working llm response and changed to response in the articel language

src/Ai/llm.py

@@ -10,16 +10,15 @@ class ArticleRater:
             token = f.read().strip()
         return {"Authorization": f"Token {token}"}
 
-    def get_response(self, article, result):
+    def get_response(self, article, result, confidence):
         ollama_params = {
             "base_url": self.client,
             "model": "mistral-nemo:12b-instruct-2407-q8_0",
             "headers": self.headers,
-            "system": "Give a short explanation max 1-3 sentence why this article is rated like that. Begin the RESPONSE ALWAYS with the RESULT and the confidence"
+            "system": "ANTWORTE in der SPRACHE, die auch der ARTIKEL hat. Erkläre in 1-3 Sätzen, warum es sich um solch einen Artikel handeln könnte und woran man es erkennen kann. Beginne die NACHRICHT immer mit dem Resultat under Wahrscheinlichkeit"
         }
 
-        message = (f"A Machine Learning Model labeled an article the following: "
+        message = (f"A Machine Learning Model labeled an article the following, but RESPOND in the LANGUAGE FROM the ARTICLE: Result: {result} Confidence: {confidence}, The Article: {article}")
-                   f"Result: {result['result']}, Confidence: {result['confidence']}, The Article: {article}")
 
         # Initialize the Ollama object with the prepared parameters
         llm = Ollama(**ollama_params)
@@ -44,9 +43,10 @@ Derzeit ist noch unklar, wie es nach dem schweren Cringe-Attentat um den Präsid
 Der Secret Service und das FBI wollen jetzt Untersuchungen einleiten, wie es zu dem für alle Beteiligten höchst unangenehmen Vorfall kommen konnte.
 
 Bizarr: Elon Musk befindet sich trotz seiner Tat nach wie vor auf freiem Fuß und könnte jederzeit wieder auf republikanischen Wahlkampfveranstaltungen für peinliche Momente sorgen."""
-    result = {"result": "FAKE", "confidence": 0.9996}
+    result = "FAKE"
+    confidence = 0.9996
 
     # Capture the stream response
-    response_stream = article_rater.get_response(article, result)
+    response_stream = article_rater.get_response(article, result, confidence=confidence)
     for chunk in response_stream:
         print(chunk, end='', flush=True)

src/controller/mainFrameController.py

@@ -1,6 +1,7 @@
 from views.mainScreen import MainFrame
 from models.data import TextData
 from Ai.interence import VeraMindInference
+from Ai.llm import ArticleRater
 
 
 class MainFrameController:
@@ -8,7 +9,7 @@ class MainFrameController:
     def __init__(self,frame:MainFrame) -> None:
         self.frame = frame
         self.model_inference = VeraMindInference('VeraMind-Mini')
-        
+        self.rater = ArticleRater()
         
     def get_textdata(self) -> TextData:
         text_data = TextData()
@@ -20,12 +21,83 @@ class MainFrameController:
     
     def press_check_button(self):
         text_data = self.get_textdata()
-        print(f"text:{text_data.text}")
+        print(f"text:{text_data}")
         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()}")
+        
+        response_stream = self.rater.get_response(text_data.text, text_data.confidence, text_data.result)
+        display_chunks = []
+        highlight_chunks = []
+        
+        DISPLAY_CHUNK_SIZE = 1  # Display as each chunk arrives
+        HIGHLIGHT_BATCH_SIZE = 5
+        OVERLAP_SIZE = 2
+
+        for chunk in response_stream:
+            display_chunks.append(chunk)
+            highlight_chunks.append(chunk)
+
+            # Display chunk
+            if len(display_chunks) == DISPLAY_CHUNK_SIZE:
+                start_index = self.frame.output_textbox.index("end-1c")
+                self.frame.output_textbox.insert("end", ''.join(display_chunks))
+                self.frame.output_textbox.see("end")  # Scroll to the end
+                self.frame.update_idletasks()  # Update the textbox immediately
+                display_chunks = []
+
+            # Process highlighting
+            if len(highlight_chunks) >= HIGHLIGHT_BATCH_SIZE:
+                start_index = self.frame.output_textbox.index(f"end-{sum(len(c) for c in highlight_chunks)}c")
+                end_index = self.frame.output_textbox.index("end-1c")
+                self.highlight_words(start_index, end_index)
+
+                # Maintain overlap and reset for next batch
+                highlight_chunks = highlight_chunks[-OVERLAP_SIZE:]
+
+        # Highlight remaining chunks if any
+        if highlight_chunks:
+            start_index = self.frame.output_textbox.index(f"end-{sum(len(c) for c in highlight_chunks)}c")
+            end_index = self.frame.output_textbox.index("end-1c")
+            self.highlight_words(start_index, end_index)
+
         self.frame.output_textbox.configure(state="disabled")
+
+
+    def highlight_words(self, start_index, end_index):
+        content = self.frame.output_textbox.get(start_index, end_index)
+        print(content)
+        current_index = start_index
+
+        while True:
+            # Find "FAKE"
+            fake_index = content.find("FAKE")
+            if fake_index != -1:
+                fake_pos_start = f"{current_index}+{fake_index}c"
+                fake_pos_end = f"{fake_pos_start}+4c"
+                self.frame.output_textbox.tag_add("fake_color", fake_pos_start, fake_pos_end)
+                self.frame.output_textbox.tag_config("fake_color", foreground="red")
+                content = content[fake_index + 4:]
+                current_index = fake_pos_end
+            else:
+                break
+
+        current_index = start_index
+        content = self.frame.output_textbox.get(start_index, end_index)
+
+        while True:
+            # Find "REAL"
+            real_index = content.find("REAL")
+            if real_index != -1:
+                real_pos_start = f"{current_index}+{real_index}c"
+                real_pos_end = f"{real_pos_start}+4c"
+                self.frame.output_textbox.tag_add("real_color", real_pos_start, real_pos_end)
+                self.frame.output_textbox.tag_config("real_color", foreground="green")
+                content = content[real_index + 4:]
+                current_index = real_pos_end
+            else:
+                break
+
         
     def prediction(self, text_data:TextData) -> TextData:
         result = self.model_inference.predict(text_data.text)