llm optional gemacht

src/controller/mainFrameController.py

@@ -7,12 +7,7 @@ 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
-BAD_WORDS = ["FAKE", "SATIRE", "Fake", "fake", "fake news", "Fake News", "FakeNews"]
-GOOD_WORDS = ["REAL", "real ", "Real", "Reale News", "reale", "reale News", "realen", "Real News"]
-BAD_COLOR = "#ff8080"
-GOOD_COLOR = "#80ff8f"
-WORDS = BAD_WORDS + GOOD_WORDS
 
 
 class MainFrameController:
@@ -47,63 +42,36 @@ class MainFrameController:
         return text_data
 
     def press_check_button(self):
+        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.frame.output_textbox.configure(state="normal")
         self.frame.output_textbox.delete("0.0", "end")
 
-        response_stream = self.rater.get_response(text_data.text, text_data.result, float(f"{text_data.confidence * 100:.2f}"))
+        confidence = text_data.confidence * 100
+        self.frame.confidence_label.configure(text=f"{confidence:.2f}%")
 
-        highlight_buffer = deque(maxlen=5)
+        result_color = "green" if text_data.result == "REAL" else "red"
+        self.frame.result_label.configure(text=text_data.result, fg_color=result_color)
 
-        for chunk in response_stream:
+        confidence_color = "green" if confidence > 80 else ("orange" if confidence > 50 else "red")
-            # Display the chunk immediately
+        self.frame.confidence_label.configure(fg_color=confidence_color)
-            self.frame.output_textbox.insert("end", chunk)
+        
-            self.frame.output_textbox.see("end")
+        if get_token().strip():
-            self.frame.update_idletasks()
+            response_stream = self.rater.get_response(text_data.text, text_data.result, confidence)
 
-            # Add to highlight buffer
+            for chunk in response_stream:
-            highlight_buffer.append(chunk)
+                self.frame.output_textbox.insert("end", chunk)
+                self.frame.output_textbox.see("end")
+                self.frame.update_idletasks()
 
-            # Process highlighting when buffer is full
-            if len(highlight_buffer) == 5:
-                self._process_highlighting(highlight_buffer)
-
-        # Process any remaining chunks in the buffer
-        if highlight_buffer:
-            self._process_highlighting(highlight_buffer)
-
-        self.frame.output_textbox.configure(state="disabled")
-        self.update_provider_list()
     
-    def _process_highlighting(self, highlight_buffer):
-        start_index = self.frame.output_textbox.index(f"end-{sum(len(c) for c in highlight_buffer)}c")
-        end_index = self.frame.output_textbox.index("end")
-        self._highlight_words(start_index, end_index)
-
-        # Keep overlap of 2 chunks
-        highlight_buffer = deque(list(highlight_buffer)[-3:], maxlen=5)
-
-    def _highlight_words(self, start_index, end_index):
-        content = self.frame.output_textbox.get(start_index, end_index)
-
-        for word in WORDS:
-            start = 0
-            while True:
-                pos = content.find(word, start)
-                if pos == -1:
-                    break
-                word_start = f"{start_index}+{pos}c"
-                word_end = f"{word_start}+{len(word)}c"
-                tag_name = f"{word.lower()}_color"
-                self.frame.output_textbox.tag_add(tag_name, word_start, word_end)
-                if word in BAD_WORDS:
-                    self.frame.output_textbox.tag_config(tag_name, foreground=BAD_COLOR)
-                elif word in GOOD_WORDS:
-                    self.frame.output_textbox.tag_config(tag_name, foreground=GOOD_COLOR)
-                start = pos + len(word)
-
     def _predict(self, text_data: TextData) -> TextData:
         """
         Make a prediction using the VeraMind model.