Update app.py

app.py

@@ -111,18 +111,17 @@ def calculate_sentence_similarity(text1, text2):
 
 # Function to compare logic of student and teacher answers
 def compare_answers(student_answer, teacher_answer):
-    # Use primarily BERT similarity, and sentence embeddings as a secondary method
     bert_similarity = calculate_cosine_similarity(get_bert_embedding(student_answer), get_bert_embedding(teacher_answer))
     
-    # Adjust weight: give more emphasis to BERT
+    # Emphasize the importance of BERT similarity
     sentence_similarity = calculate_sentence_similarity(student_answer, teacher_answer)
     
-    final_similarity = (0.7 * bert_similarity + 0.3 * sentence_similarity)
+    # Use a higher weight for BERT similarity
+    final_similarity = (0.8 * bert_similarity + 0.2 * sentence_similarity)
     return final_similarity
 
 # Function to extract keywords from the model answer
 def extract_keywords(text):
-    # Split text into words and count them
     return set(text.lower().split())
 
 # Adjust grading based on key terms present in student answer
@@ -142,8 +141,8 @@ def evaluate_answer(image, languages, model_answer):
     # Include keyword similarity
     keyword_similarity = check_keywords(student_answer, model_answer)
     
-    # Combine semantic similarity with keyword presence check
-    combined_similarity = (semantic_similarity + keyword_similarity) / 2
+    # Combine semantic similarity with keyword presence check, emphasizing semantic similarity
+    combined_similarity = (semantic_similarity + (0.5 * keyword_similarity)) / 1.5
     grade = get_grade(combined_similarity)
     feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
     badge = assign_badge(grade)