Update app.py

app.py

@@ -116,21 +116,28 @@ def detect_grammar_errors(text):
 
 def penalize_for_grammar(student_answer):
     grammar_errors = detect_grammar_errors(student_answer)
+    print(f"Grammar errors detected: {grammar_errors}")
     
     # Apply a penalty based on the number of grammar errors (max 50% penalty)
     penalty = max(0.3, 1 - 0.1 * grammar_errors)
+    print(f"Grammar penalty applied: {penalty}")
     return penalty
 
 def compare_answers(student_answer, teacher_answer):
     bert_similarity = calculate_cosine_similarity(get_bert_embedding(student_answer), get_bert_embedding(teacher_answer))
+    print(f"BERT similarity: {bert_similarity}")
+    
     sentence_similarity = calculate_sentence_similarity(student_answer, teacher_answer)
+    print(f"Sentence similarity: {sentence_similarity}")
     
     # Use a higher weight for BERT similarity
-    semantic_similarity = (0.50 * bert_similarity + 0.50 * sentence_similarity)
+    semantic_similarity = (0.80 * bert_similarity + 0.20 * sentence_similarity)
+    print(f"Semantic similarity: {semantic_similarity}")
     
     # Apply grammar penalty
     grammar_penalty = penalize_for_grammar(student_answer)
     final_similarity = semantic_similarity * grammar_penalty
+    print(f"Final similarity (after grammar penalty): {final_similarity}")
     
     return final_similarity
 
@@ -141,10 +148,13 @@ def check_keywords(student_answer, model_answer):
     student_keywords = extract_keywords(student_answer)
     teacher_keywords = extract_keywords(model_answer)
     keyword_overlap = len(student_keywords.intersection(teacher_keywords))
-    return keyword_overlap / (len(teacher_keywords) if len(teacher_keywords) > 0 else 1)
+    keyword_similarity = keyword_overlap / (len(teacher_keywords) if len(teacher_keywords) > 0 else 1)
+    print(f"Keyword similarity: {keyword_similarity}")
+    return keyword_similarity
 
 def evaluate_answer(image, languages, model_answer):
     student_answer = extract_text_from_image(image, languages)
+    print(f"Extracted student answer: {student_answer}")
     
     # Calculate semantic similarity
     semantic_similarity = compare_answers(student_answer, model_answer)
@@ -154,11 +164,15 @@ def evaluate_answer(image, languages, model_answer):
     
     # Adjust the weight of keyword similarity
     combined_similarity = (0.9 * semantic_similarity + 0.1 * keyword_similarity)
+    print(f"Combined similarity score: {combined_similarity}")
+    
     grade = get_grade(combined_similarity)
     feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
     badge = assign_badge(grade)
     detailed_feedback_msg = detailed_feedback(combined_similarity)
+    
     prompt = f"The student got grade: {grade} when the student's answer is: {student_answer} and the teacher's answer is: {model_answer}. Justify the grade given to the student."
+    
     return grade, combined_similarity * 100, feedback, badge, detailed_feedback_msg, prompt
 
 async def gradio_interface(image, languages: List[str], model_answer="The process of photosynthesis helps plants produce glucose using sunlight.", prompt="", history=[]):