Update app.py

app.py

@@ -128,15 +128,15 @@ def evaluate_answer(image, languages, 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."
-    return grade, similarity_score * 100, feedback, visual_feedback, badge, detailed_feedback_msg, prompt
+    return grade, similarity_score * 100, feedback, badge, detailed_feedback_msg, prompt
 
 # Main interface function for Gradio
 async def gradio_interface(image, languages: List[str], model_answer="The process of photosynthesis helps plants produce glucose using sunlight.", prompt="", history=[]):
-    grade, similarity_score, feedback, visual_feedback, badge, detailed_feedback_msg, prompt = evaluate_answer(image, languages, model_answer)
+    grade, similarity_score, feedback, badge, detailed_feedback_msg, prompt = evaluate_answer(image, languages, model_answer)
     response = ""
     async for result in chat_groq(prompt, history):
         response = result  # Get the Groq response
-    return grade, similarity_score, feedback, visual_feedback, badge, detailed_feedback_msg, response
+    return grade, similarity_score, feedback, badge, detailed_feedback_msg, response
 
 # Get available Tesseract languages
 language_choices = pytesseract.get_languages()
@@ -154,7 +154,7 @@ interface = gr.Interface(
         gr.Text(label="Grade"), 
         gr.Number(label="Similarity Score (%)"), 
         gr.Text(label="Feedback"), 
-        gr.HTML(label="Visual Feedback"), 
+        # gr.HTML(label="Visual Feedback"), 
         gr.Text(label="Badge"), 
         gr.JSON(label="Detailed Feedback"), 
         gr.Text(label="Generated Response")