Update app.py

app.py

@@ -1,66 +1,34 @@
 import gradio as gr
 from transformers import pipeline
-import re
-from difflib import SequenceMatcher
+import re  
 
 text_pipe = pipeline("text-classification", model="karalif/myTestModel", return_all_scores=True)
 
 def predict(text):
     greeting_pattern = r"^(Halló|Hæ|Sæl|Góða|Kær|Daginn|Kvöldið|Ágæt|Elsku)"
+    
     greeting_feedback = ""
     
     results = text_pipe(text)
     all_scores = results[0]
     response = ""
-
-    # Helper function to mark text with a specific tag
-    def mark_text(text, tag):
-        return (text, tag)
-
-    # Helper function to mark spans of text with a specific tag
-    def mark_span(text, tag):
-        return [mark_text(token, tag) for token in text]
-
-    # Helper function to markup differences between two texts
-    def markup_diff(a, b, mark=mark_span, default_mark=lambda x: x, isjunk=None):
-        """Returns a and b with any differences processed by mark
-        Junk is ignored by the differ
-        """
-        seqmatcher = SequenceMatcher(isjunk=isjunk, a=a, b=b, autojunk=False)
-        out_a, out_b = [], []
-        for tag, a0, a1, b0, b1 in seqmatcher.get_opcodes():
-            markup = mark
-            out_a += markup(a[a0:a1], tag)
-            out_b += markup(b[b0:b1], tag)
-        return out_a, out_b
-
-    # Generating scores and markup
     for result in all_scores:
         label = result['label']
         score = result['score']
         response += f"{label}: {score:.3f}\n"
-
-    # Apply markup to class names only
-    original_text = text.split()
-    processed_text = response.split()
-    markup_response, _ = markup_diff(original_text, processed_text, mark=lambda x, tag: mark_text(x, tag) if x.lower() in ['politeness', 'toxicity', 'sentiment', 'formality'] else x)
-
-    # Convert marked text back to string
-    marked_response = " ".join([token[0] for token in markup_response])
-
-    # Check if the input text matches the greeting pattern
+    
     if not re.match(greeting_pattern, text, re.IGNORECASE):
         greeting_feedback = "\n- Heilsaðu dóninn þinn\n"
 
-    # Add greeting feedback and return the marked response
-    marked_response += greeting_feedback
-    return marked_response
+    response += greeting_feedback
+    
+    return response
 
 description_html = """
     <center>
         <img src='http://www.ru.is/media/HR_logo_vinstri_transparent.png' width='250' height='auto'>
     </center>
-"""
+    """
 
 gr.Interface(
     fn=predict,
@@ -74,4 +42,4 @@ gr.Interface(
         ["Hver á þenan bússtað? já eða nei."],
         ["Hafi þau svo látið gólfið þorna vel og síðan flotað það til lagfæringar eftir motturnar."],
     ],
-).launch()
+).launch()