fxing llm error

main.py

@@ -629,7 +629,7 @@ def compute_marks():
                                 word_score = question_vector_word(extracted_text, clean_correct_answer)
                                 tfidf_score = tfidf_answer_score(extracted_text, clean_correct_answer, max_tfidf)
                                 ft_score = fasttext_similarity(extracted_text, clean_correct_answer)
-                                llm_marks = llm_score(extracted_text, clean_correct_answer)
+                                llm_marks = llm_score(clean_correct_answer, extracted_text)
                                 
                                 # Send individual scores to frontend
                                 notification_queue.put({

similarity_check/llm_based_scoring/llm.py

@@ -18,6 +18,10 @@ except Exception as e:
 
 def llm_score(correct_answers, answer):
     try:
+        # Convert single answer to list if needed
+        if isinstance(correct_answers, str):
+            correct_answers = [correct_answers]
+            
         score = []
 
         for correct_answer in correct_answers:
@@ -73,20 +77,31 @@ def llm_score(correct_answers, answer):
 
                 # Decode and clean response
                 response = models.flan_tokenizer.decode(outputs[0], skip_special_tokens=True)
+                
+                # Extract numeric score
                 try:
-                    # Extract numeric score from response
-                    score_value = float(''.join(filter(str.isdigit, response)))
-                    score_value = max(0, min(10, score_value))  # Clamp between 0 and 10
-                    score.append(score_value)
-                except ValueError:
-                    print(f"Warning: Could not extract numeric score from response: {response}")
-                    score.append(0)
+                    # Try to find a number between 0 and 10
+                    import re
+                    numbers = re.findall(r'\b\d+(?:\.\d+)?\b', response)
+                    for num in numbers:
+                        score_value = float(num)
+                        if 0 <= score_value <= 10:
+                            score.append(score_value)
+                            break
+                    else:
+                        score.append(0.0)
+                except:
+                    score.append(0.0)
                     
             except Exception as e:
-                print(f"Error processing answer: {str(e)}")
-                score.append(0)
+                print(f"Error processing individual answer: {str(e)}")
+                score.append(0.0)
+                
+        # Return average score if multiple correct answers, otherwise single score
+        if score:
+            return sum(score) / len(score)
+        return 0.0
         
-        return score
     except Exception as e:
         print(f"Error in llm_score: {str(e)}")
-        return [0] * len(correct_answers)
+        return 0.0