Display predictions
-    axarr[1].barh(class_names, probabilities)
-    axarr[1].set_xlabel("Probability")
-    axarr[1].set_title("Class Predictions")
-    axarr[1].set_xlim(0, 1)
-
-    plt.tight_layout()
-    plt.show()
-transform = transforms.Compose([
-    transforms.Resize((128, 128)),
-    transforms.ToTensor()
-])
-test_images = glob('./train/*/*')
-test_examples = np.random.choice(test_images, 10)
-for example in test_examples:
-    model.eval()
-    original_image, image_tensor = preprocess_image(example, transform)
-    probabilities = predict(model, image_tensor, device)
-    print(probabilities)
-
-    # Assuming dataset.classes gives the class names
-    class_names = dataset.dataset.classes
-    visualize_predictions(original_image, probabilities, class_names)
-    model(image_tensor)