Merge pull request 'docs_with_new_ui' (#16) from docs_with_new_ui into develop

Reviewed-on: Berufsschule/Veracity_AI#16

.gitignore

@@ -155,7 +155,7 @@ cython_debug/
 
 #ML
 VeraMind-Mini/
-Token.py
+Token.txt
 
 # OS generated files #
 ######################

README.md

@@ -1,3 +1,43 @@
-# 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 MIT license. See the [LICENSE](LICENSE) file for more details.

src/Ai/llm.py

@@ -1,11 +1,15 @@
 from langchain_community.llms import Ollama
-from Ai.Token import get_token
+
 
 class ArticleRater:
     def __init__(self):
         self.client = "https://ai.fabelous.app/v1/ollama/generic"
-        self.headers = {"Authorization": f"Token {get_token()}"}
+        self.token = self._get_token()
+        self.headers = {"Authorization": f"Token {self.token}"}
 
+    def _get_token(self):
+        with open("Token/Token.txt", "r") as t:
+            return t.readline().strip()
     def get_response(self, article, result, confidence):
         ollama_params = {
             "base_url": self.client,

src/controller/mainFrameController.py

@@ -7,7 +7,6 @@ from utils.database.database import FakeNewsChecker
 from models.provider import Provider
 from collections import Counter
 from Ai.llm import ArticleRater
-from Ai.Token import get_token
 
 
 class MainFrameController:
@@ -63,7 +62,7 @@ class MainFrameController:
         confidence_color = "green" if confidence > 80 else ("orange" if confidence > 50 else "red")
         self.frame.confidence_label.configure(fg_color=confidence_color)
         
-        if get_token().strip():
+        if self.rater.token:
             response_stream = self.rater.get_response(text_data.text, text_data.result, confidence)
 
             for chunk in response_stream: