diff --git a/.github/workflows/python-coverage.yml b/.github/workflows/python-coverage.yml
index aadb7c0c..70dca56b 100644
--- a/.github/workflows/python-coverage.yml
+++ b/.github/workflows/python-coverage.yml
@@ -16,11 +16,18 @@ jobs:
         python-version: 3.9
     - name: Install dependencies
       run: |
+        sudo apt-get update
+        sudo apt-get install python3-tk python3-dev
+        sudo apt-get install xvfb
+        sudo apt install libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0
+
         python -m pip install --upgrade pip
         pip install -r requirements.txt
+        pip install -r requirements-dev.txt
+        touch /home/runner/.Xauthority
+
     - name: Build unit coverage using pytest-cov
       run: |
-        pip install -r requirements-dev.txt
         pytest --cov=opencodeblocks --cov-report=xml tests/unit
         score=$(python coverage_score.py --score)
         color=$(python coverage_score.py --color)
@@ -40,14 +47,18 @@ jobs:
         style: plastic
     - name: Build integration coverage using pytest-cov
       run: |
-        pip install -r requirements-dev.txt
-        pytest --cov=opencodeblocks --cov-report=xml tests/integration
+        /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1080x24 -ac +extension GLX
+
+        pytest -s --cov=opencodeblocks --cov-report=xml tests/integration
         score=$(python coverage_score.py --score)
         color=$(python coverage_score.py --color)
         echo "COVERAGE_INTEGRATION_SCORE=$score"
         echo "COVERAGE_INTEGRATION_COLOR=$color"
         echo "COVERAGE_INTEGRATION_SCORE=$score" >> $GITHUB_ENV
         echo "COVERAGE_INTEGRATION_COLOR=$color" >> $GITHUB_ENV
+      env:
+        DISPLAY: :99
+        # QT_DEBUG_PLUGINS: 1 # Uncomment to debug Qt library issues.
     - name: Create integration coverage badge
       uses: schneegans/dynamic-badges-action@v1.1.0
       with:
diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml
index aacf0e40..a4a7db6d 100644
--- a/.github/workflows/python-tests.yml
+++ b/.github/workflows/python-tests.yml
@@ -8,7 +8,7 @@ on: ["push"]
 jobs:
   build:
 
-    runs-on: windows-latest
+    runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [3.7, 3.8, 3.9]
@@ -21,16 +21,18 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
+        sudo apt-get update
+        sudo apt-get install python3-tk python3-dev
+        sudo apt-get install xvfb
+        sudo apt install libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0
+
         python -m pip install --upgrade pip
         pip install -r requirements.txt
-    - name: Lint with flake8
-      run: |
-        pip install flake8
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        pip install -r requirements-dev.txt
+        touch /home/runner/.Xauthority
     - name: Test with pytest
       run: |
-        pip install -r requirements-dev.txt
+        /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1080x24 -ac +extension GLX
         pytest tests
+      env:
+        DISPLAY: :99
diff --git a/pytest.ini b/pytest.ini
index e3b129f9..cabfc9ec 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -2,3 +2,4 @@
 filterwarnings =
     ignore::DeprecationWarning
 addopts = --pspec 
+qt_api=pyqt5
\ No newline at end of file
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 764c45a9..cef44d30 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -3,5 +3,7 @@ pytest-cov
 pytest-mock
 pytest-check
 pytest-pspec
+pytest-qt
+pyautogui
 pylint
 pylint-pytest
\ No newline at end of file
diff --git a/tests/integration/test_blocks.py b/tests/integration/test_blocks.py
new file mode 100644
index 00000000..3aa6e881
--- /dev/null
+++ b/tests/integration/test_blocks.py
@@ -0,0 +1,171 @@
+"""
+Integration tests for OCB.
+
+We use xvfb to perform the tests without opening any windows.
+We use pyautogui to move the mouse and interact with the application.
+
+To pass the tests on windows, you need to not move the mouse.
+Use this if you need to understand why a test fails.
+
+To pass the tests on linux, you just need to install xvfb and it's dependencies.
+On linux, no windows are opened to the user during the test.
+To understand why a test fails, pass the flag "--no-xvfb" and use your own X server
+to see the test running live.
+"""
+
+# Imports needed for testing
+import time, threading, queue, os, sys
+import pytest
+from pytest_mock import MockerFixture
+import pytest_check as check
+import pyautogui
+ 
+# Packages tested
+from opencodeblocks.graphics.blocks.codeblock import OCBCodeBlock
+from opencodeblocks.graphics.socket import OCBSocket
+from opencodeblocks.graphics.window import OCBWindow
+from opencodeblocks.graphics.widget import OCBWidget
+
+from qtpy.QtWidgets import QApplication
+from PyQt5.QtWidgets import QWidget
+from PyQt5.QtGui import QFocusEvent, QMouseEvent
+from PyQt5.QtCore import QCoreApplication, QEvent, Qt, QPointF, QPoint
+from PyQt5 import QtTest
+
+def test_window_opening(qtbot):
+    """ The OCBWindow should open and close correctly """
+    wnd = OCBWindow()
+    wnd.close()
+
+"""
+def test_running_python(qtbot):
+    # The blocks should run arbitrary python when unfocused
+    wnd = OCBWindow()
+    
+    EXPRESSION = "3 + 5 * 2"
+    SOURCE_TEST = \
+        '''
+            print(%s)
+        ''' % EXPRESSION
+    expected_result = str(eval(EXPRESSION))
+
+    # Let's add a block with the source to the window !
+    ocb_widget = OCBWidget()
+    test_block = OCBCodeBlock(title="Testing block", source=SOURCE_TEST)
+    ocb_widget.scene.addItem(test_block)
+    wnd.mdiArea.addSubWindow(ocb_widget)
+
+    # Let's run the block !
+    pyeditor = test_block.source_editor.widget()
+    # pyeditor.setModified(True)
+    # test_block._source = ""
+    QApplication.processEvents()
+    QtTest.QTest.mouseClick(pyeditor,Qt.MouseButton.LeftButton)
+    QApplication.processEvents()
+    QtTest.QTest.keyPress(pyeditor," ")
+    QApplication.processEvents()
+    
+    # Click outside the block to lose focus of the previous block.
+    # This will need to be changed by the click to the run button.
+    QtTest.QTest.mouseClick(ocb_widget,Qt.MouseButton.LeftButton)
+    QApplication.processEvents()
+
+    # When the execution becomes non-blocking for the UI, a refactor will be needed here.
+    result = test_block.stdout.strip()
+    
+    check.equal(expected_result,result)
+    wnd.close()
+"""
+
+def test_move_blocks(qtbot):
+    """ 
+    Newly created blocks are displayed in the center.
+    They can be dragged around with the mouse.
+    """
+    wnd = OCBWindow()
+    
+    ocb_widget = OCBWidget()
+    subwnd = wnd.mdiArea.addSubWindow(ocb_widget)
+
+    test_block1 = OCBCodeBlock(title="Testing block 1", source="print(1)")
+    ocb_widget.scene.addItem(test_block1)
+
+    test_block2 = OCBCodeBlock(title="Testing block 2", source="print(2)")
+    ocb_widget.scene.addItem(test_block2)
+
+    subwnd.show()
+
+    QApplication.processEvents()
+
+    expected_move_amount = [70,-30]
+    STOP_MSG = "stop"
+    CHECK_MSG = "check"
+
+    msgQueue = queue.Queue()
+
+    def testing_drag(msgQueue):
+        time.sleep(.4) # Wait for proper setup of app
+        
+        # test_block1 == (0,0) but it's not crucial for this test.
+        pos_block_1 = QPoint(int(test_block1.pos().x()),int(test_block1.pos().y()))
+        
+        pos_block_1.setX(pos_block_1.x() + test_block1.title_height//2)
+        pos_block_1.setY(pos_block_1.y() + test_block1.title_height//2)
+
+        pos_block_1 = ocb_widget.view.mapFromScene(pos_block_1)
+        pos_block_1 = ocb_widget.view.mapToGlobal(pos_block_1)
+        
+        pyautogui.moveTo(pos_block_1.x(),pos_block_1.y())
+        pyautogui.mouseDown(button="left")
+
+        iterations = 5
+        for i in range(iterations+1):
+            time.sleep(0.05)
+            pyautogui.moveTo(
+                pos_block_1.x() + expected_move_amount[0] * i // iterations,
+                pos_block_1.y() + expected_move_amount[1] * i // iterations
+            )
+    
+        pyautogui.mouseUp(button="left")
+        time.sleep(.2)
+
+        move_amount = [test_block2.pos().x(),test_block2.pos().y()]
+        # rectify because the scene can be zoomed :
+        move_amount[0] = int(move_amount[0] * ocb_widget.view.zoom)
+        move_amount[1] = int(move_amount[1] * ocb_widget.view.zoom)
+
+        msgQueue.put([
+            CHECK_MSG,
+            move_amount,
+            expected_move_amount,
+            "Block moved by the correct amound"
+        ])
+        
+        msgQueue.put([STOP_MSG])
+
+
+    t = threading.Thread(target=testing_drag, args=(msgQueue,))
+    t.start()
+
+    while True:
+        QApplication.processEvents()
+        time.sleep(0.02)
+        if not msgQueue.empty():
+            msg = msgQueue.get()
+            if msg[0] == STOP_MSG:
+                break
+            elif msg[0] == CHECK_MSG:
+                check.equal(msg[1],msg[2],msg[3])
+    t.join()
+    wnd.close()
+
+def test_open_file():
+    """
+        The application loads files properly. 
+    """
+
+    wnd = OCBWindow()
+    file_example_path = "./tests/testing_assets/example_graph1.ipyg"
+    subwnd = wnd.createNewMdiChild(os.path.abspath(file_example_path))
+    subwnd.show()
+    wnd.close()
diff --git a/tests/testing_assets/example_graph1.ipyg b/tests/testing_assets/example_graph1.ipyg
new file mode 100644
index 00000000..588e72bf
--- /dev/null
+++ b/tests/testing_assets/example_graph1.ipyg
@@ -0,0 +1,401 @@
+{
+    "id": 2205665405400,
+    "blocks": [
+        {
+            "id": 2443477874008,
+            "title": "Model Train",
+            "block_type": "code",
+            "source": "print(\"training \")\r\nmodel.fit(x=x_train,y=y_train, epochs=10)\r\n\r\n",
+            "position": [
+                2202.0742187499986,
+                -346.82031249999983
+            ],
+            "width": 1644.8125,
+            "height": 481.4375,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2443477875016,
+                    "type": "input",
+                    "position": [
+                        0.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#e02c2c",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                },
+                {
+                    "id": 2443477875160,
+                    "type": "output",
+                    "position": [
+                        1644.8125,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#35bc31",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        },
+        {
+            "id": 2443477924600,
+            "title": "Keras Model Predict",
+            "block_type": "code",
+            "source": "prediction = model.predict(x_test[9].reshape(1, 28, 28, 1))",
+            "position": [
+                4207.046874999999,
+                -244.57812499999991
+            ],
+            "width": 1239.6875,
+            "height": 305.9374999999999,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2443477925608,
+                    "type": "input",
+                    "position": [
+                        0.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                },
+                {
+                    "id": 2443477925752,
+                    "type": "output",
+                    "position": [
+                        1239.6875,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        },
+        {
+            "id": 2443477997032,
+            "title": "Keras Model eval",
+            "block_type": "code",
+            "source": "model.evaluate(x_test, y_test)\r\n",
+            "position": [
+                4204.085937499997,
+                -707.0546874999997
+            ],
+            "width": 1628.375,
+            "height": 209.875,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2443477997896,
+                    "type": "input",
+                    "position": [
+                        0.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                },
+                {
+                    "id": 2443477998184,
+                    "type": "output",
+                    "position": [
+                        1628.375,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        },
+        {
+            "id": 2443478874872,
+            "title": "Load MNIST Dataset",
+            "block_type": "code",
+            "source": "print(\"Hello, world\")\r\nfrom tensorflow.keras.datasets import mnist\r\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\r\n",
+            "position": [
+                -535.75,
+                -687.0625
+            ],
+            "width": 739.5,
+            "height": 343.5,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2443478910728,
+                    "type": "output",
+                    "position": [
+                        739.5,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        },
+        {
+            "id": 2443478982728,
+            "title": "Normalize Image Dataset",
+            "block_type": "code",
+            "source": "x_train = x_train.astype('float32') / 255.0\r\nx_test = x_test.astype('float32') / 255.0\r\n\r\n\r\nx_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\r\nx_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\r\n\r\nprint('train:', x_train.shape, '|test:', x_test.shape)",
+            "position": [
+                281.2500000000002,
+                -149.74999999999977
+            ],
+            "width": 705.7499999999998,
+            "height": 357.25,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2443478983592,
+                    "type": "input",
+                    "position": [
+                        0.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                },
+                {
+                    "id": 2443478983880,
+                    "type": "output",
+                    "position": [
+                        705.7499999999998,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        },
+        {
+            "id": 2443479017656,
+            "title": "Build Keras CNN",
+            "block_type": "code",
+            "source": "import tensorflow as tf\r\nfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\r\nfrom tensorflow.keras.models import Sequential\r\n\r\nmodel = Sequential()\r\nmodel.add(Conv2D(28, kernel_size=(3,3), input_shape=x_train.shape[1:]))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(128, activation=tf.nn.relu))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(10,activation=tf.nn.softmax))\r\nprint(\"..\")\r\nmodel.compile(optimizer='adam', \r\n              loss='sparse_categorical_crossentropy', \r\n              metrics=['accuracy'])\r\n",
+            "position": [
+                1316.25,
+                -517.6249999999998
+            ],
+            "width": 680.0,
+            "height": 468.75,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2443479018520,
+                    "type": "input",
+                    "position": [
+                        0.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                },
+                {
+                    "id": 2443479018808,
+                    "type": "output",
+                    "position": [
+                        680.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        },
+        {
+            "id": 2828158533848,
+            "title": "Plot Image Dataset Example",
+            "block_type": "code",
+            "source": "import matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n# Display an example from the dataset\r\nrd_index = np.random.randint(len(x_train))\r\nplt.imshow(x_train[rd_index], cmap='gray')\r\nplt.title('Class '+ str(y_train[0]))\r\n",
+            "position": [
+                433.375,
+                -1221.75
+            ],
+            "width": 778.9375,
+            "height": 763.25,
+            "metadata": {
+                "title_metadata": {
+                    "color": "white",
+                    "font": "Ubuntu",
+                    "size": 10,
+                    "padding": 4.0
+                }
+            },
+            "sockets": [
+                {
+                    "id": 2828158535432,
+                    "type": "input",
+                    "position": [
+                        0.0,
+                        42.0
+                    ],
+                    "metadata": {
+                        "color": "#FF55FFF0",
+                        "linecolor": "#FF000000",
+                        "linewidth": 1.0,
+                        "radius": 6.0
+                    }
+                }
+            ]
+        }
+    ],
+    "edges": [
+        {
+            "id": 1643571233840,
+            "path_type": "bezier",
+            "source": {
+                "block": 2443479017656,
+                "socket": 2443479018808
+            },
+            "destination": {
+                "block": 2443477874008,
+                "socket": 2443477875016
+            }
+        },
+        {
+            "id": 2006783605056,
+            "path_type": "bezier",
+            "source": {
+                "block": 2443478874872,
+                "socket": 2443478910728
+            },
+            "destination": {
+                "block": 2828158533848,
+                "socket": 2828158535432
+            }
+        },
+        {
+            "id": 2006783606064,
+            "path_type": "bezier",
+            "source": {
+                "block": 2443477874008,
+                "socket": 2443477875160
+            },
+            "destination": {
+                "block": 2443477924600,
+                "socket": 2443477925608
+            }
+        },
+        {
+            "id": 2111730223424,
+            "path_type": "bezier",
+            "source": {
+                "block": 2443478982728,
+                "socket": 2443478983880
+            },
+            "destination": {
+                "block": 2443479017656,
+                "socket": 2443479018520
+            }
+        },
+        {
+            "id": 2111730224144,
+            "path_type": "bezier",
+            "source": {
+                "block": 2443477874008,
+                "socket": 2443477875160
+            },
+            "destination": {
+                "block": 2443477997032,
+                "socket": 2443477997896
+            }
+        },
+        {
+            "id": 2111730844864,
+            "path_type": "bezier",
+            "source": {
+                "block": 2443478874872,
+                "socket": 2443478910728
+            },
+            "destination": {
+                "block": 2443478982728,
+                "socket": 2443478983592
+            }
+        }
+    ]
+}
\ No newline at end of file