Update app.py

app.py

@@ -520,7 +520,6 @@ def calculate_similarity(text1, text2):
     matcher = SequenceMatcher(None, clean1, clean2)
     return matcher.ratio() * 100
 
-# Add this route to your Flask app
 @app.route("/evaluate", methods=["POST"])
 def evaluate_pronunciation():
     if asr_model is None or asr_processor is None:
@@ -603,6 +602,7 @@ def evaluate_pronunciation():
         results = []
         best_score = 0
         best_reference = None
+        best_transcription = None
         
         for ref_file in reference_files:
             try:
@@ -635,6 +635,7 @@ def evaluate_pronunciation():
                 if similarity > best_score:
                     best_score = similarity
                     best_reference = os.path.basename(ref_file)
+                    best_transcription = ref_transcription
                     
                 logger.debug(f"📊 Reference '{os.path.basename(ref_file)}': {similarity:.2f}%")
             except Exception as e:
@@ -647,15 +648,30 @@ def evaluate_pronunciation():
         except Exception as e:
             logger.warning(f"⚠️ Failed to clean up temp files: {str(e)}")
         
-        # Decision on pronunciation correctness (70% threshold)
+        # Enhanced feedback based on score range
         is_correct = best_score >= 70.0
-        feedback = "Great pronunciation!" if is_correct else "Try again! Listen to the sample"
+        feedback = ""
+        
+        if best_score >= 90.0:
+            feedback = "Perfect pronunciation! Excellent job!"
+        elif best_score >= 80.0:
+            feedback = "Great pronunciation! Your accent is very good."
+        elif best_score >= 70.0:
+            feedback = "Good pronunciation. Keep practicing!"
+        elif best_score >= 50.0:
+            feedback = "Fair attempt. Try focusing on the syllables that differ from the sample."
+        else:
+            feedback = "Try again. Listen carefully to the sample pronunciation."
+        
+        # Sort results by score descending
+        results.sort(key=lambda x: x["similarity_score"], reverse=True)
         
         return jsonify({
             "is_correct": is_correct,
             "score": best_score,
             "feedback": feedback,
-            "transcription": user_transcription,
+            "user_transcription": user_transcription,
+            "best_reference_transcription": best_transcription,
             "reference_locator": reference_locator,
             "details": results
         })