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Merge pull request 'develop' (#18) from develop into main
Gitea Actions Demo / Explore-Gitea-Actions (push) Successful in 9m53s Details 

Reviewed-on: Berufsschule/Veracity_AI#18
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Falko Victor Habel 2024-12-05 11:16:03 +00:00
parent 08b990e658 1715325699
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.gitea/workflows/run.yaml Normal file
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name: Gitea Actions Demo
run-name: ${{ gitea.actor }} is testing out Gitea Actions 🚀
on: [push]
jobs:
Explore-Gitea-Actions:
runs-on: ubuntu-latest
container: catthehacker/ubuntu:act-latest
steps:
- name: Check out repository code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11.7'
- name: Cache pip dependencies
uses: actions/cache@v3
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Install system dependencies
run: |
sudo apt-get update
sudo apt-get install -y python3-tk xvfb
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Download model
run: |
curl -L -o VeraMind-Mini.zip https://gitea.fabelous.app/Fabel/VeraMind/releases/download/latest/VeraMind-Mini.zip
unzip VeraMind-Mini.zip -d ./VeraMind-mini
- name: Run tests with Xvfb
run: |
xvfb-run -a pytest tests/

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#ML
VeraMind-Mini/
Token.txt
# OS generated files #
######################

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README.md
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# VeracityAI
# Veracity_AI
Projekt zum erkennen von Fake News.
## Overview
Veracity_AIn is designed to analyze articles and determine their authenticity using Natural Language Processing (NLP) techniques and machine learning models. The application consists of two main components: a user interface for inputting URLs or text, and a backend system that processes the data and makes predictions about the veracity of the article.
## User Interface
![Fake News Checker UI](/docs/ui/screenshot.png)
The user interface is built using CustomTkinter, a modern and customizable Python UI library. The main screen is divided into three sections:
1. **Input Section**: This section allows users to enter the URL of an article or paste the text directly into the input box.
2. **Result Section**: Displays the result of the analysis (Real or Fake) and the confidence level in percentage.
3. **Leaderboard Section**: Shows a list of news providers along with their fake news percentages, sorted by the highest fake news rate.
## Backend System
The backend system is responsible for processing user input, communicating with the database, and making predictions using the VeraMind model. Here's an overview of its components:
### Data Models
- **TextData**: Stores the URL, text content, provider, result, confidence, and a flag indicating if the news is fake.
- **Provider**: Represents a news provider with attributes for name, total articles, fake articles count, and a list of associated TextData objects.
### Database
The application uses a DuckDB database to store analyzed data. The `FakeNewsChecker` class manages database operations such as inserting new data and fetching existing data.
### Machine Learning Model
- **VeraMindInference**: An inference engine for the VeraMind model, which is used to predict whether an article is real or fake news based on its text content.
- **ArticleRater**: A class that uses the Large Language Model (LLM) to generate a response based on the analyzed text data.
## Usage
To use the Fake News Checker application, follow these steps:
1. Enter an article URL or paste the text directly into the input box.
2. Click on the "Check" button to initiate the analysis process.
3. Once the analysis is complete, the result (Real or Fake) and confidence level will be displayed in the result section.
4. The leaderboard section will automatically update with the latest news providers' fake news percentages.
## License
This application is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International. See the [LICENSE](LICENSE) file for more details.

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1. LLM For our custom LLM
```json
{
"model": "phi3.5:latest",
"model": "mistral-nemo:12b-instruct-2407-q8_0",
"apiBase": "https://ai.fabelous.app/v1/ollama/generic",
"headers": {"Authorization": "Token xxx"},
"system": "System Message",

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Hier sind die User Stories auf Deutsch:
---
### 1. **Artikeltext extrahieren**
- **User Story**: Als Inhaltsanalyst möchte ich Text aus Artikeln extrahieren, damit ich die Rohdaten für Analysen und weitere Verarbeitung verwenden kann.
- **Akzeptanzkriterien**:
- Text kann aus verschiedenen Artikel-Formaten genau extrahiert werden.
- Der extrahierte Text wird in einem strukturierten Format gespeichert, um einfachen Zugriff zu gewährleisten.
- Nicht-Text-Elemente (z.B. Bilder) sind im Output ausgeschlossen.
---
### 2. **Anbindung eines Large Language Models**
- **User Story**: Als Entwickler möchte ich ein großes Sprachmodell integrieren, damit ich natürliche Sprachverarbeitungsfunktionen in unserer Anwendung aktivieren kann.
- **Akzeptanzkriterien**:
- Das Modell ist erfolgreich in die Anwendung integriert.
- Die Anwendung kann Anfragen an das Modell senden und Antworten empfangen.
- Die Integration erfüllt die Anforderungen an Latenz und Leistung.
---
### 3. **Datenbankanbindung**
- **User Story**: Als Data Engineer möchte ich die Anwendung an eine Datenbank anbinden, damit ich Daten effizient speichern und abrufen kann.
- **Akzeptanzkriterien**:
- Die Datenbankverbindung ist stabil und sicher.
- Daten können erfolgreich aus der Datenbank gelesen und in diese geschrieben werden.
- Die Verbindung erfüllt die Anforderungen an Geschwindigkeit und Zuverlässigkeit beim Datenzugriff.
---
### 4. **ML-Trainingsdaten sammeln und labeln**
- **User Story**: Als Data Scientist möchte ich Trainingsdaten für maschinelles Lernen sammeln und labeln, damit ich präzise Vorhersagemodelle erstellen kann.
- **Akzeptanzkriterien**:
- Daten werden in einem strukturierten und konsistenten Format gesammelt.
- Labels werden entsprechend vordefinierter Richtlinien korrekt angewendet.
- Der Datensatz ist ausreichend groß und vielfältig, um das Modelltraining zu unterstützen.
---
### 5. **ML-Modellentwicklung**
- **User Story**: Als Machine Learning Engineer möchte ich ein Modell entwickeln, damit ich Benutzerverhalten vorhersagen und die Funktionalität der Anwendung verbessern kann.
- **Akzeptanzkriterien**:
- Das Modell wird entwickelt und auf Trainingsdaten getestet.
- Die Modellleistung erreicht die in den Anforderungen definierte Genauigkeit.
- Das Modell kann bereitgestellt und in die Anwendung integriert werden.
---
### 6. **UI-Design**
- **User Story**: Als UI-Designer möchte ich eine benutzerfreundliche Oberfläche erstellen, damit Nutzer intuitiv mit der Anwendung interagieren können.
- **Akzeptanzkriterien**:
- Das UI-Design folgt etablierten Designprinzipien und Richtlinien.
- Die Oberfläche wird auf Benutzerfreundlichkeit und Barrierefreiheit getestet.
- Feedback von Testnutzern wird in das endgültige Design integriert.
---
Jede User Story ist darauf ausgelegt, die Ziele und Anforderungen der verschiedenen Beteiligten Ihres Projekts zu erfassen. Lassen Sie mich wissen, wenn Sie zusätzliche Details für eine bestimmte Story benötigen.

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<mxGeometry width="160" relative="1" as="geometry">
<mxPoint x="420" y="600" as="sourcePoint"/>
<mxPoint x="580" y="600" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="49" value="&amp;lt;&amp;lt;include&amp;gt;&amp;gt;" style="edgeStyle=none;html=1;endArrow=open;verticalAlign=bottom;dashed=1;labelBackgroundColor=none;exitX=1;exitY=1;exitDx=0;exitDy=0;entryX=0;entryY=0;entryDx=0;entryDy=0;" parent="1" source="6" target="22" edge="1">
<mxGeometry width="160" relative="1" as="geometry">
<mxPoint x="420" y="600" as="sourcePoint"/>
<mxPoint x="580" y="600" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="51" value="&amp;lt;&amp;lt;include&amp;gt;&amp;gt;" style="edgeStyle=none;html=1;endArrow=open;verticalAlign=bottom;dashed=1;labelBackgroundColor=none;entryX=0.5;entryY=0;entryDx=0;entryDy=0;exitX=0.5;exitY=1;exitDx=0;exitDy=0;" parent="1" source="22" target="31" edge="1">
<mxGeometry width="160" relative="1" as="geometry">
<mxPoint x="420" y="600" as="sourcePoint"/>
<mxPoint x="580" y="600" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="52" value="&amp;lt;&amp;lt;include&amp;gt;&amp;gt;" style="edgeStyle=none;html=1;endArrow=open;verticalAlign=bottom;dashed=1;labelBackgroundColor=none;exitX=0;exitY=1;exitDx=0;exitDy=0;" parent="1" source="28" target="17" edge="1">
<mxGeometry width="160" relative="1" as="geometry">
<mxPoint x="420" y="600" as="sourcePoint"/>
<mxPoint x="580" y="600" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="53" value="&amp;lt;&amp;lt;include&amp;gt;&amp;gt;" style="edgeStyle=none;html=1;endArrow=open;verticalAlign=bottom;dashed=1;labelBackgroundColor=none;entryX=1;entryY=0.5;entryDx=0;entryDy=0;exitX=0;exitY=0.5;exitDx=0;exitDy=0;" parent="1" source="31" target="33" edge="1">
<mxGeometry width="160" relative="1" as="geometry">
<mxPoint x="420" y="600" as="sourcePoint"/>
<mxPoint x="580" y="600" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="54" value="&amp;lt;&amp;lt;extend&amp;gt;&amp;gt;" style="edgeStyle=none;html=1;startArrow=open;endArrow=none;startSize=12;verticalAlign=bottom;dashed=1;labelBackgroundColor=none;exitX=0;exitY=0.5;exitDx=0;exitDy=0;entryX=1;entryY=0.5;entryDx=0;entryDy=0;" edge="1" parent="1" source="28" target="22">
<mxGeometry width="160" relative="1" as="geometry">
<mxPoint x="540" y="800" as="sourcePoint"/>
<mxPoint x="700" y="800" as="targetPoint"/>
</mxGeometry>
</mxCell>
</root>
</mxGraphModel>
</diagram>
</mxfile>

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fake_news_checker.db
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10
requirements.txt Normal file
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@ -0,0 +1,10 @@
customtkinter
Pillow
requests
beautifulsoup4
duckdb
langchain-community==0.3.0
torch
transformers
pytest
pytest-mock

0
src/Ai/__init__.py Normal file
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5
src/Ai/interence.py
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@ -33,6 +33,7 @@ class VeraMindInference:
confidence = prediction if is_fake else 1 - prediction
return {
"result": is_fake,
"confidence": float(confidence)
"result": "FAKE" if is_fake else "REAL",
"confidence": float(confidence),
"is_fake": is_fake
}

43
src/Ai/llm.py Normal file
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@ -0,0 +1,43 @@
from langchain_ollama import ChatOllama
from langchain_core.messages import AIMessage
import os
class ArticleRater:
def __init__(self):
self.client = "https://ai.fabelous.app/v1/ollama/generic"
self.token = self._get_token()
self.headers = {"Authorization": f"Token {self.token}"}
self.model = "phi3.5:3.8b-mini-instruct-q4_K_M"
self.llm = ChatOllama(model=self.model, client_kwargs={'headers': self.headers}, base_url=self.client)
def _get_token(self):
if os.path.exists("Token/Token.txt"):
with open("Token/Token.txt", "r") as t:
return t.readline().strip()
else:
return None
def get_response(self, article, result, confidence):
messages = [
("system", """Ein Mashine Learning Model hat einen Text bewertet, ob es sich um FakeNews handelt oder um Reale News.
Erkläre in 1-2 Sätzen warum dieses Modell zu dieser Entscheidung.
DU SOLLST KEINE ÜBERSCHRIFTEN oder ähnliches ERKLÄREN. Du erhählst einen TEXT und sollst erklären wie das RESULTAT zustande kam"""),
("human", f"{article}, result: {result}, confidence {confidence}")
]
# Return the response stream
return self.llm.stream(messages)
# Usage
if __name__ == "__main__":
article_rater = ArticleRater()
article = """die wöchentliche Glosse von Stefan Kuzmany Thüringer Landtag: AfD will stören - sichert stattdessen Stabilität der Regierung Ramelow Suche starten Suche öffnen Zur Ausgabe Artikel 79 / 79 Eklat bei Landtagssitzung Thüringer Demokratwurst Eine Glosse von Stefan Kuzmany Ordnungsrufe! Mikros aus! Sitzung unterbrochen! Der Thüringer Alterspräsident Jürgen Treutler (AfD) sichert mit kreativer Sitzungsleitung die Stabilität der Regierung Ramelow. 27.09.2024, 13.00 Uhr • aus DER SPIEGEL 40/2024 Zur Merkliste hinzufügen Artikel anhören (2 Minuten) 2 Min X.com Facebook E-Mail Link kopieren Weitere Optionen zum Teilen X.com Facebook E-Mail Messenger WhatsApp Link kopieren Bild vergrößern Jürgen Treutler (AfD) Foto: Bodo Schackow / dpa Sämtliche Sorgen, die AfD könnte nach ihrem dortigen Wahlerfolg die Demokratie in dem osthessischen Bundeslandstrich Thüringen abschaffen, erweisen sich als unbegründet. Tatsächlich zeigte sich auf der konstituierenden Sitzung des Erfurter Landtags am Donnerstag eindrucksvoll die Stabilität des bewährten Systems. Zu verdanken ist diese beruhigende Entwicklung dem wackeren Alterspräsidenten Jürgen Treutler (73), der bei seinem furiosen Debüt als Landesparlamentarier alle Möglichkeiten ausschöpfte, die ihm in seiner Rolle als Sitzungsleiter zustanden – und sogar noch einige mehr. DER SPIEGEL 40/2024 Foto: Melina Mara / The Washington Post / Getty Images Was kommt, falls sie gewinnt?Als Präsidentin würde Kamala Harris in einer krisengeschüttelten Welt regieren. Öffentlich beteuert sie, in die Fußstapfen von Joe Biden zu trreten, aber in der Außenpolitik will Harris eigene Akzente setzen. Für Europa ist das nicht nur eine gute Nachricht – in Sachhen Protektionismus ist sie eine Schülerin Trumps.Lesen Sie unsere Titelgeschichte, weitere Hintergründe und Analysen im digiitalen SPIEGEL. """
result = "REAL"
confidence = 0.67
# Capture the stream response
response_stream = article_rater.get_response(article, result, confidence=confidence)
for chunk in response_stream:
print(chunk.content, end="")

0
src/__init__.py Normal file
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166
src/controller/mainFrameController.py
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@ -1,34 +1,160 @@
from collections import deque
import customtkinter as ctk
from views.mainScreen import MainFrame
from models.data import TextData
from Ai.interence import VeraMindInference
from utils.database.database import FakeNewsChecker
from models.provider import Provider
from collections import Counter
from Ai.llm import ArticleRater
class MainFrameController:
def __init__(self,frame:MainFrame) -> None:
"""
Controller class for the main frame of the application.
Handles user interactions, data processing, and database operations.
"""
def __init__(self, frame: MainFrame) -> None:
"""
Initialize the controller with the main frame and required components.
:param frame: The main frame of the application
"""
self.frame = frame
def get_textdata(self) -> TextData:
self.model_inference = VeraMindInference('VeraMind-Mini')
self.db = FakeNewsChecker()
self.update_provider_list()
self.rater = ArticleRater()
def get_text_data(self) -> TextData:
"""
Retrieve text data from the UI input fields.
:return: TextData object containing URL and text content
"""
text_data = TextData()
text_data.url = self.frame.entry_url.get()
if text_data.text_from_url():
if not text_data.text_from_url():
text_data.text = self.frame.input_textbox.get("0.0", "end")
text_data.provider = "Unknown"
return text_data
def press_check_button(self):
text_data = self.get_textdata()
print(f"text:{text_data.text}")
self.prediction(text_data)
self.frame.result_label.configure(text="", fg_color="#333333")
self.frame.confidence_label.configure(text="", fg_color="#333333")
text_data = self.get_text_data()
if not text_data.text.strip():
return
text_data = self._predict(text_data)
self._add_to_db(text_data)
self.update_provider_list()
self.frame.output_textbox.configure(state="normal")
self.frame.output_textbox.delete("0.0", "end")
self.frame.output_textbox.insert("0.0",f"{text_data.get_output()}")
self.frame.output_textbox.configure(state="disabled")
confidence = text_data.confidence * 100
self.frame.confidence_label.configure(text=f"{confidence:.2f}%")
result_color = "green" if text_data.result == "REAL" else "red"
self.frame.result_label.configure(text=text_data.result, fg_color=result_color)
confidence_color = "green" if confidence > 80 else ("orange" if confidence > 50 else "red")
self.frame.confidence_label.configure(fg_color=confidence_color)
def prediction(self, text_data:TextData) -> TextData:
inference = VeraMindInference('VeraMind-Mini')
result = inference.predict(text_data.text)
if self.rater.token:
response_stream = self.rater.get_response(text_data.text, text_data.result, confidence)
for chunk in response_stream:
self.frame.output_textbox.insert("end", chunk.content)
self.frame.output_textbox.see("end")
self.frame.update_idletasks()
def _predict(self, text_data: TextData) -> TextData:
"""
Make a prediction using the VeraMind model.
:param text_data: TextData object containing the text to analyze
:return: Updated TextData object with prediction results
"""
result = self.model_inference.predict(text_data.text)
text_data.confidence = result["confidence"]
text_data.isfake_news = result["result"]
print(f"Prediction: {'Real' if text_data.isfake_news == 1 else 'Fake'}")
print(f"Confidence: {text_data.confidence}")
return text_data
text_data.result = result["result"]
text_data.is_fake_news = result["is_fake"]
return text_data
def _add_to_db(self, text_data: TextData) -> None:
"""
Add the analyzed data to the database.
:param text_data: TextData object containing the analyzed information
"""
self.db.insert_data(url=text_data.url, anbieter=text_data.get_provider(), is_fake_news= text_data.is_fake_news)
def _fetch_db_data(self):
self.text_data_list = []
data = self.db.fetch_data()
if data:
for row in data:
text_data = TextData(url=row[1], provider=row[2], is_fake_news= row[3])
self.text_data_list.append(text_data)
def sort_provider(self, text_data_list):
# Gruppiere TextData-Objekte nach Provider
provider_groups = {}
for text_data in text_data_list:
if text_data.provider:
if text_data.provider not in provider_groups:
provider_groups[text_data.provider] = []
provider_groups[text_data.provider].append(text_data)
# Zähle die Häufigkeit jedes Providers
provider_counts = Counter(text_data.provider for text_data in text_data_list if text_data.provider)
# Erstelle die Provider-Liste
providers = [
Provider(name, count, provider_groups.get(name, []))
for name, count in provider_counts.items()
]
# Sortiere die Provider-Liste nach dem Fake-Prozentsatz (absteigend)
sorted_providers = sorted(providers, key=lambda x: x.get_fake_percentage(), reverse=True)
return sorted_providers
def update_provider_list(self):
self._fetch_db_data()
# Lösche vorhandene Einträge in der scrollbaren Ansicht
for widget in self.frame.provider_container.winfo_children():
widget.destroy()
# Sortiere und zähle die Provider
sorted_providers = self.sort_provider(self.text_data_list)
# Füge die sortierten Provider zur scrollbaren Ansicht hinzu
for i, provider in enumerate(sorted_providers):
provider_frame = ctk.CTkFrame(self.frame.provider_container)
provider_frame.pack(fill="x", padx=5, pady=2)
name_label = ctk.CTkLabel(provider_frame, text=provider.title)
name_label.pack(side="left", padx=5)
count_label = ctk.CTkLabel(provider_frame, text=str(provider.get_fake_percentage())+"%")
count_label.pack(side="right", padx=5)
def _update_output(self, output: str) -> None:
"""
Update the output text box with the result.
:param output: String containing the output to display
"""
self.frame.output_textbox.configure(state="normal")
self.frame.output_textbox.delete("0.0", "end")
self.frame.output_textbox.insert("0.0", output)
self.frame.output_textbox.configure(state="disabled")

21
src/main.py
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@ -1,6 +1,10 @@
import customtkinter
from views.mainScreen import MainFrame
from controller.mainFrameController import MainFrameController
import os
from PIL import ImageTk
class Main(customtkinter.CTk):
def __init__(self) -> None:
@ -9,18 +13,21 @@ class Main(customtkinter.CTk):
self.grid_rowconfigure(0, weight=1)
self.grid_columnconfigure(0, weight=1)
mainFrame = MainFrame(self)
mainFrame.grid(row=0, column=0, padx=10, pady=10,sticky="nsew")
controller_mainframe = MainFrameController(mainFrame)
mainFrame.set_controller(controller_mainframe)
self.iconpath = ImageTk.PhotoImage(file=os.path.join("src/ui","logo.png"))
self.wm_iconbitmap()
self.iconphoto(False, self.iconpath)
self.title("VeracityAI")
main_frame = MainFrame(self)
main_frame.grid(row=0, column=0, padx=10, pady=10,sticky="nsew")
controller_mainframe = MainFrameController(main_frame)
main_frame.set_controller(controller_mainframe)
self.title("Veracity_AI")
self.geometry("800x500")
if __name__ == "__main__":
customtkinter.deactivate_automatic_dpi_awareness()
customtkinter.set_default_color_theme('theme.json')
customtkinter.set_default_color_theme('theme/theme.json')
customtkinter.set_appearance_mode("dark")
app = Main()
app.mainloop()

51
src/models/data.py
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@ -1,10 +1,15 @@
from urllib.parse import urlparse
from utils.webTextExtractor import WebTextExtractor
class TextData:
def __init__(self, url: str = "") -> None:
def __init__(self, url: str = "",text: str = "",result: str = "", is_fake_news: bool = False, provider: str = "") -> None:
self.url = url
self.text = ""
self.isfake_news = False
self.text = text
self.result = result
self.is_fake_news = is_fake_news
self.provider = provider
self.confidence = None
self._extractor = None
@ -15,8 +20,7 @@ class TextData:
def text_from_url(self)-> bool:
if not self.url:
print("No url")
return True
return False
if not self.text:
print("Extrahiere Text von URL...")
@ -24,11 +28,40 @@ class TextData:
self._extractor.fetch_content()
self._extractor.extract_text()
self.text = self._extractor.get_text()
return False
return True
def get_output(self):
if self.confidence != None:
output = f"Prediction: {'Real' if self.isfake_news else 'Fake'}" + f" Confidence: {self.confidence:.4f}"
print(output)
return output
output = f"Prediction: {self.result}" + f" Confidence: {self.confidence:.4f}"
return output
def get_provider(self)-> str:
self.extract_provider()
return self.provider
def extract_provider(self):
"""
Extract the domain (anbieter) from a given URL.
:param url: The URL to process
:return: The extracted domain or None if the URL is invalid
"""
if not self._is_valid_url(self.url):
self.provider = "Unknown"
parsed_url = urlparse(self.url)
domain_parts = parsed_url.netloc.split('.')
self.provider = f"{domain_parts[-2]}.{domain_parts[-1]}" if len(domain_parts) >= 2 else "Unknown"
def _is_valid_url(self, url: str) -> bool:
"""
Check if a given URL is valid.
:param url: The URL to validate
:return: True if the URL is valid, False otherwise
"""
try:
result = urlparse(url)
return all([result.scheme, result.netloc])
except ValueError:
return False

22
src/models/provider.py Normal file
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@ -0,0 +1,22 @@
class Provider():
def __init__(self, title: str, count: int, text_data_list) -> None:
self.title = title
self.count = count
self.text_data_list = text_data_list
def get_fake_percentage(self) -> float:
count_all = 0
count_fake = 0
for text_data in self.text_data_list:
count_all += 1
if text_data.is_fake_news:
count_fake += 1
if count_all == 0:
return 0.0
return round((count_fake / count_all) * 100, 2)

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43
src/utils/database/database.py
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@ -1,4 +1,4 @@
import sqlite3
import duckdb
class FakeNewsChecker:
def __init__(self, db_name='fake_news_checker.db'):
@ -6,49 +6,50 @@ class FakeNewsChecker:
self.create_table()
def create_connection(self):
return sqlite3.connect(self.db_name)
return duckdb.connect(self.db_name)
def create_table(self):
conn = self.create_connection()
cursor = conn.cursor()
cursor.execute('''
conn.execute('''
CREATE TABLE IF NOT EXISTS url_info (
id INTEGER PRIMARY KEY AUTOINCREMENT,
url TEXT NOT NULL,
anbieter TEXT NOT NULL,
id INTEGER PRIMARY KEY,
url VARCHAR NOT NULL,
anbieter VARCHAR NOT NULL,
is_fake_news BOOLEAN NOT NULL
)
''')
conn.commit()
conn.close()
def get_next_id(self):
conn = self.create_connection()
result = conn.execute('SELECT COALESCE(MAX(id), 0) + 1 FROM url_info').fetchone()
conn.close()
return result[0]
def insert_data(self, url, anbieter, is_fake_news):
conn = self.create_connection()
cursor = conn.cursor()
cursor.execute('''
INSERT INTO url_info (url, anbieter, is_fake_news)
VALUES (?, ?, ?)
''', (url, anbieter, is_fake_news))
conn.commit()
next_id = self.get_next_id()
conn.execute('''
INSERT INTO url_info (id, url, anbieter, is_fake_news)
VALUES (?, ?, ?, ?)
''', [next_id, url, anbieter, bool(is_fake_news)])
conn.close()
def fetch_data(self):
conn = self.create_connection()
cursor = conn.cursor()
cursor.execute('SELECT * FROM url_info')
rows = cursor.fetchall()
result = conn.execute('SELECT * FROM url_info').fetchall()
conn.close()
return rows
return result
# Beispielnutzung der Klasse
if __name__ == '__main__':
checker = FakeNewsChecker()
# Daten hinzufügen
checker.insert_data('https://example.com/news/123', 'Example News', 0)
checker.insert_data('https://fakenews.com/article/456', 'Fake News', 1)
checker.insert_data('https://example.com/news/123', 'Example News', False)
checker.insert_data('https://fakenews.com/article/456', 'Fake News', True)
# Daten abrufen
data = checker.fetch_data()
for row in data:
print(f"ID: {row[0]}, URL: {row[1]}, Anbieter: {row[2]}, Fake News: {'Ja' if row[3] else 'Nein'}")
print(f"ID: {row[0]}, URL: {row[1]}, Anbieter: {row[2]}, Fake News: {'Ja' if row[3] else 'Nein'}")

20
src/utils/webTextExtractor.py
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@ -22,6 +22,7 @@ class WebTextExtractor:
self.text = soup.get_text()
# Optional: Entferne überflüssige Leerzeichen und Zeilenumbrüche
self.text = ' '.join(self.text.split())
self.text = self.resize_article(self.text)
else:
raise Exception("Kein Inhalt zum Parsen. Bitte zuerst fetch_content() aufrufen.")
@ -31,6 +32,23 @@ class WebTextExtractor:
return self.text
else:
raise Exception("Kein Text extrahiert. Bitte zuerst extract_text() aufrufen.")
def resize_article(self, article):
"""Resizes the article by removing the first 30 words and limiting the length to 512 words."""
# Split the article into a list of words
words = article.split()
# Remove the first 30 words
words = words[30:]
# Calculate the number of words to keep (up to 512 words)
num_to_keep = min(512, len(words))
# Slice the list of words to keep only the first num_to_keep words
words = words[:num_to_keep]
# Join the remaining words back into a single string
resized_article = ' '.join(words)
return resized_article
# Beispielaufruf

64
src/views/mainScreen.py
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@ -1,43 +1,67 @@
from typing import Any
import customtkinter as ctk
class MainFrame(ctk.CTkFrame):
class MainFrame(ctk.CTkFrame):
def __init__(self, master: Any, **kwargs):
super().__init__(master, **kwargs)
self.controller = None
# Konfiguriere das Hauptframe, um sich zu dehnen
# Configure the main frame to stretch
self.grid_rowconfigure(0, weight=1)
self.grid_columnconfigure(0, weight=1) # Linke Spalte soll sich dehnen
self.grid_columnconfigure(1, weight=0) # Mittlere Spalte (Button) soll sich nicht dehnen
self.grid_columnconfigure(2, weight=1) # Rechte Spalte soll sich dehnen
self.grid_columnconfigure(0, weight=1) # Left column should stretch
self.grid_columnconfigure(1, weight=0) # Middle column (button) should not stretch
self.grid_columnconfigure(2, weight=1) # Right column should stretch
# Linkes Frame
# Left frame
self.frame1 = ctk.CTkFrame(self)
self.frame1.grid(row=0, column=0, sticky="nsew", padx=10, pady=10)
self.frame1.grid_rowconfigure(2, weight=1) # Lasse die Output-Textbox wachsen
self.frame1.grid_columnconfigure(0, weight=1) # Lasse frame1 horizontal wachsen
self.frame1.grid_rowconfigure(3, weight=1)
self.frame1.grid_columnconfigure(0, weight=1)
self.entry_url = ctk.CTkEntry(self.frame1, placeholder_text='Web link to article', height=50)
self.entry_url.grid(row=0, column=0, padx=10, pady=10, sticky="ew")
self.entry_url = ctk.CTkEntry(self.frame1, placeholder_text='Enter the article link', height=50)
self.entry_url.grid(row=0, column=0,columnspan=2, padx=10, pady=10, sticky="ew")
self.input_textbox = ctk.CTkTextbox(self.frame1, height=200)
self.input_textbox.grid(row=1, column=0, padx=10, pady=10, sticky="nsew")
# Middle button
self.check_button = ctk.CTkButton(self.frame1, text="Check", width=60, height=50, command=self.check_button_event)
self.check_button.grid(row=0, column=2,columnspan=1, padx=10, pady=10, sticky="e")
self.output_textbox = ctk.CTkTextbox(self.frame1, height=150, state="disabled")
self.output_textbox.grid(row=2, column=0, padx=10, pady=10, sticky="nsew")
# Input Checkbox
self.input_textbox = ctk.CTkTextbox(self.frame1, height=125)
self.input_textbox.grid(row=1, column=0,columnspan=3, padx=10, pady=10, sticky="nsew")
# Mittlerer Button
self.check_button = ctk.CTkButton(self, text="Check", width=60, height=300, command=self.check_button_event)
self.check_button.grid(row=0, column=1, padx=10, pady=10, sticky="nsew")
# Frame for Result and Confidence labels
self.label_frame = ctk.CTkFrame(self.frame1, fg_color="#333333")
self.label_frame.grid(row=2, column=0, columnspan=3, padx=10, pady=10, sticky="ew")
self.label_frame.grid_columnconfigure(0, weight=1)
self.label_frame.grid_columnconfigure(1, weight=1)
# Rechte scrollbare Ansicht
# Result label
self.result_label = ctk.CTkLabel(self.label_frame, text="", height=50, fg_color="#333333", corner_radius=5)
self.result_label.grid(row=0, column=0, padx=(0, 5), pady=0, sticky="ew")
# Confidence label
self.confidence_label = ctk.CTkLabel(self.label_frame, text="", height=50, fg_color="#333333", corner_radius=5)
self.confidence_label.grid(row=0, column=1, padx=(5, 0), pady=0, sticky="ew")
# Ensure equal width for both labels
self.label_frame.grid_columnconfigure(0, weight=1, minsize=200)
self.label_frame.grid_columnconfigure(1, weight=1, minsize=200)
self.output_textbox = ctk.CTkTextbox(self.frame1, height=175, state="disabled")
self.output_textbox.grid(row=3, column=0, columnspan=3, padx=10, pady=10, sticky="nsew")
# Right scrollable view
self.scrollview = ctk.CTkScrollableFrame(self)
self.scrollview.grid(row=0, column=2, padx=10, pady=10, sticky="nsew")
# Überschrift hinzufügen
# Add header
self.header = ctk.CTkLabel(self.scrollview, text="Leaderboard", font=("Arial", 24, "bold"))
self.header.pack(pady=10, padx=10, anchor="w")
# Container for provider entries
self.provider_container = ctk.CTkFrame(self.scrollview)
self.provider_container.pack(fill="both", expand=True)
def set_controller(self, controller):
self.controller = controller

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import pytest
import torch
import os
import sys
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from Ai.interence import VeraMindInference
@pytest.fixture
def model_fixture():
model_path = "VeraMind-mini"
max_len = 512
return VeraMindInference(model_path, max_len)
def test_init(model_fixture):
assert model_fixture.device.type == "cuda" if torch.cuda.is_available() else "cpu"
def test_predict_fake(model_fixture):
fake_text = "Das ist sehr traurig"
prediction = model_fixture.predict(fake_text)
assert prediction["result"] == "FAKE", f"Expected FAKE, got {prediction['result']}"
assert prediction["confidence"] > 0.5, f"Confidence {prediction['confidence']} is not > 0.5"
assert prediction["is_fake"] in [True, 1], f"Expected is_fake to be True, got {prediction['is_fake']}"
def test_predict_real(model_fixture):
real_text = "Das sind die Freitag Abend Nachrichten"
prediction = model_fixture.predict(real_text)
assert prediction["result"] == "REAL", f"Expected REAL, got {prediction['result']}"
assert prediction["confidence"] > 0.5, f"Confidence {prediction['confidence']} is not > 0.5"
assert prediction["is_fake"] in [False, 0], f"Expected is_fake to be False or 0, got {prediction['is_fake']}"
def test_predict_confidence_range(model_fixture):
for _ in range(5):
text = "Insert a random text for testing"
prediction = model_fixture.predict(text)
assert 0 <= prediction["confidence"] <= 1, f"Confidence {prediction['confidence']} is not between 0 and 1"
if __name__ == "__main__":
pytest.main([__file__])

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tests/__init__.py Normal file
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tests/controller/test_mainFrameController.py Normal file
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import sys
import os
import pytest
from unittest.mock import MagicMock, patch
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from controller.mainFrameController import MainFrameController
from views.mainScreen import MainFrame
from models.data import TextData
@pytest.fixture
def mock_main_frame():
mock_frame = MagicMock(spec=MainFrame)
mock_frame.provider_container = MagicMock()
mock_frame.entry_url = MagicMock()
mock_frame.input_textbox = MagicMock()
return mock_frame
@pytest.fixture
def controller(mock_main_frame):
with patch('controller.mainFrameController.VeraMindInference'), \
patch('controller.mainFrameController.FakeNewsChecker'), \
patch('controller.mainFrameController.ArticleRater'):
return MainFrameController(mock_main_frame)
def test_init(controller):
assert isinstance(controller.frame, MagicMock)
assert isinstance(controller.frame.provider_container, MagicMock)
assert isinstance(controller.model_inference, MagicMock)
assert isinstance(controller.db, MagicMock)
assert isinstance(controller.rater, MagicMock)
def test_get_text_data(controller):
controller.frame.entry_url.get.return_value = "https://example.com"
controller.frame.input_textbox.get.return_value = "Sample text"
with patch('models.data.TextData.text_from_url', return_value=False):
text_data = controller.get_text_data()
assert isinstance(text_data, TextData)
assert text_data.url == "https://example.com"
assert text_data.text == "Sample text"
assert text_data.provider == "Unknown"
@pytest.mark.parametrize("result, expected_result_color, confidence, expected_confidence_color", [
("REAL", "green", 0.85, "green"), # High confidence for REAL result
("REAL", "green", 0.65, "orange"), # Medium confidence for REAL result
("REAL", "green", 0.45, "red"), # Low confidence for REAL result
("FAKE", "red", 0.85, "green"), # High confidence for FAKE result
("FAKE", "red", 0.65, "orange"), # Medium confidence for FAKE result
("FAKE", "red", 0.45, "red"), # Low confidence for FAKE result
])
def test_press_check_button(controller, result, expected_result_color, confidence, expected_confidence_color):
# Mock controller methods and properties
controller.get_text_data = MagicMock(return_value=TextData(text="Sample text"))
text_data = TextData(text="Sample text")
text_data.result = result
text_data.confidence = confidence
text_data.is_fake_news = (result == "FAKE")
controller._predict = MagicMock(return_value=text_data)
controller._add_to_db = MagicMock()
controller.update_provider_list = MagicMock()
# Create a mock response chunk with a content attribute
class MockChunk:
def __init__(self, content):
self.content = content
# Mocking get_response to return a list of MockChunk instances
mock_response = [MockChunk("Sample response")]
controller.rater.get_response = MagicMock(return_value=mock_response)
# Mock frame and its subcomponents
controller.frame = MagicMock()
controller.frame.result_label.configure = MagicMock()
controller.frame.confidence_label.configure = MagicMock()
controller.frame.output_textbox = MagicMock()
controller.frame.output_textbox.insert = MagicMock()
# Call the method
controller.press_check_button()
# Assertions for result label and confidence label colors
controller.frame.result_label.configure.assert_called_with(text=result, fg_color=expected_result_color)
controller.frame.confidence_label.configure.assert_any_call(text=f"{confidence * 100:.2f}%")
controller.frame.confidence_label.configure.assert_any_call(fg_color=expected_confidence_color)
# Additional assertion to verify that the output textbox is updated with the response content
controller.frame.output_textbox.insert.assert_called_with("end", mock_response[0].content)
def test_predict(controller):
text_data = TextData(text="Sample text")
controller.model_inference.predict.return_value = {
"confidence": 0.9,
"result": "REAL",
"is_fake": False
}
result = controller._predict(text_data)
assert result.confidence == 0.9
assert result.result == "REAL"
assert result.is_fake_news == False
def test_add_to_db(controller):
text_data = TextData(url="https://example.com", provider="Example Provider", is_fake_news=False)
controller._add_to_db(text_data)
controller.db.insert_data.assert_called_with(
url="https://example.com",
anbieter="example.com",
is_fake_news=False
)
if __name__ == "__main__":
pytest.main([__file__])

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import pytest
from unittest.mock import MagicMock, patch
import os
import sys
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from models.data import TextData
def test_init():
data = TextData()
assert data.url == ""
assert data.text == ""
assert data.result == ""
assert data.is_fake_news == False
assert data.provider == ""
assert data.confidence is None
assert data._extractor is None
def test_set_url():
data = TextData()
url = "https://www.example.com"
data.set_url(url)
assert data.url == url
assert data.text == ""
assert data._extractor is None
def test_text_from_url_with_url():
data = TextData()
url = "https://www.example.com"
data.set_url(url)
# Mock the WebTextExtractor
mock_extractor = MagicMock()
mock_extractor.get_text.return_value = "Example text"
# Patch the WebTextExtractor import in the TextData module
with patch('models.data.WebTextExtractor', return_value=mock_extractor):
result = data.text_from_url()
assert result is True
assert data.text == "Example text"
mock_extractor.fetch_content.assert_called_once()
mock_extractor.extract_text.assert_called_once()
mock_extractor.get_text.assert_called_once()
def test_text_from_url_without_url():
data = TextData()
assert data.text_from_url() is False
def test_get_output():
data = TextData()
data.result = "Fake"
data.confidence = 0.95
output = data.get_output()
assert output == "Prediction: Fake Confidence: 0.9500"
def test_get_provider():
data = TextData()
url = "https://www.example.com"
data.set_url(url)
assert data.get_provider() == "example.com"
def test_extract_provider():
data = TextData()
url = "https://www.example.com"
data.set_url(url)
data.extract_provider()
assert data.provider == "example.com"
def test_extract_provider_with_invalid_url():
data = TextData()
url = "invalid_url"
data.set_url(url)
data.extract_provider()
assert data.provider == "Unknown"
def test__is_valid_url_with_valid_url():
data = TextData()
url = "https://www.example.com"
assert data._is_valid_url(url) is True
def test__is_valid_url_with_invalid_url():
data = TextData()
url = "invalid_url"
assert data._is_valid_url(url) is False
if __name__ == "__main__":
pytest.main([__file__])

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tests/models/test_provider.py Normal file
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import pytest
import sys
import os
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from models.provider import Provider
from models.data import TextData # Assuming this is the class used for text_data_list
def test_provider_init():
title = "Test Provider"
count = 10
text_data_list = [TextData(is_fake_news=True), TextData(is_fake_news=False)]
provider = Provider(title, count, text_data_list)
assert provider.title == title
assert provider.count == count
assert provider.text_data_list == text_data_list
def test_get_fake_percentage():
text_data_list = [TextData(is_fake_news=False), TextData(is_fake_news=False), TextData(is_fake_news=True)]
provider = Provider("Test Provider", 10, text_data_list)
assert provider.get_fake_percentage() == 33.33
def test_get_fake_percentage_zero_division():
text_data_list = []
provider = Provider("Test Provider", 10, text_data_list)
assert provider.get_fake_percentage() == 0.0
if __name__ == "__main__":
pytest.main([__file__])

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import pytest
import customtkinter
import sys
import os
from unittest.mock import patch
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'src'))
sys.path.insert(0, src_dir)
from main import Main
def test_main_initialization(mocker):
# Mocking the MainFrame and MainFrameController to avoid actual UI creation
mocker.patch('main.MainFrame')
mocker.patch('main.MainFrameController')
# Initialize the Main class
app = Main()
# Check if the title is set correctly
assert app.title() == "Veracity_AI"
# Check if the grid configuration is set correctly
assert app.grid_rowconfigure(0)['weight'] == 1
assert app.grid_columnconfigure(0)['weight'] == 1
# Check if the icon is set correctly
assert app.iconpath is not None
if __name__ == "__main__":
pytest.main([__file__])

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tests/utils/test_database.py Normal file
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import pytest
import sys
import os
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from utils.database.database import FakeNewsChecker
@pytest.fixture(scope="module")
def checker():
checker = FakeNewsChecker()
yield checker
checker.create_connection().execute('DELETE FROM url_info')
checker.create_connection().close()
def test_create_table(checker):
conn = checker.create_connection()
# Inspect the actual table structure
result = conn.execute('PRAGMA table_info(url_info)').fetchall()
actual_columns = [(col[1], col[2], col[2], col[3], col[4], col[5]) for col in result]
# Compare the actual columns to the expected
expected_columns = [
('id', 'INTEGER', 'INTEGER', 1, None, 1),
('url', 'VARCHAR', 'VARCHAR', 1, None, 0),
('anbieter', 'VARCHAR', 'VARCHAR', 1, None, 0),
('is_fake_news', 'BOOLEAN', 'BOOLEAN', 1, None, 0),
]
assert actual_columns == expected_columns
# Clean up the test data
conn.execute('DELETE FROM url_info')
conn.commit()
conn.close()
def test_get_next_id(checker):
assert checker.get_next_id() == 1
def test_insert_data(checker):
checker.insert_data('https://example.com/news/123', 'Example News', False)
data = checker.fetch_data()
assert len(data) == 1
assert data[0] == (1, 'https://example.com/news/123', 'Example News', False)
checker.create_connection().execute('DELETE FROM url_info')
checker.create_connection().commit()
def test_fetch_data(checker):
checker.insert_data('https://example.com/news/123', 'Example News', False)
checker.insert_data('https://fakenews.com/article/456', 'Fake News', True)
data = checker.fetch_data()
assert len(data) == 2
assert data[0] == (1, 'https://example.com/news/123', 'Example News', False)
assert data[1] == (2, 'https://fakenews.com/article/456', 'Fake News', True)
checker.create_connection().execute('DELETE FROM url_info')
checker.create_connection().commit()
if __name__ == "__main__":
pytest.main([__file__])

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tests/utils/test_webText_Extractor.py Normal file
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import unittest.mock
import pytest
import os
import sys
from unittest.mock import MagicMock
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from utils.webTextExtractor import WebTextExtractor
@pytest.fixture
def web_text_extractor():
return WebTextExtractor("https://example.com")
def test_fetch_content(web_text_extractor):
web_text_extractor.fetch_content()
assert web_text_extractor.content is not None
def test_extract_text(web_text_extractor):
web_text_extractor.fetch_content()
web_text_extractor.extract_text()
assert web_text_extractor.text is not None
def test_get_text(web_text_extractor):
# Mock the fetch_content method to set some content
web_text_extractor.fetch_content = MagicMock()
# Set the content that fetch_content would provide
web_text_extractor.content = "Some content from the webpage"
# Mock extract_text to simulate its behavior
def mock_extract_text():
web_text_extractor.text = "Example text" # Simulate the extraction of text
web_text_extractor.extract_text = MagicMock(side_effect=mock_extract_text)
# Call the mocked fetch_content method
web_text_extractor.fetch_content()
# Call the extract_text() method, which will now set the text
web_text_extractor.extract_text()
# Call the get_text() method
result = web_text_extractor.get_text()
# Assert that the result is not None
assert result is not None
# Assert that fetch_content and extract_text were called
web_text_extractor.fetch_content.assert_called_once()
web_text_extractor.extract_text.assert_called_once()
# Assert that the return value of get_text() is "Example text"
assert result == "Example text"
def test_resize_article(web_text_extractor):
# Create a long article text for testing
article = " ".join(["This is a test article"] * 600)
resized_article = web_text_extractor.resize_article(article)
# Check if the resized article has the expected length
assert len(resized_article.split()) == 512
# Check if the resized article starts with the 31st word of the original article
assert resized_article.split()[0] == "This"
assert resized_article.split()[1] == "is"
assert resized_article.split()[2] == "a"
assert resized_article.split()[3] == "test"
assert resized_article.split()[4] == "article"
if __name__ == "__main__":
pytest.main([__file__])

91
tests/views/test_mainScreen.py Normal file
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import pytest
import customtkinter as ctk
import sys
import os
# Add the src directory to the Python path
src_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'src'))
sys.path.insert(0, src_dir)
from views.mainScreen import MainFrame
def test_mainframe_initialization(mocker):
# Mocking the controller to avoid actual UI creation
mocker.patch('views.mainScreen.MainFrame')
# Initialize the MainFrame class
main_frame = MainFrame(None)
# Check if the grid configuration is set correctly
assert main_frame.grid_rowconfigure(0)['weight'] == 1
assert main_frame.grid_columnconfigure(0)['weight'] == 1
assert main_frame.grid_columnconfigure(1)['weight'] == 0
assert main_frame.grid_columnconfigure(2)['weight'] == 1
# Check if the entry_url is created correctly
assert isinstance(main_frame.entry_url, ctk.CTkEntry)
# Check if the check_button is created correctly
assert isinstance(main_frame.check_button, ctk.CTkButton)
# Check if the input_textbox is created correctly
assert isinstance(main_frame.input_textbox, ctk.CTkTextbox)
# Check if the label_frame is created correctly
assert isinstance(main_frame.label_frame, ctk.CTkFrame)
# Check if the result_label is created correctly
assert isinstance(main_frame.result_label, ctk.CTkLabel)
# Check if the confidence_label is created correctly
assert isinstance(main_frame.confidence_label, ctk.CTkLabel)
# Check if the output_textbox is created correctly
assert isinstance(main_frame.output_textbox, ctk.CTkTextbox)
# Check if the scrollview is created correctly
assert isinstance(main_frame.scrollview, ctk.CTkScrollableFrame)
# Check if the header is created correctly
assert isinstance(main_frame.header, ctk.CTkLabel)
# Check if the provider_container is created correctly
assert isinstance(main_frame.provider_container, ctk.CTkFrame)
def test_set_controller(mocker):
# Mocking the controller to avoid actual UI creation
mocker.patch('views.mainScreen.MainFrame')
# Initialize the MainFrame class
main_frame = MainFrame(None)
# Create a mock controller
mock_controller = mocker.Mock()
# Set the controller
main_frame.set_controller(mock_controller)
# Check if the controller is set correctly
assert main_frame.controller == mock_controller
def test_check_button_event(mocker):
# Mocking the controller to avoid actual UI creation
mocker.patch('views.mainScreen.MainFrame')
# Initialize the MainFrame class
main_frame = MainFrame(None)
# Create a mock controller
mock_controller = mocker.Mock()
# Set the controller
main_frame.set_controller(mock_controller)
# Call the check_button_event method
main_frame.check_button_event()
# Check if the press_check_button method of the controller is called
mock_controller.press_check_button.assert_called_once()
if __name__ == "__main__":
pytest.main([__file__])

359
theme.json
View File

@ -1,359 +0,0 @@
{
"CTk": {
"fg_color": [
"gray92",
"gray14"
]
},
"CTkToplevel": {
"fg_color": [
"gray92",
"gray14"
]
},
"CTkFrame": {
"corner_radius": 6,
"border_width": 0,
"fg_color": [
"gray86",
"gray17"
],
"top_fg_color": [
"gray81",
"gray20"
],
"border_color": [
"gray65",
"gray28"
]
},
"CTkButton": {
"corner_radius": 6,
"border_width": 0,
"fg_color": [
"#80ffff",
"#008080"
],
"hover_color": [
"#80ffff",
"#00bfbf"
],
"border_color": [
"#3E454A",
"#949A9F"
],
"text_color": [
"#DCE4EE",
"#DCE4EE"
],
"text_color_disabled": [
"gray74",
"gray60"
]
},
"CTkLabel": {
"corner_radius": 0,
"fg_color": "transparent",
"text_color": [
"gray10",
"#DCE4EE"
]
},
"CTkEntry": {
"corner_radius": 6,
"border_width": 2,
"fg_color": [
"#F9F9FA",
"#343638"
],
"border_color": [
"#979DA2",
"#565B5E"
],
"text_color": [
"gray10",
"#DCE4EE"
],
"placeholder_text_color": [
"gray52",
"gray62"
]
},
"CTkCheckBox": {
"corner_radius": 6,
"border_width": 3,
"fg_color": [
"#80ffff",
"#008080"
],
"border_color": [
"#3E454A",
"#949A9F"
],
"hover_color": [
"#00ffff",
"#009595"
],
"checkmark_color": [
"#DCE4EE",
"gray90"
],
"text_color": [
"gray10",
"#DCE4EE"
],
"text_color_disabled": [
"gray60",
"gray45"
]
},
"CTkSwitch": {
"corner_radius": 1000,
"border_width": 3,
"button_length": 0,
"fg_color": [
"#939BA2",
"#4A4D50"
],
"progress_color": [
"#55ffff",
"#00bfbf"
],
"button_color": [
"gray36",
"#D5D9DE"
],
"button_hover_color": [
"gray20",
"gray100"
],
"text_color": [
"gray10",
"#DCE4EE"
],
"text_color_disabled": [
"gray60",
"gray45"
]
},
"CTkRadioButton": {
"corner_radius": 1000,
"border_width_checked": 6,
"border_width_unchecked": 3,
"fg_color": [
"#80ffff",
"#008080"
],
"border_color": [
"#3E454A",
"#949A9F"
],
"hover_color": [
"#36719F",
"#144870"
],
"text_color": [
"gray10",
"#DCE4EE"
],
"text_color_disabled": [
"gray60",
"gray45"
]
},
"CTkProgressBar": {
"corner_radius": 1000,
"border_width": 0,
"fg_color": [
"#939BA2",
"#4A4D50"
],
"progress_color": [
"#80ffff",
"#008080"
],
"border_color": [
"gray",
"gray"
]
},
"CTkSlider": {
"corner_radius": 1000,
"button_corner_radius": 1000,
"border_width": 6,
"button_length": 0,
"fg_color": [
"#939BA2",
"#4A4D50"
],
"progress_color": [
"gray40",
"#AAB0B5"
],
"button_color": [
"#80ffff",
"#008080"
],
"button_hover_color": [
"#55ffff",
"#00bfbf"
]
},
"CTkOptionMenu": {
"corner_radius": 6,
"fg_color": [
"#80ffff",
"#008080"
],
"button_color": [
"#80ffff",
"#006a6a"
],
"button_hover_color": [
"#80ffff",
"#00bfbf"
],
"text_color": [
"#DCE4EE",
"#DCE4EE"
],
"text_color_disabled": [
"gray74",
"gray60"
]
},
"CTkComboBox": {
"corner_radius": 6,
"border_width": 2,
"fg_color": [
"#F9F9FA",
"#343638"
],
"border_color": [
"#979DA2",
"#565B5E"
],
"button_color": [
"#979DA2",
"#565B5E"
],
"button_hover_color": [
"#6E7174",
"#7A848D"
],
"text_color": [
"gray10",
"#DCE4EE"
],
"text_color_disabled": [
"gray50",
"gray45"
]
},
"CTkScrollbar": {
"corner_radius": 1000,
"border_spacing": 4,
"fg_color": "transparent",
"button_color": [
"gray55",
"gray41"
],
"button_hover_color": [
"gray40",
"gray53"
]
},
"CTkSegmentedButton": {
"corner_radius": 6,
"border_width": 2,
"fg_color": [
"#979DA2",
"gray29"
],
"selected_color": [
"#80ffff",
"#008080"
],
"selected_hover_color": [
"#80ffff",
"#00bfbf"
],
"unselected_color": [
"#979DA2",
"gray29"
],
"unselected_hover_color": [
"gray70",
"gray41"
],
"text_color": [
"#DCE4EE",
"#DCE4EE"
],
"text_color_disabled": [
"gray74",
"gray60"
]
},
"CTkTextbox": {
"corner_radius": 6,
"border_width": 0,
"fg_color": [
"#F9F9FA",
"#1D1E1E"
],
"border_color": [
"#979DA2",
"#565B5E"
],
"text_color": [
"gray10",
"#DCE4EE"
],
"scrollbar_button_color": [
"gray55",
"gray41"
],
"scrollbar_button_hover_color": [
"gray40",
"gray53"
]
},
"CTkScrollableFrame": {
"label_fg_color": [
"gray78",
"gray23"
]
},
"DropdownMenu": {
"fg_color": [
"gray90",
"gray20"
],
"hover_color": [
"gray75",
"gray28"
],
"text_color": [
"gray10",
"gray90"
]
},
"CTkFont": {
"macOS": {
"family": "SF Display",
"size": 13,
"weight": "normal"
},
"Windows": {
"family": "Roboto",
"size": 13,
"weight": "normal"
},
"Linux": {
"family": "Roboto",
"size": 13,
"weight": "normal"
}
}
}