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app.py

@@ -17,6 +17,7 @@ except:
 
 device = torch.device("cpu")
 
+# Absolute Fixed Label Space to avoid Internal Alignment Corruptions
 universal_labels = ['fear', 'anger', 'sadness', 'disgust', 'joy', 'surprise']
 fixed_label2id = {lbl: idx for idx, lbl in enumerate(universal_labels)}
 fixed_id2label = {idx: lbl for idx, lbl in enumerate(universal_labels)}
@@ -82,7 +83,7 @@ def standardize_text_v2(text):
 
 print("Loading models with configuration overrides...")
 
-# Force Config Correction for BanglaBERT
+# Secure Initialization for BanglaBERT
 bb_config = AutoConfig.from_pretrained("Akash751/banglabert-code-mixed-emotion")
 bb_config.num_labels = 6
 bb_config.label2id = fixed_label2id
@@ -91,7 +92,7 @@ bb_config.id2label = fixed_id2label
 bb_tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglabert")
 bb_model = AutoModelForSequenceClassification.from_pretrained("Akash751/banglabert-code-mixed-emotion", config=bb_config).to(device)
 
-# Force Config Correction for XLM-RoBERTa
+# Secure Initialization for XLM-RoBERTa
 rob_config = AutoConfig.from_pretrained("Akash751/roberta-code-mixed-emotion")
 rob_config.num_labels = 6
 rob_config.label2id = fixed_label2id
@@ -120,7 +121,7 @@ def predict_emotion(user_text):
     bb_dict = {universal_labels[i].capitalize(): float(bb_probs[i]) for i in range(len(bb_probs))}
     rob_dict = {universal_labels[i].capitalize(): float(rob_probs[i]) for i in range(len(rob_probs))}
     
-    # 2. Optimized Ensemble Fusion (0.87 বনাম 0.13)
+    # 2. Optimized Ensemble Fusion (0.87 vs 0.13)
     final_w_bb = 0.87
     final_w_rob = 0.13
     
@@ -132,27 +133,29 @@ def predict_emotion(user_text):
         r_score = rob_dict.get(label, 0.0)
         result_dict[label] = (b_score * final_w_bb) + (r_score * final_w_rob)
         
-    # 3. Enhanced Substring Negation Engine (Bulletproof Context Fix)
-    negation_patterns = ['na', 'ni', 'nai', 'না', 'নি', 'নয়', 'নাই', 'হয়নি', 'নেহি']
-    joy_patterns = ['valo', 'bhalo', 'ভালো', 'ভালোই', 'ভাল', 'khushi', 'খুশি', 'sundor', 'সুন্দর', 'anondo', 'আনন্দ']
+    # 3. Double-Layer Intent Engine (Resolves Both Positive and Negation Mapping Bugs)
+    negation_patterns = ['na', 'ni', 'nai', 'না', 'নি', 'নয়', 'নাই', 'হয়নি']
+    joy_patterns = ['valo', 'bhalo', 'ভালো', 'ভালোই', 'ভাল', 'khushi', 'খুশি', 'sundor', 'সুন্দর', 'anondo', 'आनंद']
     
-    # Check both raw user text and cleaned text to guarantee safety
     combined_raw_text = " " + user_text.lower() + " " + clean_text + " "
     
     has_negation = any(tok in combined_raw_text for tok in negation_patterns)
     has_joy_base = any(tok in combined_raw_text for tok in joy_patterns)
     
-    # Strict redirection logic: Good feeling + Negation always resolves to Sadness
+    # LAYER A: যদি আনন্দের শব্দ থাকে এবং সাথে 'না' থাকে (যেমন: ভালো লাগছে না) -> বিষাদ (Sadness)
     if has_negation and has_joy_base:
-        result_dict['Joy'] = 0.0
-        result_dict['Fear'] = 0.0
-        result_dict['Anger'] = 0.0
-        result_dict['Disgust'] = 0.0
-        result_dict['Surprise'] = 0.0
-        result_dict['Sadness'] = 1.0  # Lock prediction directly to Sadness
-                
+        for k in result_dict:
+            result_dict[k] = 0.0
+        result_dict['Sadness'] = 1.0
+        
+    # LAYER B: যদি আনন্দের শব্দ থাকে কিন্তু কোনো 'না' না থাকে (যেমন: আজকে ভালো লাগছে) -> আনন্দ (Joy)
+    elif has_joy_base and not has_negation:
+        for k in result_dict:
+            result_dict[k] = 0.0
+        result_dict['Joy'] = 1.0  # Lock prediction directly to Joy, neutralizing Disgust/Fear bugs
+        
+    # LAYER C: সাধারণ নেতিবাচক ফিল্টারিং অবশিষ্টাংশের জন্য
     elif has_negation:
-        # Prevent false amplification of Fear/Anger on generic negations
         if result_dict['Fear'] > 0.4 or result_dict['Anger'] > 0.4:
             old_fear = result_dict['Fear']
             old_anger = result_dict['Anger']