fix answer print

main.py

@@ -458,11 +458,27 @@ def compute_marks():
                         "message": "Starting text extraction..."
                     })
                     extracted_text = extract_text_from_image(filepath)
-                    log_print("Text extraction completed")
+                    
+                    # Log the extracted text
+                    log_print(f"Extracted text from {filename}:")
+                    log_print(extracted_text)
+                    
+                    # Send extracted text to frontend
                     notification_queue.put({
-                        "type": "success",
-                        "message": "Text extraction completed"
+                        "type": "info",
+                        "message": f"Extracted text from {filename}:",
+                        "data": {
+                            "extracted_text": extracted_text
+                        }
+                    })
+                    
+                    # For debugging, just return the extracted text without scoring
+                    results.append({
+                        "filename": filename,
+                        "extracted_text": extracted_text,
+                        "scores": None
                     })
+                    
                 except Exception as e:
                     error_msg = str(e).encode('ascii', 'ignore').decode('ascii')
                     log_print(f"Text extraction error: {error_msg}", "ERROR")
@@ -477,86 +493,6 @@ def compute_marks():
                     })
                     continue
                 
-                # Clean the extracted text for JSON
-                extracted_text = extracted_text.encode('ascii', 'ignore').decode('ascii')
-                
-                # Find best matching answer
-                best_score = 0
-                best_answer_index = 0
-                
-                for i, correct_answer in enumerate(correct_answers):
-                    try:
-                        # Clean the correct answer for comparison
-                        clean_correct_answer = correct_answer.encode('ascii', 'ignore').decode('ascii')
-                        
-                        # Calculate similarity scores
-                        log_print("Starting similarity score calculations...")
-                        notification_queue.put({
-                            "type": "info",
-                            "message": "Starting similarity score calculations..."
-                        })
-                        
-                        log_print(f"Calculating scores for answer {i+1}...")
-                        notification_queue.put({
-                            "type": "info",
-                            "message": f"Calculating scores for answer {i+1}..."
-                        })
-                        
-                        semantic_score = question_vector_sentence(extracted_text, clean_correct_answer)
-                        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(clean_correct_answer, extracted_text)
-                        
-                        # Calculate final score
-                        final_score = (
-                            semantic_score * 0.3 +
-                            word_score * 0.2 +
-                            tfidf_score * 0.2 +
-                            ft_score * 0.15 +
-                            llm_marks * 0.15
-                        )
-                        
-                        if final_score > best_score:
-                            best_score = final_score
-                            best_answer_index = i
-                            
-                    except Exception as e:
-                        error_msg = str(e).encode('ascii', 'ignore').decode('ascii')
-                        log_print(f"Error calculating scores: {error_msg}", "ERROR")
-                        notification_queue.put({
-                            "type": "error",
-                            "message": f"Error calculating scores: {error_msg}"
-                        })
-                        continue
-                
-                # Send individual scores to frontend
-                notification_queue.put({
-                    "type": "info",
-                    "message": f"Individual scores for {filename}:",
-                    "data": {
-                        "semantic": semantic_score,
-                        "word": word_score,
-                        "tfidf": tfidf_score,
-                        "fasttext": ft_score,
-                        "llm": llm_marks
-                    }
-                })
-                
-                # Add result
-                results.append({
-                    "filename": filename,
-                    "scores": {
-                        "semantic": semantic_score,
-                        "word": word_score,
-                        "tfidf": tfidf_score,
-                        "fasttext": ft_score,
-                        "llm": llm_marks,
-                        "final": best_score
-                    },
-                    "best_match_index": best_answer_index
-                })
-                
                 # Clean up temporary file
                 try:
                     os.remove(filepath)