final

.gitignore

@@ -153,6 +153,14 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+#ML
+VeraMind-Mini/
+
+# OS generated files #
+######################
+.DS_Store
+.DS_Store?
+
 # PyCharm
 #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore

src/Ai/interence.py

@@ -0,0 +1,38 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+class VeraMindInference:
+    def __init__(self, model_path, max_len=512):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
+        self.model.to(self.device)
+        self.model.eval()
+        self.max_len = max_len
+
+    def predict(self, text):
+        encoding = self.tokenizer.encode_plus(
+            text,
+            add_special_tokens=True,
+            max_length=self.max_len,
+            return_token_type_ids=False,
+            padding='max_length',
+            truncation=True,
+            return_attention_mask=True,
+            return_tensors='pt',
+        )
+
+        input_ids = encoding['input_ids'].to(self.device)
+        attention_mask = encoding['attention_mask'].to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(input_ids, attention_mask=attention_mask).logits
+            prediction = torch.sigmoid(outputs).cpu().numpy()[0][0]
+
+        is_fake = prediction >= 0.5
+        confidence = prediction if is_fake else 1 - prediction
+
+        return {
+            "result": is_fake,
+            "confidence": float(confidence)
+        }

src/controller/mainFrameController.py

@@ -1,5 +1,6 @@
 from views.mainScreen import MainFrame
 from models.data import TextData
+from Ai.interence import VeraMindInference
 class MainFrameController:
     
     def __init__(self,frame:MainFrame) -> None:
@@ -17,5 +18,17 @@ class MainFrameController:
     def press_check_button(self):
         text_data = self.get_textdata()
         print(f"text:{text_data.text}")
+        self.prediction(text_data)
+        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")
         
-     
+    def prediction(self, text_data:TextData) -> TextData:
+        inference = VeraMindInference('VeraMind-Mini')
+        result = 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

src/models/data.py

@@ -4,6 +4,8 @@ class TextData:
     def __init__(self, url: str = "") -> None:
         self.url = url
         self.text = ""
+        self.isfake_news = False
+        self.confidence = None
         self._extractor = None
 
     def set_url(self, url: str) -> None:
@@ -15,7 +17,7 @@ class TextData:
         if not self.url:
             print("No url")
             return True
-        
+
         if not self.text:
             print("Extrahiere Text von URL...")
             self._extractor = WebTextExtractor(self.url)
@@ -23,6 +25,10 @@ class TextData:
             self._extractor.extract_text()
             self.text = self._extractor.get_text()
             return False
+    
+    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