Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-import difflib
 from groq import Groq
 import gradio as gr 
 from transformers import pipeline
@@ -57,7 +56,7 @@ def extract_text_from_image(filepath: str, languages: List[str]):
 def get_embedding(text):
     return model1.encode(text, convert_to_tensor=True)
 
-# Calculate similarity between two texts using cosine similarity
+# Calculate similarity between two texts focusing on sequence
 def calculate_similarity(text1, text2):
     embedding1 = get_embedding(text1)
     embedding2 = get_embedding(text2)
@@ -75,13 +74,21 @@ def assign_badge(grade):
     else:
         return "Keep Improving Badge 💪"
 
-# Generate visual feedback by comparing answers
-def generate_visual_feedback(student_answer, model_answer):
-    diff = difflib.ndiff(student_answer.split(), model_answer.split())
-    highlighted_diff = ' '.join(
-        [f"**{word}**" if word.startswith('-') else word for word in diff if not word.startswith('?')]
-    )
-    return highlighted_diff
+# Generate sequence-based feedback
+def generate_sequence_feedback(student_answer, model_answer):
+    student_words = student_answer.split()
+    model_words = model_answer.split()
+    common_sequences = []
+    
+    for i in range(len(student_words)):
+        sequence = ' '.join(student_words[i:i+3])  # Compare sequences of 3 words at a time
+        if sequence in model_answer:
+            common_sequences.append(f"**{sequence}**")
+        else:
+            common_sequences.append(sequence)
+    
+    highlighted_feedback = ' '.join(common_sequences)
+    return highlighted_feedback
 
 # Categorize feedback into clarity, completeness, and accuracy
 def detailed_feedback(similarity_score):
@@ -113,7 +120,7 @@ def evaluate_answer(image, languages, model_answer):
     similarity_score = calculate_similarity(student_answer, model_answer)
     grade = get_grade(similarity_score)
     feedback = f"Student's answer: {student_answer}\nTeacher's answer: {model_answer}"
-    visual_feedback = generate_visual_feedback(student_answer, model_answer)
+    visual_feedback = generate_sequence_feedback(student_answer, model_answer)
     badge = assign_badge(grade)
     detailed_feedback_msg = detailed_feedback(similarity_score)
     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."