UI updated

config/model_config.py

@@ -20,6 +20,8 @@ class ModelType(Enum):
     EMBEDDING               = "embedding"
     RULE_BASED              = "rule_based"
     SEQUENCE_CLASSIFICATION = "sequence_classification"  
+    CAUSAL_LM               = "causal_lm"        
+    MASKED_LM               = "masked_lm" 
 
 
 @dataclass
@@ -99,7 +101,7 @@ MODEL_REGISTRY : Dict[str, ModelConfig] = {"perplexity_gpt2"            : ModelC
                                                                                       quantizable       = True,
                                                                                      ),
                                            "multi_perturbation_base"    : ModelConfig(model_id          = "gpt2",
-                                                                                      model_type        = ModelType.GPTMASK,
+                                                                                      model_type        = ModelType.CAUSAL_LM,
                                                                                       description       = "MultiPerturbationStability model (reuses gpt2)",
                                                                                       size_mb           = 0,  
                                                                                       required          = True,  
@@ -108,7 +110,7 @@ MODEL_REGISTRY : Dict[str, ModelConfig] = {"perplexity_gpt2"            : ModelC
                                                                                       batch_size        = 4,
                                                                                      ),
                                            "multi_perturbation_mask"    : ModelConfig(model_id          = "distilroberta-base",
-                                                                                      model_type        = ModelType.TRANSFORMER,
+                                                                                      model_type        = ModelType.MASKED_LM,
                                                                                       description       = "Masked LM for text perturbation",
                                                                                       size_mb           = 330,
                                                                                       required          = True, 

data/reports/analysis_1761903397224_20251031_150713.pdf

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detector/attribution.py

@@ -77,7 +77,7 @@ class ModelAttributor:
     - Confidence-weighted aggregation
     - Explainable reasoning
     """
-    # DOCUMENT-ALIGNED: Metric weights from technical specification
+    # Metric weights from technical specification
     METRIC_WEIGHTS           = {"perplexity"                   : 0.25,  
                                 "structural"                   : 0.15,   
                                 "semantic_analysis"            : 0.15,  
@@ -86,7 +86,7 @@ class ModelAttributor:
                                 "multi_perturbation_stability" : 0.10,  
                                }
     
-    # DOMAIN-AWARE model patterns for ALL 16 DOMAINS
+    # Domain-aware model patterns for ALL 16 DOMAINS
     DOMAIN_MODEL_PREFERENCES = {Domain.GENERAL       : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_3_5],
                                 Domain.ACADEMIC      : [AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.GEMINI_ULTRA, AIModel.GPT_4_TURBO],
                                 Domain.TECHNICAL_DOC : [AIModel.GPT_4_TURBO, AIModel.CLAUDE_3_SONNET, AIModel.LLAMA_3, AIModel.GPT_4],
@@ -105,7 +105,7 @@ class ModelAttributor:
                                 Domain.TUTORIAL      : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_4_TURBO],
                                }
 
-    # Enhanced Model-specific fingerprints with comprehensive patterns
+    # Model-specific fingerprints with comprehensive patterns
     MODEL_FINGERPRINTS = {AIModel.GPT_3_5       : {"phrases"              : ["as an ai language model",
                                                                              "i don't have personal opinions",
                                                                              "it's important to note that",
@@ -460,13 +460,13 @@ class ModelAttributor:
                                                                                      domain             = domain,
                                                                                     )
             
-            # Domain-aware prediction - FIXED: Always show the actual highest probability model
+            # Domain-aware prediction : Always show the actual highest probability model
             predicted_model, confidence           = self._make_domain_aware_prediction(combined_scores    = combined_scores,
                                                                                        domain             = domain,
                                                                                        domain_preferences = domain_preferences,
                                                                                       )
             
-            # Reasoning with domain context - FIXED
+            # Reasoning with domain context
             reasoning                             = self._generate_detailed_reasoning(predicted_model      = predicted_model,
                                                                                       confidence           = confidence,
                                                                                       domain               = domain,
@@ -490,7 +490,7 @@ class ModelAttributor:
 
     def _calculate_fingerprint_scores(self, text: str, domain: Domain) -> Dict[AIModel, float]:
         """
-        Calculate fingerprint match scores with DOMAIN CALIBRATION - FIXED for all domains
+        Calculate fingerprint match scores with domain calibration - for all domains
         """
         scores             = {model: 0.0 for model in AIModel if model not in [AIModel.HUMAN, AIModel.UNKNOWN]}
         
@@ -812,7 +812,7 @@ class ModelAttributor:
 
     def _make_domain_aware_prediction(self, combined_scores: Dict[str, float], domain: Domain, domain_preferences: List[AIModel]) -> Tuple[AIModel, float]:
         """
-        Domain aware prediction that considers domain-specific model preferences - FIXED
+        Domain aware prediction that considers domain-specific model preferences
         """
         if not combined_scores:
             return AIModel.UNKNOWN, 0.0
@@ -825,109 +825,103 @@ class ModelAttributor:
         
         best_model_name, best_score = sorted_models[0]
         
-        # FIXED: Only return UNKNOWN if the best score is very low
-        # Use a more reasonable threshold for attribution
-        if best_score < 0.05:  # Changed from 0.08 to 0.05 to be less restrictive
+        # Thresholding to show model only if confidence is sufficient
+        if (best_score < 0.01): 
             return AIModel.UNKNOWN, best_score
         
-        # FIXED: Don't override with domain preferences if there's a clear winner
-        # Only consider domain preferences if scores are very close
-        if len(sorted_models) > 1:
-            second_model_name, second_score = sorted_models[1]
-            score_difference = best_score - second_score
-            
-            # If scores are very close (within 3%) and second is domain-preferred, consider it
-            if score_difference < 0.03:
-                try:
-                    best_model = AIModel(best_model_name)
-                    second_model = AIModel(second_model_name)
-                    
-                    # If second model is domain-preferred and first is not, prefer second
-                    if (second_model in domain_preferences and 
-                        best_model not in domain_preferences):
-                        best_model_name = second_model_name
-                        best_score = second_score
-                except ValueError:
-                    pass
-        
         try:
             best_model = AIModel(best_model_name)
+
         except ValueError:
             best_model = AIModel.UNKNOWN
         
-        # Calculate confidence based on score dominance
-        if len(sorted_models) > 1:
+        # Calculate confidence - be more generous
+        if (len(sorted_models) > 1):
             second_score = sorted_models[1][1]
-            margin = best_score - second_score
-            # Confidence based on both absolute score and margin
-            confidence = min(1.0, best_score * 0.6 + margin * 2.0)
+            margin       = best_score - second_score
+            # More generous confidence calculation
+            confidence   = min(1.0, best_score * 0.8 + margin * 1.5)
+
         else:
-            confidence = best_score * 0.7
+            confidence = best_score * 0.9
         
-        # FIXED: Don't downgrade to UNKNOWN based on confidence alone
-        # If we have a model with reasonable probability, show it even with low confidence
-        return best_model, confidence
+        # Always return the actual best model, never downgrade to UNKNOWN
+        return best_model, max(0.05, confidence)  
 
 
     def _generate_detailed_reasoning(self, predicted_model: AIModel, confidence: float, domain: Domain, metric_contributions: Dict[str, float], 
                                      combined_scores: Dict[str, float]) -> List[str]:
         """
-        Generate Explainable reasoning - FIXED to show proper formatting
+        Generate Explainable reasoning - ENHANCED version
         """
         reasoning = []
         
         reasoning.append("**AI Model Attribution Analysis**")
         reasoning.append("")
-        reasoning.append(f"**Domain**: {domain.value.replace('_', ' ').title()}")
-        reasoning.append("")
         
         # Show prediction with confidence
         if (predicted_model == AIModel.UNKNOWN):
             reasoning.append("**Most Likely**: Unable to determine with high confidence")
-            reasoning.append("")
-            reasoning.append("**Top Candidates:**")
-        
+
         else:
             model_name = predicted_model.value.replace("-", " ").replace("_", " ").title()
             reasoning.append(f"**Predicted Model**: {model_name}")
             reasoning.append(f"**Confidence**: {confidence*100:.1f}%")
-            reasoning.append("")
-            reasoning.append("**Model Probability Distribution:**")
         
+        reasoning.append(f"**Domain**: {domain.value.replace('_', ' ').title()}")
+        reasoning.append("")
+        
+        # Show model probability distribution
+        reasoning.append("**Model Probability Distribution:**")
         reasoning.append("")
         
-        # Show top candidates in proper format
         if combined_scores:
             sorted_models = sorted(combined_scores.items(), key = lambda x: x[1], reverse = True)
             
             for i, (model_name, score) in enumerate(sorted_models[:6]):
-                # Skip very low probability models
+                # Skip very low probabilities
                 if (score < 0.01):  
                     continue
-                
+                    
                 display_name = model_name.replace("-", " ").replace("_", " ").title()
                 percentage   = score * 100
                 
-                # Single line format: "• Model Name: XX.X%"
+                # Use proper markdown formatting
                 reasoning.append(f"• **{display_name}**: {percentage:.1f}%")
         
         reasoning.append("")
         
-        # Domain-specific insights - FIXED: Removed duplicate header
+        # Add analysis insights
         reasoning.append("**Analysis Notes:**")
-        reasoning.append(f"• Calibrated for {domain.value.replace('_', ' ')} domain")
-        
-        if (domain in [Domain.ACADEMIC, Domain.TECHNICAL_DOC, Domain.AI_ML, Domain.SOFTWARE_DEV, Domain.ENGINEERING, Domain.SCIENCE]):
-            reasoning.append("• Higher weight on structural coherence and technical patterns")
         
-        elif (domain in [Domain.CREATIVE, Domain.MARKETING, Domain.SOCIAL_MEDIA, Domain.BLOG_PERSONAL]):
-            reasoning.append("• Emphasis on linguistic diversity and stylistic variation")
-        
-        elif (domain in [Domain.LEGAL, Domain.MEDICAL]):
-            reasoning.append("• Focus on formal language and specialized terminology")
-        
-        elif (domain in [Domain.BUSINESS, Domain.JOURNALISM, Domain.TUTORIAL]):
-            reasoning.append("• Balanced analysis across multiple attribution factors")
+        if (confidence < 0.3):
+            reasoning.append("• Low confidence attribution - text patterns are ambiguous")
+            reasoning.append("• May be human-written or from multiple AI sources")
+
+        else:
+            reasoning.append(f"• Calibrated for {domain.value.replace('_', ' ')} domain")
+            
+            # Domain-specific insights
+            domain_insights = {Domain.ACADEMIC      : "Academic writing patterns analyzed",
+                               Domain.TECHNICAL_DOC : "Technical coherence and structure weighted",
+                               Domain.CREATIVE      : "Stylistic and linguistic diversity emphasized",
+                               Domain.SOCIAL_MEDIA  : "Casual language and engagement patterns considered",
+                               Domain.AI_ML         : "Technical terminology and analytical patterns emphasized",
+                               Domain.SOFTWARE_DEV  : "Code-like structures and technical precision weighted",
+                               Domain.ENGINEERING   : "Technical specifications and formal language analyzed",
+                               Domain.SCIENCE       : "Scientific terminology and methodological patterns considered",
+                               Domain.BUSINESS      : "Professional communication and strategic language weighted",
+                               Domain.LEGAL         : "Formal language and legal terminology emphasized",
+                               Domain.MEDICAL       : "Medical terminology and clinical language analyzed",
+                               Domain.JOURNALISM    : "News reporting style and factual presentation weighted",
+                               Domain.MARKETING     : "Persuasive language and engagement patterns considered",
+                               Domain.BLOG_PERSONAL : "Personal voice and conversational style analyzed",
+                               Domain.TUTORIAL      : "Instructional clarity and step-by-step structure weighted",
+                              }
+            
+            insight         = domain_insights.get(domain, "Multiple attribution factors analyzed")
+
+            reasoning.append(f"• {insight}")
         
         return reasoning
 

detector/highlighter.py

@@ -48,14 +48,14 @@ class TextHighlighter:
     - Explainable tooltips
     - Highlighting metrics calculation
     """
-    # Color thresholds with mixed content support
+    # Color thresholds with mixed content support - FIXED: No gaps
     COLOR_THRESHOLDS = [(0.00, 0.10, "very-high-human", "#dcfce7", "Very likely human-written"),
                         (0.10, 0.25, "high-human", "#bbf7d0", "Likely human-written"),
                         (0.25, 0.40, "medium-human", "#86efac", "Possibly human-written"),
                         (0.40, 0.60, "uncertain", "#fef9c3", "Uncertain"),
                         (0.60, 0.75, "medium-ai", "#fde68a", "Possibly AI-generated"),
                         (0.75, 0.90, "high-ai", "#fed7aa", "Likely AI-generated"),
-                        (0.90, 1.01, "very-high-ai", "#fecaca", "Very likely AI-generated"),
+                        (0.90, 1.00, "very-high-ai", "#fecaca", "Very likely AI-generated"),
                        ]
     
     # Mixed content pattern
@@ -86,11 +86,23 @@ class TextHighlighter:
         self.text_processor     = TextProcessor()
         self.domain             = domain
         self.domain_thresholds  = get_threshold_for_domain(domain)
-        self.ensemble           = ensemble_classifier or EnsembleClassifier(primary_method  = "confidence_calibrated",
-                                                                            fallback_method = "domain_weighted",
-                                                                           )
+        self.ensemble           = ensemble_classifier or self._create_default_ensemble()
     
 
+    def _create_default_ensemble(self) -> EnsembleClassifier:
+        """
+        Create default ensemble classifier with proper error handling
+        """
+        try:
+            return EnsembleClassifier(primary_method  = "confidence_calibrated",
+                                      fallback_method = "domain_weighted",
+                                     )
+        except Exception as e:
+            logger.warning(f"Failed to create default ensemble: {e}. Using fallback mode.")
+            # Return a minimal ensemble or raise based on requirements
+            return EnsembleClassifier(primary_method = "weighted_average")
+
+
     def generate_highlights(self, text: str, metric_results: Dict[str, MetricResult], ensemble_result: Optional[EnsembleResult] = None,
                             enabled_metrics: Optional[Dict[str, bool]] = None, use_sentence_level: bool = True) -> List[HighlightedSentence]:
         """
@@ -112,80 +124,197 @@ class TextHighlighter:
         --------
                          { list }                 : List of HighlightedSentence objects
         """
-        # Get domain-appropriate weights for enabled metrics
-        if enabled_metrics is None:
-            enabled_metrics = {name: True for name in metric_results.keys()}
-        
-        weights   = get_active_metric_weights(self.domain, enabled_metrics)
-        
-        # Split text into sentences
-        sentences = self._split_sentences(text)
+        try:
+            # Validate inputs
+            if not text or not text.strip():
+                return self._handle_empty_text(text, metric_results, ensemble_result)
+            
+            # Get domain-appropriate weights for enabled metrics
+            if enabled_metrics is None:
+                enabled_metrics = {name: True for name in metric_results.keys()}
+            
+            weights   = get_active_metric_weights(self.domain, enabled_metrics)
+            
+            # Split text into sentences with error handling
+            sentences = self._split_sentences_with_fallback(text)
+            
+            if not sentences:
+                return self._handle_no_sentences(text, metric_results, ensemble_result)
+            
+            # Calculate probabilities for each sentence using ENSEMBLE METHODS
+            highlighted_sentences = list()
+            
+            for idx, sentence in enumerate(sentences):
+                try:
+                    if use_sentence_level:
+                        # Use ENSEMBLE for sentence-level analysis
+                        ai_prob, human_prob, mixed_prob, confidence, breakdown = self._calculate_sentence_ensemble_probability(sentence        = sentence, 
+                                                                                                                               metric_results  = metric_results,
+                                                                                                                               weights         = weights,
+                                                                                                                               ensemble_result = ensemble_result,
+                                                                                                                              )
+                    else:
+                        # Use document-level ensemble probabilities
+                        ai_prob, human_prob, mixed_prob, confidence, breakdown = self._get_document_ensemble_probability(ensemble_result = ensemble_result,
+                                                                                                                         metric_results  = metric_results,
+                                                                                                                         weights         = weights,
+                                                                                                                        )
+                    
+                    # Apply domain-specific adjustments with limits
+                    ai_prob                              = self._apply_domain_specific_adjustments(sentence        = sentence, 
+                                                                                                   ai_prob         = ai_prob, 
+                                                                                                   sentence_length = len(sentence.split()),
+                                                                                                  )
+                    
+                    # Determine if this is mixed content
+                    is_mixed_content                     = (mixed_prob > self.MIXED_THRESHOLD)
+                    
+                    # Get confidence level
+                    confidence_level                     = get_confidence_level(confidence)
+                    
+                    # Get color class (consider mixed content)
+                    color_class, color_hex, tooltip_base = self._get_color_for_probability(probability      = ai_prob,
+                                                                                           is_mixed_content = is_mixed_content,
+                                                                                           mixed_prob       = mixed_prob,
+                                                                                          )
+                    
+                    # Generate enhanced tooltip
+                    tooltip                              = self._generate_ensemble_tooltip(sentence         = sentence, 
+                                                                                           ai_prob          = ai_prob,
+                                                                                           human_prob       = human_prob,
+                                                                                           mixed_prob       = mixed_prob,
+                                                                                           confidence       = confidence, 
+                                                                                           confidence_level = confidence_level, 
+                                                                                           tooltip_base     = tooltip_base, 
+                                                                                           breakdown        = breakdown,
+                                                                                           is_mixed_content = is_mixed_content,
+                                                                                          )
+                    
+                    highlighted_sentences.append(HighlightedSentence(text              = sentence,
+                                                                     ai_probability    = ai_prob,
+                                                                     human_probability = human_prob,
+                                                                     mixed_probability = mixed_prob,
+                                                                     confidence        = confidence,
+                                                                     confidence_level  = confidence_level,
+                                                                     color_class       = color_class,
+                                                                     tooltip           = tooltip,
+                                                                     index             = idx,
+                                                                     is_mixed_content  = is_mixed_content,
+                                                                     metric_breakdown  = breakdown,
+                                                                    )
+                                                )
+                
+                except Exception as e:
+                    logger.warning(f"Failed to process sentence {idx}: {e}")
+                    # Add fallback sentence
+                    highlighted_sentences.append(self._create_fallback_sentence(sentence, idx))
+            
+            return highlighted_sentences
         
-        if not sentences:
-            return []
+        except Exception as e:
+            logger.error(f"Highlight generation failed: {e}")
+            return self._create_error_fallback(text, metric_results)
+
+
+    def _handle_empty_text(self, text: str, metric_results: Dict[str, MetricResult], ensemble_result: Optional[EnsembleResult]) -> List[HighlightedSentence]:
+        """
+        Handle empty input text
+        """
+        if ensemble_result:
+            return [self._create_fallback_sentence(text       = "No text content", 
+                                                   index      = 0, 
+                                                   ai_prob    = ensemble_result.ai_probability, 
+                                                   human_prob = ensemble_result.human_probability,
+                                                  )
+                   ]
+
+        return [self._create_fallback_sentence("No text content", 0)]
+
+
+    def _handle_no_sentences(self, text: str, metric_results: Dict[str, MetricResult], ensemble_result: Optional[EnsembleResult]) -> List[HighlightedSentence]:
+        """
+        Handle case where no sentences could be extracted
+        """
+        if (text and (len(text.strip()) > 0)):
+            # Treat entire text as one sentence
+            return [self._create_fallback_sentence(text.strip(), 0)]
+
+        return [self._create_fallback_sentence("No processable content", 0)]
+
+
+    def _create_fallback_sentence(self, text: str, index: int, ai_prob: float = 0.5, human_prob: float = 0.5) -> HighlightedSentence:
+        """
+        Create a fallback sentence when processing fails
+        """
+        confidence_level             = get_confidence_level(0.3)
+        color_class, _, tooltip_base = self._get_color_for_probability(probability      = ai_prob, 
+                                                                       is_mixed_content = False, 
+                                                                       mixed_prob       = 0.0,
+                                                                      )
         
-        # Calculate probabilities for each sentence using ENSEMBLE METHODS
-        highlighted_sentences = list()
-        
-        for idx, sentence in enumerate(sentences):
-            if use_sentence_level:
-                # Use ENSEMBLE for sentence-level analysis
-                ai_prob, human_prob, mixed_prob, confidence, breakdown = self._calculate_sentence_ensemble_probability(sentence        = sentence, 
-                                                                                                                       metric_results  = metric_results,
-                                                                                                                       weights         = weights,
-                                                                                                                       ensemble_result = ensemble_result,
-                                                                                                                      )
-            else:
-                # Use document-level ensemble probabilities
-                ai_prob, human_prob, mixed_prob, confidence, breakdown = self._get_document_ensemble_probability(ensemble_result = ensemble_result,
-                                                                                                                 metric_results  = metric_results,
-                                                                                                                 weights         = weights,
-                                                                                                                )
+        return HighlightedSentence(text              = text,
+                                   ai_probability    = ai_prob,
+                                   human_probability = human_prob,
+                                   mixed_probability = 0.0,
+                                   confidence        = 0.3,
+                                   confidence_level  = confidence_level,
+                                   color_class       = color_class,
+                                   tooltip           = f"Fallback: {tooltip_base}\nProcessing failed for this sentence",
+                                   index             = index,
+                                   is_mixed_content  = False,
+                                   metric_breakdown  = {"fallback": ai_prob},
+                                  )
+
+
+    def _create_error_fallback(self, text: str, metric_results: Dict[str, MetricResult]) -> List[HighlightedSentence]:
+        """
+        Create fallback when entire processing fails
+        """
+        return [HighlightedSentence(text              = text[:100] + "..." if len(text) > 100 else text,
+                                    ai_probability    = 0.5,
+                                    human_probability = 0.5,
+                                    mixed_probability = 0.0,
+                                    confidence        = 0.1,
+                                    confidence_level  = get_confidence_level(0.1),
+                                    color_class       = "uncertain",
+                                    tooltip           = "Error in text processing",
+                                    index             = 0,
+                                    is_mixed_content  = False,
+                                    metric_breakdown  = {"error": 0.5},
+                                   )
+               ]
+ 
+
+    def _split_sentences_with_fallback(self, text: str) -> List[str]:
+        """
+        Split text into sentences with comprehensive fallback handling
+        """
+        try:
+            sentences          = self.text_processor.split_sentences(text)
+            filtered_sentences = [s.strip() for s in sentences if len(s.strip()) >= 3] 
             
-            # Apply domain-specific adjustments
-            ai_prob = self._apply_domain_specific_adjustments(sentence, ai_prob, len(sentence.split()))
+            if filtered_sentences:
+                return filtered_sentences
             
-            # Determine if this is mixed content
-            is_mixed_content                     = (mixed_prob > self.MIXED_THRESHOLD)
+            # Fallback: split by common sentence endings
+            fallback_sentences = re.split(r'[.!?]+', text)
+            fallback_sentences = [s.strip() for s in fallback_sentences if len(s.strip()) >= 3]
             
-            # Get confidence level
-            confidence_level                     = get_confidence_level(confidence)
+            if fallback_sentences:
+                return fallback_sentences
             
-            # Get color class (consider mixed content)
-            color_class, color_hex, tooltip_base = self._get_color_for_probability(probability      = ai_prob,
-                                                                                   is_mixed_content = is_mixed_content,
-                                                                                   mixed_prob       = mixed_prob,
-                                                                                  )
+            # Ultimate fallback: treat as single sentence if meaningful
+            if text.strip():
+                return [text.strip()]
             
-            # Generate enhanced tooltip
-            tooltip                              = self._generate_ensemble_tooltip(sentence         = sentence, 
-                                                                                   ai_prob          = ai_prob,
-                                                                                   human_prob       = human_prob,
-                                                                                   mixed_prob       = mixed_prob,
-                                                                                   confidence       = confidence, 
-                                                                                   confidence_level = confidence_level, 
-                                                                                   tooltip_base     = tooltip_base, 
-                                                                                   breakdown        = breakdown,
-                                                                                   is_mixed_content = is_mixed_content,
-                                                                                  )
+            return []
             
-            highlighted_sentences.append(HighlightedSentence(text              = sentence,
-                                                             ai_probability    = ai_prob,
-                                                             human_probability = human_prob,
-                                                             mixed_probability = mixed_prob,
-                                                             confidence        = confidence,
-                                                             confidence_level  = confidence_level,
-                                                             color_class       = color_class,
-                                                             tooltip           = tooltip,
-                                                             index             = idx,
-                                                             is_mixed_content  = is_mixed_content,
-                                                             metric_breakdown  = breakdown,
-                                                            )
-                                        )
-        
-        return highlighted_sentences
+        except Exception as e:
+            logger.warning(f"Sentence splitting failed, using fallback: {e}")
+            # Return text as single sentence
+            return [text] if text.strip() else []
+
 
-    
     def _calculate_sentence_ensemble_probability(self, sentence: str, metric_results: Dict[str, MetricResult], weights: Dict[str, float], 
                                                  ensemble_result: Optional[EnsembleResult] = None) -> Tuple[float, float, float, float, Dict[str, float]]:
         """
@@ -193,10 +322,24 @@ class TextHighlighter:
         """
         sentence_length = len(sentence.split())
         
-        # IMPROVED: Better handling of short sentences
+        # Handling short sentences - don't force neutral
         if (sentence_length < 3):
-            # Return neutral probability for very short sentences with low confidence
-            return 0.5, 0.5, 0.0, 0.3, {"short_sentence": 0.5}
+            # Return probabilities with lower confidence for very short sentences
+            base_ai_prob    = 0.5
+
+            # Low confidence for very short sentences
+            base_confidence = 0.2  
+
+            breakdown       = {"short_sentence" : base_ai_prob}
+            
+            # Try to get some signal from available metrics
+            for name, result in metric_results.items():
+                if ((result.error is None) and (weights.get(name, 0) > 0)):
+                    base_ai_prob    = result.ai_probability
+                    breakdown[name] = base_ai_prob
+                    break
+            
+            return base_ai_prob, 1.0 - base_ai_prob, 0.0, base_confidence, breakdown
         
         # Calculate sentence-level metric results
         sentence_metric_results = dict()
@@ -204,20 +347,27 @@ class TextHighlighter:
         
         for name, doc_result in metric_results.items():
             if doc_result.error is None:
-                # Compute sentence-level probability for this metric
-                sentence_prob                 = self._compute_sentence_metric(metric_name = name,
-                                                                              sentence    = sentence,
-                                                                              result      = doc_result,
-                                                                              weight      = weights.get(name, 0.0),
-                                                                             )
-                
-                # Create sentence-level MetricResult
-                sentence_metric_results[name] = self._create_sentence_metric_result(metric_name = name,
-                                                                                    ai_prob     = sentence_prob,
-                                                                                    doc_result  = doc_result,
-                                                                                   )
+                try:
+                    # Compute sentence-level probability for this metric
+                    sentence_prob                 = self._compute_sentence_metric(metric_name = name,
+                                                                                  sentence    = sentence,
+                                                                                  result      = doc_result,
+                                                                                  weight      = weights.get(name, 0.0),
+                                                                                 )
+                    
+                    # Create sentence-level MetricResult
+                    sentence_metric_results[name] = self._create_sentence_metric_result(metric_name = name,
+                                                                                        ai_prob     = sentence_prob,
+                                                                                        doc_result  = doc_result,
+                                                                                        sentence_length = sentence_length,
+                                                                                       )
+                    
+                    breakdown[name]               = sentence_prob
                 
-                breakdown[name]               = sentence_prob
+                except Exception as e:
+                    logger.warning(f"Metric {name} failed for sentence: {e}")
+                    # Use document probability as fallback
+                    breakdown[name] = doc_result.ai_probability
         
         # Use ensemble to combine sentence-level metrics
         if sentence_metric_results:
@@ -226,8 +376,11 @@ class TextHighlighter:
                                                                  domain         = self.domain,
                                                                 )
                 
-                return (ensemble_sentence_result.ai_probability, ensemble_sentence_result.human_probability, ensemble_sentence_result.mixed_probability, 
-                        ensemble_sentence_result.overall_confidence, breakdown)
+                return (ensemble_sentence_result.ai_probability, 
+                        ensemble_sentence_result.human_probability, 
+                        ensemble_sentence_result.mixed_probability, 
+                        ensemble_sentence_result.overall_confidence, 
+                        breakdown)
                         
             except Exception as e:
                 logger.warning(f"Sentence ensemble failed: {e}")
@@ -262,12 +415,12 @@ class TextHighlighter:
         return adjusted_prob
     
 
-    def _create_sentence_metric_result(self, metric_name: str, ai_prob: float, doc_result: MetricResult) -> MetricResult:
+    def _create_sentence_metric_result(self, metric_name: str, ai_prob: float, doc_result: MetricResult, sentence_length: int) -> MetricResult:
         """
         Create sentence-level MetricResult from document-level result
         """
-        # Adjust confidence based on sentence characteristics
-        sentence_confidence = self._calculate_sentence_confidence(doc_result.confidence)
+        # IMPROVED: Calculate confidence based on sentence characteristics
+        sentence_confidence = self._calculate_sentence_confidence(doc_result.confidence, sentence_length)
         
         return MetricResult(metric_name       = metric_name,
                             ai_probability    = ai_prob,
@@ -279,12 +432,15 @@ class TextHighlighter:
                            )
     
 
-    def _calculate_sentence_confidence(self, doc_confidence: float) -> float:
+    def _calculate_sentence_confidence(self, doc_confidence: float, sentence_length: int) -> float:
         """
-        Calculate confidence for sentence-level analysis
+        IMPROVED: Calculate confidence for sentence-level analysis with length consideration
         """
-        # Sentence-level analysis typically has lower confidence
-        return max(0.1, doc_confidence * 0.8)
+        base_reduction = 0.8
+        # Scale confidence penalty with sentence length
+        length_penalty = max(0.3, min(1.0, sentence_length / 12.0))  # Normalize around 12 words
+        
+        return max(0.1, doc_confidence * base_reduction * length_penalty)
     
 
     def _calculate_weighted_probability(self, metric_results: Dict[str, MetricResult], weights: Dict[str, float], breakdown: Dict[str, float]) -> Tuple[float, float, float, float, Dict[str, float]]:
@@ -306,8 +462,8 @@ class TextHighlighter:
                     confidences.append(result.confidence)
                     total_weight += weight
         
-        if not weighted_ai_probs or total_weight == 0:
-            return 0.5, 0.5, 0.0, 0.5, {}
+        if ((not weighted_ai_probs) or (total_weight == 0)):
+            return 0.5, 0.5, 0.0, 0.5, breakdown or {}
         
         ai_prob        = sum(weighted_ai_probs) / total_weight
         human_prob     = sum(weighted_human_probs) / total_weight
@@ -331,84 +487,94 @@ class TextHighlighter:
         else:
             # Calculate from metrics
             return self._calculate_weighted_probability(metric_results, weights, {})
-    
+
 
     def _apply_domain_specific_adjustments(self, sentence: str, ai_prob: float, sentence_length: int) -> float:
         """
-        Apply domain-specific adjustments to AI probability - UPDATED FOR ALL DOMAINS
+        Apply domain-specific adjustments to AI probability with limits
         """
+        original_prob  = ai_prob
+        adjustments    = list()
         sentence_lower = sentence.lower()
         
         # Technical & AI/ML domains
-        if self.domain in [Domain.AI_ML, Domain.SOFTWARE_DEV, Domain.TECHNICAL_DOC, Domain.ENGINEERING, Domain.SCIENCE]:
+        if (self.domain in [Domain.AI_ML, Domain.SOFTWARE_DEV, Domain.TECHNICAL_DOC, Domain.ENGINEERING, Domain.SCIENCE]):
             if self._has_technical_terms(sentence_lower):
-                # Technical terms more common in AI
-                ai_prob *= 1.1  
+                adjustments.append(1.1)  
 
             elif self._has_code_like_patterns(sentence):
-                ai_prob *= 1.15
+                adjustments.append(1.15)
             
-            elif sentence_length > 35:
-                ai_prob *= 1.05
+            elif (sentence_length > 35):
+                adjustments.append(1.05)
                 
         # Creative & informal domains
-        elif self.domain in [Domain.CREATIVE, Domain.SOCIAL_MEDIA, Domain.BLOG_PERSONAL]:
+        elif (self.domain in [Domain.CREATIVE, Domain.SOCIAL_MEDIA, Domain.BLOG_PERSONAL]):
             if self._has_informal_language(sentence_lower):
-                # Informal language more human-like
-                ai_prob *= 0.7  
+                adjustments.append(0.7)  
             
             elif self._has_emotional_language(sentence):
-                ai_prob *= 0.8
+                adjustments.append(0.8)
 
             elif (sentence_length < 10):
-                ai_prob *= 0.8
+                adjustments.append(0.8)
                 
         # Academic & formal domains
-        elif self.domain in [Domain.ACADEMIC, Domain.LEGAL, Domain.MEDICAL]:
+        elif (self.domain in [Domain.ACADEMIC, Domain.LEGAL, Domain.MEDICAL]):
             if self._has_citation_patterns(sentence):
-                # Citations more human-like
-                ai_prob *= 0.8  
+                adjustments.append(0.8)  
 
             elif self._has_technical_terms(sentence_lower):
-                ai_prob *= 1.1
+                adjustments.append(1.1)
 
             elif (sentence_length > 40):
-                ai_prob *= 1.1
+                adjustments.append(1.1)
                 
         # Business & professional domains
-        elif self.domain in [Domain.BUSINESS, Domain.MARKETING, Domain.JOURNALISM]:
+        elif (self.domain in [Domain.BUSINESS, Domain.MARKETING, Domain.JOURNALISM]):
             if self._has_business_jargon(sentence_lower):
-                # Jargon can be AI-like
-                ai_prob *= 1.05  
+                adjustments.append(1.05)  
             
             elif self._has_ambiguous_phrasing(sentence_lower):
-                # Ambiguity more human
-                ai_prob *= 0.9  
+                adjustments.append(0.9)  
             
             elif (15 <= sentence_length <= 25):
-                ai_prob *= 0.9
+                adjustments.append(0.9)
                 
         # Tutorial & educational domains
         elif (self.domain == Domain.TUTORIAL):
             if self._has_instructional_language(sentence_lower):
-                # Instructional tone more human
-                ai_prob *= 0.85  
+                adjustments.append(0.85)  
 
             elif self._has_step_by_step_pattern(sentence):
-                ai_prob *= 0.8
+                adjustments.append(0.8)
             
             elif self._has_examples(sentence):
-                ai_prob *= 0.9
+                adjustments.append(0.9)
         
         # General domain - minimal adjustments
-        elif self.domain == Domain.GENERAL:
+        elif (self.domain == Domain.GENERAL):
             if self._has_complex_structure(sentence):
-                ai_prob *= 0.9
+                adjustments.append(0.9)
             
             elif self._has_repetition(sentence):
-                ai_prob *= 1.1
+                adjustments.append(1.1)
+        
+        # Apply adjustments with limits - take strongest 2 adjustments maximum
+        if adjustments:
+            # Sort by impact (farthest from 1.0)
+            adjustments.sort(key = lambda x: abs(x - 1.0), reverse = True)
+            # Limit to 2 strongest
+            strongest_adjustments = adjustments[:2]  
+            
+            for adjustment in strongest_adjustments:
+                ai_prob *= adjustment
+        
+        # Ensure probability stays within bounds and doesn't change too drastically : Maximum 30% change from original
+        max_change   = 0.3  
+        bounded_prob = max(original_prob - max_change, min(original_prob + max_change, ai_prob))
         
-        return max(0.0, min(1.0, ai_prob))
+        return max(0.0, min(1.0, bounded_prob))
     
 
     def _apply_metric_specific_adjustments(self, metric_name: str, sentence: str, base_prob: float, sentence_length: int, thresholds: MetricThresholds) -> float:
@@ -466,8 +632,12 @@ class TextHighlighter:
 
     def _get_color_for_probability(self, probability: float, is_mixed_content: bool = False, mixed_prob: float = 0.0) -> Tuple[str, str, str]:
         """
-        Get color class with mixed content support
+        Get color class with mixed content support and no threshold gaps
         """
+        # Handle probability = 1.0 explicitly
+        if (probability >= 1.0):
+            return "very-high-ai", "#fecaca", "Very likely AI-generated (100%)"
+        
         # Check mixed content first
         if (is_mixed_content and (mixed_prob > self.MIXED_THRESHOLD)):
             return "mixed-content", "#e9d5ff", f"Mixed AI/Human content ({mixed_prob:.1%} mixed)"
@@ -477,12 +647,12 @@ class TextHighlighter:
             if (min_thresh <= probability < max_thresh):
                 return color_class, color_hex, tooltip
         
-        # Fallback
-        return "uncertain", "#fef9c3", "Uncertain"
-    
+        # Fallback for probability = 1.0 (should be caught above, but just in case)
+        return "very-high-ai", "#fecaca", "Very likely AI-generated"
+
 
     def _generate_ensemble_tooltip(self, sentence: str, ai_prob: float, human_prob: float, mixed_prob: float, confidence: float, confidence_level: ConfidenceLevel, 
-                                  tooltip_base: str, breakdown: Optional[Dict[str, float]] = None, is_mixed_content: bool = False) -> str:
+                                   tooltip_base: str, breakdown: Optional[Dict[str, float]] = None, is_mixed_content: bool = False) -> str:
         """
         Generate enhanced tooltip with ENSEMBLE information
         """
@@ -504,7 +674,7 @@ class TextHighlighter:
             for metric, prob in list(breakdown.items())[:4]:  
                 tooltip += f"\n• {metric}: {prob:.1%}"
         
-        tooltip += f"\n\nEnsemble Method: {self.ensemble.primary_method}"
+        tooltip += f"\n\nEnsemble Method: {getattr(self.ensemble, 'primary_method', 'fallback')}"
         
         return tooltip
 
@@ -619,7 +789,7 @@ class TextHighlighter:
         Analyze sentence complexity (0 = simple, 1 = complex)
         """
         words = sentence.split()
-        if len(words) < 5:
+        if (len(words) < 5):
             return 0.2
         
         complexity_indicators = ['although', 'because', 'while', 'when', 'if', 'since', 'unless', 'until', 'which', 'that', 'who', 'whom', 'whose', 'and', 'but', 'or', 'yet', 'so', 'however', 'therefore', 'moreover', 'furthermore', 'nevertheless', ',', ';', ':', '—']
@@ -637,7 +807,7 @@ class TextHighlighter:
         
         clause_indicators = [',', ';', 'and', 'but', 'or', 'because', 'although']
         clause_count      = sum(1 for indicator in clause_indicators if indicator in sentence.lower())
-        score             += min(0.2, clause_count * 0.05)
+        score            += min(0.2, clause_count * 0.05)
         
         return min(1.0, score)
     
@@ -671,7 +841,7 @@ class TextHighlighter:
         for sentence in sentences:
             clean_sentence = sentence.strip()
             
-            if (len(clean_sentence) >= 10):
+            if (len(clean_sentence) >= 3): 
                 filtered_sentences.append(clean_sentence)
         
         return filtered_sentences
@@ -1002,7 +1172,7 @@ class TextHighlighter:
         total_sentences = len(highlighted_sentences)
         
         # Calculate weighted risk score
-        weighted_risk = 0.0
+        weighted_risk   = 0.0
 
         for sent in highlighted_sentences:
             weight         = self.RISK_WEIGHTS.get(sent.color_class, 0.4)

logs/application/app_2025-10-29.log

@@ -1,105 +0,0 @@
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-{"text": "Loading model: domain_classifier (cross-encoder/nli-roberta-base)\n", "record": {"elapsed": {"repr": "0:00:03.683492", "seconds": 3.683492}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Loading model: domain_classifier (cross-encoder/nli-roberta-base)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:27.261165+05:30", "timestamp": 1761742167.261165}}}
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-{"text": "Successfully loaded model: domain_classifier\n", "record": {"elapsed": {"repr": "0:00:04.551392", "seconds": 4.551392}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 262, "message": "Successfully loaded model: domain_classifier", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:28.129065+05:30", "timestamp": 1761742168.129065}}}
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-{"text": "Added model to cache: domain_classifier_fallback\n", "record": {"elapsed": {"repr": "0:00:05.680966", "seconds": 5.680966}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 86, "message": "Added model to cache: domain_classifier_fallback", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:29.258639+05:30", "timestamp": 1761742169.258639}}}
-{"text": "Successfully loaded model: domain_classifier_fallback\n", "record": {"elapsed": {"repr": "0:00:05.681158", "seconds": 5.681158}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 262, "message": "Successfully loaded model: domain_classifier_fallback", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:29.258831+05:30", "timestamp": 1761742169.258831}}}
-{"text": "Fallback classifier loaded successfully\n", "record": {"elapsed": {"repr": "0:00:05.681248", "seconds": 5.681248}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/text_auth/processors/domain_classifier.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 69, "message": "Fallback classifier loaded successfully", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:29.258921+05:30", "timestamp": 1761742169.258921}}}
-{"text": "Domain classifier initialized successfully\n", "record": {"elapsed": {"repr": "0:00:05.681335", "seconds": 5.681335}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/text_auth/processors/domain_classifier.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 76, "message": "Domain classifier initialized successfully", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:29.259008+05:30", "timestamp": 1761742169.259008}}}
-{"text": "Initializing language detection model...\n", "record": {"elapsed": {"repr": "0:00:05.681407", "seconds": 5.681407}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/text_auth/processors/language_detector.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 195, "message": "Initializing language detection model...", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:29.259080+05:30", "timestamp": 1761742169.25908}}}
-{"text": "Loading pipeline: text-classification with language_detector\n", "record": {"elapsed": {"repr": "0:00:05.681476", "seconds": 5.681476}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_pipeline", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 430, "message": "Loading pipeline: text-classification with language_detector", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:29.259149+05:30", "timestamp": 1761742169.259149}}}
-{"text": "Language detector initialized successfully\n", "record": {"elapsed": {"repr": "0:00:06.694072", "seconds": 6.694072}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/text_auth/processors/language_detector.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 203, "message": "Language detector initialized successfully", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:30.271745+05:30", "timestamp": 1761742170.271745}}}
-{"text": "Initializing entropy metric...\n", "record": {"elapsed": {"repr": "0:00:06.694295", "seconds": 6.694295}, "exception": null, "extra": {}, "file": {"name": "entropy.py", "path": "/Users/itobuz/projects/text_auth/metrics/entropy.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 39, "message": "Initializing entropy metric...", "module": "entropy", "name": "metrics.entropy", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:30.271968+05:30", "timestamp": 1761742170.271968}}}
-{"text": "Loading model: perplexity_gpt2 (gpt2)\n", "record": {"elapsed": {"repr": "0:00:06.694388", "seconds": 6.694388}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Loading model: perplexity_gpt2 (gpt2)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:30.272061+05:30", "timestamp": 1761742170.272061}}}
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-{"text": "Entropy metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:08.177499", "seconds": 8.177499}, "exception": null, "extra": {}, "file": {"name": "entropy.py", "path": "/Users/itobuz/projects/text_auth/metrics/entropy.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 52, "message": "Entropy metric initialized successfully", "module": "entropy", "name": "metrics.entropy", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:31.755172+05:30", "timestamp": 1761742171.755172}}}
-{"text": "Initializing perplexity metric...\n", "record": {"elapsed": {"repr": "0:00:08.177585", "seconds": 8.177585}, "exception": null, "extra": {}, "file": {"name": "perplexity.py", "path": "/Users/itobuz/projects/text_auth/metrics/perplexity.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 41, "message": "Initializing perplexity metric...", "module": "perplexity", "name": "metrics.perplexity", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:31.755258+05:30", "timestamp": 1761742171.755258}}}
-{"text": "Perplexity metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:08.177656", "seconds": 8.177656}, "exception": null, "extra": {}, "file": {"name": "perplexity.py", "path": "/Users/itobuz/projects/text_auth/metrics/perplexity.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 55, "message": "Perplexity metric initialized successfully", "module": "perplexity", "name": "metrics.perplexity", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:31.755329+05:30", "timestamp": 1761742171.755329}}}
-{"text": "Initializing semantic analysis metric...\n", "record": {"elapsed": {"repr": "0:00:08.177722", "seconds": 8.177722}, "exception": null, "extra": {}, "file": {"name": "semantic_analysis.py", "path": "/Users/itobuz/projects/text_auth/metrics/semantic_analysis.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 41, "message": "Initializing semantic analysis metric...", "module": "semantic_analysis", "name": "metrics.semantic_analysis", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:31.755395+05:30", "timestamp": 1761742171.755395}}}
-{"text": "Loading model: semantic_primary (sentence-transformers/all-MiniLM-L6-v2)\n", "record": {"elapsed": {"repr": "0:00:08.177789", "seconds": 8.177789}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Loading model: semantic_primary (sentence-transformers/all-MiniLM-L6-v2)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:31.755462+05:30", "timestamp": 1761742171.755462}}}
-{"text": "Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", "record": {"elapsed": {"repr": "0:00:08.179934", "seconds": 8.179934}, "exception": null, "extra": {}, "file": {"name": "SentenceTransformer.py", "path": "/Users/itobuz/anaconda3/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py"}, "function": "__init__", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 218, "message": "Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2", "module": "SentenceTransformer", "name": "sentence_transformers.SentenceTransformer", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:31.757607+05:30", "timestamp": 1761742171.757607}}}
-{"text": "Added model to cache: semantic_primary\n", "record": {"elapsed": {"repr": "0:00:12.965674", "seconds": 12.965674}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 86, "message": "Added model to cache: semantic_primary", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.543347+05:30", "timestamp": 1761742176.543347}}}
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-{"text": "Semantic analysis metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:12.966523", "seconds": 12.966523}, "exception": null, "extra": {}, "file": {"name": "semantic_analysis.py", "path": "/Users/itobuz/projects/text_auth/metrics/semantic_analysis.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 49, "message": "Semantic analysis metric initialized successfully", "module": "semantic_analysis", "name": "metrics.semantic_analysis", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.544196+05:30", "timestamp": 1761742176.544196}}}
-{"text": "Initializing linguistic metric...\n", "record": {"elapsed": {"repr": "0:00:12.966714", "seconds": 12.966714}, "exception": null, "extra": {}, "file": {"name": "linguistic.py", "path": "/Users/itobuz/projects/text_auth/metrics/linguistic.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 39, "message": "Initializing linguistic metric...", "module": "linguistic", "name": "metrics.linguistic", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.544387+05:30", "timestamp": 1761742176.544387}}}
-{"text": "Loading model: linguistic_spacy (en_core_web_sm)\n", "record": {"elapsed": {"repr": "0:00:12.966901", "seconds": 12.966901}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Loading model: linguistic_spacy (en_core_web_sm)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.544574+05:30", "timestamp": 1761742176.544574}}}
-{"text": "Loaded spaCy model: en_core_web_sm\n", "record": {"elapsed": {"repr": "0:00:13.261871", "seconds": 13.261871}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "_load_spacy_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 447, "message": "Loaded spaCy model: en_core_web_sm", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.839544+05:30", "timestamp": 1761742176.839544}}}
-{"text": "Added model to cache: linguistic_spacy\n", "record": {"elapsed": {"repr": "0:00:13.262395", "seconds": 13.262395}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 86, "message": "Added model to cache: linguistic_spacy", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.840068+05:30", "timestamp": 1761742176.840068}}}
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-{"text": "Linguistic metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:13.262600", "seconds": 13.2626}, "exception": null, "extra": {}, "file": {"name": "linguistic.py", "path": "/Users/itobuz/projects/text_auth/metrics/linguistic.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 46, "message": "Linguistic metric initialized successfully", "module": "linguistic", "name": "metrics.linguistic", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.840273+05:30", "timestamp": 1761742176.840273}}}
-{"text": "Initializing DetectGPT metric...\n", "record": {"elapsed": {"repr": "0:00:13.262676", "seconds": 13.262676}, "exception": null, "extra": {}, "file": {"name": "detect_gpt.py", "path": "/Users/itobuz/projects/text_auth/metrics/detect_gpt.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 44, "message": "Initializing DetectGPT metric...", "module": "detect_gpt", "name": "metrics.detect_gpt", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.840349+05:30", "timestamp": 1761742176.840349}}}
-{"text": "Loading model: detectgpt_base (gpt2)\n", "record": {"elapsed": {"repr": "0:00:13.262757", "seconds": 13.262757}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Loading model: detectgpt_base (gpt2)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:36.840430+05:30", "timestamp": 1761742176.84043}}}
-{"text": "Evicted model from cache: domain_classifier\n", "record": {"elapsed": {"repr": "0:00:16.074200", "seconds": 16.0742}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 82, "message": "Evicted model from cache: domain_classifier", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:39.651873+05:30", "timestamp": 1761742179.651873}}}
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-{"text": "Evicted model from cache: domain_classifier_fallback\n", "record": {"elapsed": {"repr": "0:00:18.221749", "seconds": 18.221749}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 82, "message": "Evicted model from cache: domain_classifier_fallback", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:41.799422+05:30", "timestamp": 1761742181.799422}}}
-{"text": "Added model to cache: detectgpt_mask\n", "record": {"elapsed": {"repr": "0:00:18.221942", "seconds": 18.221942}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 86, "message": "Added model to cache: detectgpt_mask", "module": "model_manager", "name": "models.model_manager", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:19:41.799615+05:30", "timestamp": 1761742181.799615}}}
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-{"text": "Step 2: Detecting language...\n", "record": {"elapsed": {"repr": "0:01:07.933266", "seconds": 67.933266}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 272, "message": "Step 2: Detecting language...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:31.510939+05:30", "timestamp": 1761742231.510939}}}
-{"text": "Text too long, truncated to 2000 characters for language detection\n", "record": {"elapsed": {"repr": "0:01:07.941615", "seconds": 67.941615}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/text_auth/processors/language_detector.py"}, "function": "_detect_with_model", "level": {"icon": "⚠️", "name": "WARNING", "no": 30}, "line": 304, "message": "Text too long, truncated to 2000 characters for language detection", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:31.519288+05:30", "timestamp": 1761742231.519288}}}
-{"text": "Detected language: en (confidence: 0.98, method: xlm-roberta-model)\n", "record": {"elapsed": {"repr": "0:01:08.145482", "seconds": 68.145482}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/text_auth/processors/language_detector.py"}, "function": "detect", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 288, "message": "Detected language: en (confidence: 0.98, method: xlm-roberta-model)", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:31.723155+05:30", "timestamp": 1761742231.723155}}}
-{"text": "Step 3: Classifying domain...\n", "record": {"elapsed": {"repr": "0:01:08.145741", "seconds": 68.145741}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 291, "message": "Step 3: Classifying domain...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:31.723414+05:30", "timestamp": 1761742231.723414}}}
-{"text": "Primary model classified domain: social_media (confidence: 0.109)\n", "record": {"elapsed": {"repr": "0:01:10.726145", "seconds": 70.726145}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/text_auth/processors/domain_classifier.py"}, "function": "_classify_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Primary model classified domain: social_media (confidence: 0.109)", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:34.303818+05:30", "timestamp": 1761742234.303818}}}
-{"text": "Primary classifier low confidence, trying fallback model...\n", "record": {"elapsed": {"repr": "0:01:10.726378", "seconds": 70.726378}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/text_auth/processors/domain_classifier.py"}, "function": "classify", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 118, "message": "Primary classifier low confidence, trying fallback model...", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:34.304051+05:30", "timestamp": 1761742234.304051}}}
-{"text": "Fallback model classified domain: science (confidence: 0.063)\n", "record": {"elapsed": {"repr": "0:01:13.849320", "seconds": 73.84932}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/text_auth/processors/domain_classifier.py"}, "function": "_classify_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Fallback model classified domain: science (confidence: 0.063)", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:37.426993+05:30", "timestamp": 1761742237.426993}}}
-{"text": "Detected domain: social_media (confidence: 0.11)\n", "record": {"elapsed": {"repr": "0:01:13.849569", "seconds": 73.849569}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 319, "message": "Detected domain: social_media (confidence: 0.11)", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:37.427242+05:30", "timestamp": 1761742237.427242}}}
-{"text": "Step 4: Executing detection metrics calculations...\n", "record": {"elapsed": {"repr": "0:01:13.849687", "seconds": 73.849687}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 322, "message": "Step 4: Executing detection metrics calculations...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:37.427360+05:30", "timestamp": 1761742237.42736}}}
-{"text": "Executed 6 metrics successfully\n", "record": {"elapsed": {"repr": "0:01:20.393725", "seconds": 80.393725}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 362, "message": "Executed 6 metrics successfully", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:43.971398+05:30", "timestamp": 1761742243.971398}}}
-{"text": "Step 5: Aggregating results with ensemble...\n", "record": {"elapsed": {"repr": "0:01:20.393966", "seconds": 80.393966}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 365, "message": "Step 5: Aggregating results with ensemble...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:43.971639+05:30", "timestamp": 1761742243.971639}}}
-{"text": "Analysis complete: Human-Written (AI probability: 38.5%, confidence: 0.63) in 12.47s\n", "record": {"elapsed": {"repr": "0:01:20.394253", "seconds": 80.394253}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 394, "message": "Analysis complete: Human-Written (AI probability: 38.5%, confidence: 0.63) in 12.47s", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:43.971926+05:30", "timestamp": 1761742243.971926}}}
-{"text": "[analysis_1761742231503] Running attribution...\n", "record": {"elapsed": {"repr": "0:01:20.394704", "seconds": 80.394704}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/text_auth/text_auth_app.py"}, "function": "analyze_text", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 745, "message": "[analysis_1761742231503] Running attribution...", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:43.972377+05:30", "timestamp": 1761742243.972377}}}
-{"text": "[analysis_1761742231503] Generating highlights...\n", "record": {"elapsed": {"repr": "0:01:20.396346", "seconds": 80.396346}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/text_auth/text_auth_app.py"}, "function": "analyze_text", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 763, "message": "[analysis_1761742231503] Generating highlights...", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:43.974019+05:30", "timestamp": 1761742243.974019}}}
-{"text": "Detection completed: analysis_1761742231503 -> Human-Written\n", "record": {"elapsed": {"repr": "0:01:20.406711", "seconds": 80.406711}, "exception": null, "extra": {"log_type": "application", "extra": {"analysis_id": "analysis_1761742231503", "text_length": 6124, "verdict": "Human-Written", "confidence": 0.6342, "domain": "social_media", "processing_time_seconds": 12.4812, "timestamp": "2025-10-29T18:20:43.984376", "enable_attribution": true, "enable_highlighting": true}}, "file": {"name": "logger.py", "path": "/Users/itobuz/projects/text_auth/utils/logger.py"}, "function": "log_detection_event", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 419, "message": "Detection completed: analysis_1761742231503 -> Human-Written", "module": "logger", "name": "utils.logger", "process": {"id": 66535, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-10-29 18:20:43.984384+05:30", "timestamp": 1761742243.984384}}}
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+{"text": "LanguageDetector initialized (use_model=True)\n", "record": {"elapsed": {"repr": "0:00:04.037154", "seconds": 4.037154}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/office/text_auth/processors/language_detector.py"}, "function": "__init__", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 182, "message": "LanguageDetector initialized (use_model=True)", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:38.253745+05:30", "timestamp": 1762204238.253745}}}
+{"text": "Initialized 6 metrics: ['structural', 'entropy', 'perplexity', 'semantic_analysis', 'linguistic', 'multi_perturbation_stability']\n", "record": {"elapsed": {"repr": "0:00:04.037261", "seconds": 4.037261}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "_initialize_metrics", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 189, "message": "Initialized 6 metrics: ['structural', 'entropy', 'perplexity', 'semantic_analysis', 'linguistic', 'multi_perturbation_stability']", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:38.253852+05:30", "timestamp": 1762204238.253852}}}
+{"text": "AdvancedEnsembleClassifier initialized (primary=confidence_calibrated, fallback=domain_weighted, ml_ensemble=False)\n", "record": {"elapsed": {"repr": "0:00:04.037349", "seconds": 4.037349}, "exception": null, "extra": {}, "file": {"name": "ensemble.py", "path": "/Users/itobuz/projects/office/text_auth/detector/ensemble.py"}, "function": "__init__", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 91, "message": "AdvancedEnsembleClassifier initialized (primary=confidence_calibrated, fallback=domain_weighted, ml_ensemble=False)", "module": "ensemble", "name": "detector.ensemble", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:38.253940+05:30", "timestamp": 1762204238.25394}}}
+{"text": "DetectionOrchestrator initialized (language_detection=True, skip_expensive=False)\n", "record": {"elapsed": {"repr": "0:00:04.037419", "seconds": 4.037419}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "__init__", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 132, "message": "DetectionOrchestrator initialized (language_detection=True, skip_expensive=False)", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:38.254010+05:30", "timestamp": 1762204238.25401}}}
+{"text": "Initializing detection pipeline...\n", "record": {"elapsed": {"repr": "0:00:04.037533", "seconds": 4.037533}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 202, "message": "Initializing detection pipeline...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:38.254124+05:30", "timestamp": 1762204238.254124}}}
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+{"text": "Loading model: domain_classifier (cross-encoder/nli-roberta-base)\n", "record": {"elapsed": {"repr": "0:00:04.037752", "seconds": 4.037752}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: domain_classifier (cross-encoder/nli-roberta-base)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:38.254343+05:30", "timestamp": 1762204238.254343}}}
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+{"text": "Successfully loaded model: domain_classifier\n", "record": {"elapsed": {"repr": "0:00:05.024486", "seconds": 5.024486}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: domain_classifier", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:39.241077+05:30", "timestamp": 1762204239.241077}}}
+{"text": "Loading model: domain_classifier_fallback (microsoft/deberta-v3-small)\n", "record": {"elapsed": {"repr": "0:00:05.024576", "seconds": 5.024576}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: domain_classifier_fallback (microsoft/deberta-v3-small)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:39.241167+05:30", "timestamp": 1762204239.241167}}}
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+{"text": "Successfully loaded model: domain_classifier_fallback\n", "record": {"elapsed": {"repr": "0:00:06.618167", "seconds": 6.618167}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: domain_classifier_fallback", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:40.834758+05:30", "timestamp": 1762204240.834758}}}
+{"text": "Fallback classifier loaded successfully\n", "record": {"elapsed": {"repr": "0:00:06.618250", "seconds": 6.61825}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 69, "message": "Fallback classifier loaded successfully", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:40.834841+05:30", "timestamp": 1762204240.834841}}}
+{"text": "Domain classifier initialized successfully\n", "record": {"elapsed": {"repr": "0:00:06.618328", "seconds": 6.618328}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 76, "message": "Domain classifier initialized successfully", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:40.834919+05:30", "timestamp": 1762204240.834919}}}
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+{"text": "Loading pipeline: text-classification with language_detector\n", "record": {"elapsed": {"repr": "0:00:06.618570", "seconds": 6.61857}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_pipeline", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 491, "message": "Loading pipeline: text-classification with language_detector", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:40.835161+05:30", "timestamp": 1762204240.835161}}}
+{"text": "Language detector initialized successfully\n", "record": {"elapsed": {"repr": "0:00:07.492609", "seconds": 7.492609}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/office/text_auth/processors/language_detector.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 206, "message": "Language detector initialized successfully", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:41.709200+05:30", "timestamp": 1762204241.7092}}}
+{"text": "Initializing entropy metric...\n", "record": {"elapsed": {"repr": "0:00:07.492845", "seconds": 7.492845}, "exception": null, "extra": {}, "file": {"name": "entropy.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/entropy.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 39, "message": "Initializing entropy metric...", "module": "entropy", "name": "metrics.entropy", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:41.709436+05:30", "timestamp": 1762204241.709436}}}
+{"text": "Loading model: perplexity_gpt2 (gpt2)\n", "record": {"elapsed": {"repr": "0:00:07.492937", "seconds": 7.492937}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: perplexity_gpt2 (gpt2)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:41.709528+05:30", "timestamp": 1762204241.709528}}}
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+{"text": "Successfully loaded model: perplexity_gpt2\n", "record": {"elapsed": {"repr": "0:00:09.470415", "seconds": 9.470415}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: perplexity_gpt2", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.687006+05:30", "timestamp": 1762204243.687006}}}
+{"text": "Entropy metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:09.470506", "seconds": 9.470506}, "exception": null, "extra": {}, "file": {"name": "entropy.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/entropy.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 52, "message": "Entropy metric initialized successfully", "module": "entropy", "name": "metrics.entropy", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.687097+05:30", "timestamp": 1762204243.687097}}}
+{"text": "Initializing perplexity metric...\n", "record": {"elapsed": {"repr": "0:00:09.470584", "seconds": 9.470584}, "exception": null, "extra": {}, "file": {"name": "perplexity.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/perplexity.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 41, "message": "Initializing perplexity metric...", "module": "perplexity", "name": "metrics.perplexity", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.687175+05:30", "timestamp": 1762204243.687175}}}
+{"text": "Perplexity metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:09.470659", "seconds": 9.470659}, "exception": null, "extra": {}, "file": {"name": "perplexity.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/perplexity.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 55, "message": "Perplexity metric initialized successfully", "module": "perplexity", "name": "metrics.perplexity", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.687250+05:30", "timestamp": 1762204243.68725}}}
+{"text": "Initializing semantic analysis metric...\n", "record": {"elapsed": {"repr": "0:00:09.470730", "seconds": 9.47073}, "exception": null, "extra": {}, "file": {"name": "semantic_analysis.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/semantic_analysis.py"}, "function": "initialize", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 41, "message": "Initializing semantic analysis metric...", "module": "semantic_analysis", "name": "metrics.semantic_analysis", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.687321+05:30", "timestamp": 1762204243.687321}}}
+{"text": "Loading model: semantic_primary (sentence-transformers/all-MiniLM-L6-v2)\n", "record": {"elapsed": {"repr": "0:00:09.470798", "seconds": 9.470798}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: semantic_primary (sentence-transformers/all-MiniLM-L6-v2)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.687389+05:30", "timestamp": 1762204243.687389}}}
+{"text": "Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n", "record": {"elapsed": {"repr": "0:00:09.473093", "seconds": 9.473093}, "exception": null, "extra": {}, "file": {"name": "SentenceTransformer.py", "path": "/Users/itobuz/anaconda3/lib/python3.10/site-packages/sentence_transformers/SentenceTransformer.py"}, "function": "__init__", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 218, "message": "Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2", "module": "SentenceTransformer", "name": "sentence_transformers.SentenceTransformer", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:43.689684+05:30", "timestamp": 1762204243.689684}}}
+{"text": "Added model to cache: semantic_primary\n", "record": {"elapsed": {"repr": "0:00:14.013444", "seconds": 14.013444}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 87, "message": "Added model to cache: semantic_primary", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:48.230035+05:30", "timestamp": 1762204248.230035}}}
+{"text": "Successfully loaded model: semantic_primary\n", "record": {"elapsed": {"repr": "0:00:14.014011", "seconds": 14.014011}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: semantic_primary", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:48.230602+05:30", "timestamp": 1762204248.230602}}}
+{"text": "Semantic analysis metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:14.014265", "seconds": 14.014265}, "exception": null, "extra": {}, "file": {"name": "semantic_analysis.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/semantic_analysis.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 49, "message": "Semantic analysis metric initialized successfully", "module": "semantic_analysis", "name": "metrics.semantic_analysis", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:48.230856+05:30", "timestamp": 1762204248.230856}}}
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+{"text": "Loading model: linguistic_spacy (en_core_web_sm)\n", "record": {"elapsed": {"repr": "0:00:14.014708", "seconds": 14.014708}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: linguistic_spacy (en_core_web_sm)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:48.231299+05:30", "timestamp": 1762204248.231299}}}
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+{"text": "Successfully loaded model: linguistic_spacy\n", "record": {"elapsed": {"repr": "0:00:14.322937", "seconds": 14.322937}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: linguistic_spacy", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:48.539528+05:30", "timestamp": 1762204248.539528}}}
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+{"text": "Loading model: multi_perturbation_base (gpt2)\n", "record": {"elapsed": {"repr": "0:00:14.323156", "seconds": 14.323156}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: multi_perturbation_base (gpt2)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:48.539747+05:30", "timestamp": 1762204248.539747}}}
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+{"text": "Successfully loaded model: multi_perturbation_base\n", "record": {"elapsed": {"repr": "0:00:16.259685", "seconds": 16.259685}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: multi_perturbation_base", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:50.476276+05:30", "timestamp": 1762204250.476276}}}
+{"text": "✓ GPT-2 model loaded for MultiPerturbationStability\n", "record": {"elapsed": {"repr": "0:00:16.382942", "seconds": 16.382942}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 62, "message": "✓ GPT-2 model loaded for MultiPerturbationStability", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:50.599533+05:30", "timestamp": 1762204250.599533}}}
+{"text": "Loading model: multi_perturbation_mask (distilroberta-base)\n", "record": {"elapsed": {"repr": "0:00:16.383195", "seconds": 16.383195}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 222, "message": "Loading model: multi_perturbation_mask (distilroberta-base)", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:50.599786+05:30", "timestamp": 1762204250.599786}}}
+{"text": "Evicted model from cache: domain_classifier_fallback\n", "record": {"elapsed": {"repr": "0:00:17.880725", "seconds": 17.880725}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "put", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 83, "message": "Evicted model from cache: domain_classifier_fallback", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.097316+05:30", "timestamp": 1762204252.097316}}}
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+{"text": "Successfully loaded model: multi_perturbation_mask\n", "record": {"elapsed": {"repr": "0:00:17.881040", "seconds": 17.88104}, "exception": null, "extra": {}, "file": {"name": "model_manager.py", "path": "/Users/itobuz/projects/office/text_auth/models/model_manager.py"}, "function": "load_model", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 269, "message": "Successfully loaded model: multi_perturbation_mask", "module": "model_manager", "name": "models.model_manager", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.097631+05:30", "timestamp": 1762204252.097631}}}
+{"text": "✓ DistilRoBERTa model loaded for MultiPerturbationStability\n", "record": {"elapsed": {"repr": "0:00:17.975351", "seconds": 17.975351}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 84, "message": "✓ DistilRoBERTa model loaded for MultiPerturbationStability", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.191942+05:30", "timestamp": 1762204252.191942}}}
+{"text": "GPT-2 test - Likelihood: 5.3535\n", "record": {"elapsed": {"repr": "0:00:18.153332", "seconds": 18.153332}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "_verify_model_loading", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 114, "message": "GPT-2 test - Likelihood: 5.3535", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.369923+05:30", "timestamp": 1762204252.369923}}}
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+{"text": "DistilRoBERTa tokenization test - Input shape: torch.Size([1, 11])\n", "record": {"elapsed": {"repr": "0:00:18.156860", "seconds": 18.15686}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "_verify_model_loading", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 128, "message": "DistilRoBERTa tokenization test - Input shape: torch.Size([1, 11])", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.373451+05:30", "timestamp": 1762204252.373451}}}
+{"text": "MultiPerturbationStability metric initialized successfully\n", "record": {"elapsed": {"repr": "0:00:18.157003", "seconds": 18.157003}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 96, "message": "MultiPerturbationStability metric initialized successfully", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.373594+05:30", "timestamp": 1762204252.373594}}}
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+{"text": "✓ Detection Orchestrator initialized\n", "record": {"elapsed": {"repr": "0:00:18.157174", "seconds": 18.157174}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/office/text_auth/text_auth_app.py"}, "function": "startup_event", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 383, "message": "✓ Detection Orchestrator initialized", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.373765+05:30", "timestamp": 1762204252.373765}}}
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+{"text": "ModelAttributor initialized with domain-aware calibration\n", "record": {"elapsed": {"repr": "0:00:18.157317", "seconds": 18.157317}, "exception": null, "extra": {}, "file": {"name": "attribution.py", "path": "/Users/itobuz/projects/office/text_auth/detector/attribution.py"}, "function": "__init__", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 403, "message": "ModelAttributor initialized with domain-aware calibration", "module": "attribution", "name": "detector.attribution", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.373908+05:30", "timestamp": 1762204252.373908}}}
+{"text": "Model attribution system initialized with metric ensemble\n", "record": {"elapsed": {"repr": "0:00:18.157385", "seconds": 18.157385}, "exception": null, "extra": {}, "file": {"name": "attribution.py", "path": "/Users/itobuz/projects/office/text_auth/detector/attribution.py"}, "function": "initialize", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 412, "message": "Model attribution system initialized with metric ensemble", "module": "attribution", "name": "detector.attribution", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:40:52.373976+05:30", "timestamp": 1762204252.373976}}}
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+{"text": "[analysis_1762204265013] Analyzing text (7084 chars)\n", "record": {"elapsed": {"repr": "0:00:30.796571", "seconds": 30.796571}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/office/text_auth/text_auth_app.py"}, "function": "analyze_text", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 729, "message": "[analysis_1762204265013] Analyzing text (7084 chars)", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:05.013162+05:30", "timestamp": 1762204265.013162}}}
+{"text": "Step 1: Preprocessing text...\n", "record": {"elapsed": {"repr": "0:00:30.797171", "seconds": 30.797171}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 259, "message": "Step 1: Preprocessing text...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:05.013762+05:30", "timestamp": 1762204265.013762}}}
+{"text": "Step 2: Detecting language...\n", "record": {"elapsed": {"repr": "0:00:30.815050", "seconds": 30.81505}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 271, "message": "Step 2: Detecting language...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:05.031641+05:30", "timestamp": 1762204265.031641}}}
+{"text": "Split text into 16 chunks for language detection\n", "record": {"elapsed": {"repr": "0:00:30.826827", "seconds": 30.826827}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/office/text_auth/processors/language_detector.py"}, "function": "_detect_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 307, "message": "Split text into 16 chunks for language detection", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:05.043418+05:30", "timestamp": 1762204265.043418}}}
+{"text": "Detected language: en (confidence: 0.94, method: xlm-roberta-model)\n", "record": {"elapsed": {"repr": "0:00:31.632558", "seconds": 31.632558}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/office/text_auth/processors/language_detector.py"}, "function": "detect", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 291, "message": "Detected language: en (confidence: 0.94, method: xlm-roberta-model)", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:05.849149+05:30", "timestamp": 1762204265.849149}}}
+{"text": "Step 3: Classifying domain...\n", "record": {"elapsed": {"repr": "0:00:31.632810", "seconds": 31.63281}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 290, "message": "Step 3: Classifying domain...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:05.849401+05:30", "timestamp": 1762204265.849401}}}
+{"text": "Primary model classified domain: general (confidence: 0.093)\n", "record": {"elapsed": {"repr": "0:00:34.005318", "seconds": 34.005318}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "_classify_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Primary model classified domain: general (confidence: 0.093)", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:08.221909+05:30", "timestamp": 1762204268.221909}}}
+{"text": "Primary classifier low confidence, trying fallback model...\n", "record": {"elapsed": {"repr": "0:00:34.005681", "seconds": 34.005681}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "classify", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 118, "message": "Primary classifier low confidence, trying fallback model...", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:08.222272+05:30", "timestamp": 1762204268.222272}}}
+{"text": "Fallback model classified domain: medical (confidence: 0.063)\n", "record": {"elapsed": {"repr": "0:00:36.809233", "seconds": 36.809233}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "_classify_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Fallback model classified domain: medical (confidence: 0.063)", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:11.025824+05:30", "timestamp": 1762204271.025824}}}
+{"text": "Detected domain: general (confidence: 0.09)\n", "record": {"elapsed": {"repr": "0:00:36.809472", "seconds": 36.809472}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 318, "message": "Detected domain: general (confidence: 0.09)", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:11.026063+05:30", "timestamp": 1762204271.026063}}}
+{"text": "Step 4: Executing detection metrics calculations...\n", "record": {"elapsed": {"repr": "0:00:36.809574", "seconds": 36.809574}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 321, "message": "Step 4: Executing detection metrics calculations...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:11.026165+05:30", "timestamp": 1762204271.026165}}}
+{"text": "Valid perturbations: 10/10\n", "record": {"elapsed": {"repr": "0:00:43.955369", "seconds": 43.955369}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "_calculate_stability_features", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 297, "message": "Valid perturbations: 10/10", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:18.171960+05:30", "timestamp": 1762204278.17196}}}
+{"text": "Stability: 0.227, Curvature: 0.186\n", "record": {"elapsed": {"repr": "0:00:43.955711", "seconds": 43.955711}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "_calculate_stability_features", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 312, "message": "Stability: 0.227, Curvature: 0.186", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:18.172302+05:30", "timestamp": 1762204278.172302}}}
+{"text": "Executed 6 metrics successfully\n", "record": {"elapsed": {"repr": "0:00:46.034007", "seconds": 46.034007}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 361, "message": "Executed 6 metrics successfully", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.250598+05:30", "timestamp": 1762204280.250598}}}
+{"text": "Step 5: Aggregating results with ensemble...\n", "record": {"elapsed": {"repr": "0:00:46.034219", "seconds": 46.034219}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 364, "message": "Step 5: Aggregating results with ensemble...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.250810+05:30", "timestamp": 1762204280.25081}}}
+{"text": "Analysis complete: Human-Written (AI probability: 32.8%, confidence: 0.70) in 15.24s\n", "record": {"elapsed": {"repr": "0:00:46.034429", "seconds": 46.034429}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 393, "message": "Analysis complete: Human-Written (AI probability: 32.8%, confidence: 0.70) in 15.24s", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.251020+05:30", "timestamp": 1762204280.25102}}}
+{"text": "[analysis_1762204265013] Running attribution...\n", "record": {"elapsed": {"repr": "0:00:46.034763", "seconds": 46.034763}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/office/text_auth/text_auth_app.py"}, "function": "analyze_text", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 745, "message": "[analysis_1762204265013] Running attribution...", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.251354+05:30", "timestamp": 1762204280.251354}}}
+{"text": "[analysis_1762204265013] Generating highlights...\n", "record": {"elapsed": {"repr": "0:00:46.036959", "seconds": 46.036959}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/office/text_auth/text_auth_app.py"}, "function": "analyze_text", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 763, "message": "[analysis_1762204265013] Generating highlights...", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.253550+05:30", "timestamp": 1762204280.25355}}}
+{"text": "Detection completed: analysis_1762204265013 -> Human-Written\n", "record": {"elapsed": {"repr": "0:00:46.050481", "seconds": 46.050481}, "exception": null, "extra": {"log_type": "application", "extra": {"analysis_id": "analysis_1762204265013", "text_length": 7084, "verdict": "Human-Written", "confidence": 0.7012, "domain": "general", "processing_time_seconds": 15.2539, "timestamp": "2025-11-04T02:41:20.267055", "enable_attribution": true, "enable_highlighting": true}}, "file": {"name": "logger.py", "path": "/Users/itobuz/projects/office/text_auth/utils/logger.py"}, "function": "log_detection_event", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 419, "message": "Detection completed: analysis_1762204265013 -> Human-Written", "module": "logger", "name": "utils.logger", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.267072+05:30", "timestamp": 1762204280.267072}}}
+{"text": "API Request: POST /api/analyze -> 200\n", "record": {"elapsed": {"repr": "0:00:46.051774", "seconds": 46.051774}, "exception": null, "extra": {"log_type": "application", "extra": {"http_method": "POST", "path": "/api/analyze", "status_code": 200, "duration_seconds": 15.2629, "user": null, "ip_address": "127.0.0.1", "timestamp": "2025-11-04T02:41:20.268352"}}, "file": {"name": "logger.py", "path": "/Users/itobuz/projects/office/text_auth/utils/logger.py"}, "function": "log_api_request", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 374, "message": "API Request: POST /api/analyze -> 200", "module": "logger", "name": "utils.logger", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.268365+05:30", "timestamp": 1762204280.268365}}}
+{"text": "127.0.0.1:59453 - \"POST /api/analyze HTTP/1.1\" 200\n", "record": {"elapsed": {"repr": "0:00:46.052001", "seconds": 46.052001}, "exception": null, "extra": {}, "file": {"name": "h11_impl.py", "path": "/Users/itobuz/anaconda3/lib/python3.10/site-packages/uvicorn/protocols/http/h11_impl.py"}, "function": "send", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 473, "message": "127.0.0.1:59453 - \"POST /api/analyze HTTP/1.1\" 200", "module": "h11_impl", "name": "uvicorn.protocols.http.h11_impl", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:20.268592+05:30", "timestamp": 1762204280.268592}}}
+{"text": "Generating report for analysis_1762204265013\n", "record": {"elapsed": {"repr": "0:00:52.283794", "seconds": 52.283794}, "exception": null, "extra": {}, "file": {"name": "text_auth_app.py", "path": "/Users/itobuz/projects/office/text_auth/text_auth_app.py"}, "function": "generate_report", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 1089, "message": "Generating report for analysis_1762204265013", "module": "text_auth_app", "name": "text_auth_app", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:26.500385+05:30", "timestamp": 1762204286.500385}}}
+{"text": "Step 1: Preprocessing text...\n", "record": {"elapsed": {"repr": "0:00:52.284935", "seconds": 52.284935}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 259, "message": "Step 1: Preprocessing text...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:26.501526+05:30", "timestamp": 1762204286.501526}}}
+{"text": "Step 2: Detecting language...\n", "record": {"elapsed": {"repr": "0:00:52.302325", "seconds": 52.302325}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 271, "message": "Step 2: Detecting language...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:26.518916+05:30", "timestamp": 1762204286.518916}}}
+{"text": "Split text into 16 chunks for language detection\n", "record": {"elapsed": {"repr": "0:00:52.318069", "seconds": 52.318069}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/office/text_auth/processors/language_detector.py"}, "function": "_detect_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 307, "message": "Split text into 16 chunks for language detection", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:26.534660+05:30", "timestamp": 1762204286.53466}}}
+{"text": "Detected language: en (confidence: 0.94, method: xlm-roberta-model)\n", "record": {"elapsed": {"repr": "0:00:53.057609", "seconds": 53.057609}, "exception": null, "extra": {}, "file": {"name": "language_detector.py", "path": "/Users/itobuz/projects/office/text_auth/processors/language_detector.py"}, "function": "detect", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 291, "message": "Detected language: en (confidence: 0.94, method: xlm-roberta-model)", "module": "language_detector", "name": "processors.language_detector", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:27.274200+05:30", "timestamp": 1762204287.2742}}}
+{"text": "Step 3: Classifying domain...\n", "record": {"elapsed": {"repr": "0:00:53.057969", "seconds": 53.057969}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 290, "message": "Step 3: Classifying domain...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:27.274560+05:30", "timestamp": 1762204287.27456}}}
+{"text": "Primary model classified domain: general (confidence: 0.093)\n", "record": {"elapsed": {"repr": "0:00:55.176692", "seconds": 55.176692}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "_classify_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Primary model classified domain: general (confidence: 0.093)", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:29.393283+05:30", "timestamp": 1762204289.393283}}}
+{"text": "Primary classifier low confidence, trying fallback model...\n", "record": {"elapsed": {"repr": "0:00:55.177097", "seconds": 55.177097}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "classify", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 118, "message": "Primary classifier low confidence, trying fallback model...", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:29.393688+05:30", "timestamp": 1762204289.393688}}}
+{"text": "Fallback model classified domain: medical (confidence: 0.063)\n", "record": {"elapsed": {"repr": "0:00:57.615243", "seconds": 57.615243}, "exception": null, "extra": {}, "file": {"name": "domain_classifier.py", "path": "/Users/itobuz/projects/office/text_auth/processors/domain_classifier.py"}, "function": "_classify_with_model", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 221, "message": "Fallback model classified domain: medical (confidence: 0.063)", "module": "domain_classifier", "name": "processors.domain_classifier", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:31.831834+05:30", "timestamp": 1762204291.831834}}}
+{"text": "Detected domain: general (confidence: 0.09)\n", "record": {"elapsed": {"repr": "0:00:57.615484", "seconds": 57.615484}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 318, "message": "Detected domain: general (confidence: 0.09)", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:31.832075+05:30", "timestamp": 1762204291.832075}}}
+{"text": "Step 4: Executing detection metrics calculations...\n", "record": {"elapsed": {"repr": "0:00:57.615590", "seconds": 57.61559}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 321, "message": "Step 4: Executing detection metrics calculations...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:31.832181+05:30", "timestamp": 1762204291.832181}}}
+{"text": "Valid perturbations: 10/10\n", "record": {"elapsed": {"repr": "0:01:03.429344", "seconds": 63.429344}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "_calculate_stability_features", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 297, "message": "Valid perturbations: 10/10", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:37.645935+05:30", "timestamp": 1762204297.645935}}}
+{"text": "Stability: 0.223, Curvature: 0.258\n", "record": {"elapsed": {"repr": "0:01:03.429670", "seconds": 63.42967}, "exception": null, "extra": {}, "file": {"name": "multi_perturbation_stability.py", "path": "/Users/itobuz/projects/office/text_auth/metrics/multi_perturbation_stability.py"}, "function": "_calculate_stability_features", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 312, "message": "Stability: 0.223, Curvature: 0.258", "module": "multi_perturbation_stability", "name": "metrics.multi_perturbation_stability", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:37.646261+05:30", "timestamp": 1762204297.646261}}}
+{"text": "Executed 6 metrics successfully\n", "record": {"elapsed": {"repr": "0:01:04.822565", "seconds": 64.822565}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 361, "message": "Executed 6 metrics successfully", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:39.039156+05:30", "timestamp": 1762204299.039156}}}
+{"text": "Step 5: Aggregating results with ensemble...\n", "record": {"elapsed": {"repr": "0:01:04.822780", "seconds": 64.82278}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 364, "message": "Step 5: Aggregating results with ensemble...", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:39.039371+05:30", "timestamp": 1762204299.039371}}}
+{"text": "Analysis complete: Human-Written (AI probability: 32.8%, confidence: 0.70) in 12.54s\n", "record": {"elapsed": {"repr": "0:01:04.822990", "seconds": 64.82299}, "exception": null, "extra": {}, "file": {"name": "orchestrator.py", "path": "/Users/itobuz/projects/office/text_auth/detector/orchestrator.py"}, "function": "analyze", "level": {"icon": "✅", "name": "SUCCESS", "no": 25}, "line": 393, "message": "Analysis complete: Human-Written (AI probability: 32.8%, confidence: 0.70) in 12.54s", "module": "orchestrator", "name": "detector.orchestrator", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:39.039581+05:30", "timestamp": 1762204299.039581}}}
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+{"text": "API Request: GET /api/report/download/analysis_1762204265013_20251104_024139.pdf -> 200\n", "record": {"elapsed": {"repr": "0:01:04.900807", "seconds": 64.900807}, "exception": null, "extra": {"log_type": "application", "extra": {"http_method": "GET", "path": "/api/report/download/analysis_1762204265013_20251104_024139.pdf", "status_code": 200, "duration_seconds": 0.0006, "user": null, "ip_address": "127.0.0.1", "timestamp": "2025-11-04T02:41:39.117391"}}, "file": {"name": "logger.py", "path": "/Users/itobuz/projects/office/text_auth/utils/logger.py"}, "function": "log_api_request", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 374, "message": "API Request: GET /api/report/download/analysis_1762204265013_20251104_024139.pdf -> 200", "module": "logger", "name": "utils.logger", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:39.117398+05:30", "timestamp": 1762204299.117398}}}
+{"text": "127.0.0.1:59459 - \"GET /api/report/download/analysis_1762204265013_20251104_024139.pdf HTTP/1.1\" 200\n", "record": {"elapsed": {"repr": "0:01:04.900946", "seconds": 64.900946}, "exception": null, "extra": {}, "file": {"name": "h11_impl.py", "path": "/Users/itobuz/anaconda3/lib/python3.10/site-packages/uvicorn/protocols/http/h11_impl.py"}, "function": "send", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 473, "message": "127.0.0.1:59459 - \"GET /api/report/download/analysis_1762204265013_20251104_024139.pdf HTTP/1.1\" 200", "module": "h11_impl", "name": "uvicorn.protocols.http.h11_impl", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:41:39.117537+05:30", "timestamp": 1762204299.117537}}}
+{"text": "Shutting down\n", "record": {"elapsed": {"repr": "0:01:33.413597", "seconds": 93.413597}, "exception": null, "extra": {}, "file": {"name": "server.py", "path": "/Users/itobuz/anaconda3/lib/python3.10/site-packages/uvicorn/server.py"}, "function": "shutdown", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 263, "message": "Shutting down", "module": "server", "name": "uvicorn.server", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:42:07.630188+05:30", "timestamp": 1762204327.630188}}}
+{"text": "Waiting for application shutdown.\n", "record": {"elapsed": {"repr": "0:01:33.515638", "seconds": 93.515638}, "exception": null, "extra": {}, "file": {"name": "on.py", "path": "/Users/itobuz/anaconda3/lib/python3.10/site-packages/uvicorn/lifespan/on.py"}, "function": "shutdown", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 67, "message": "Waiting for application shutdown.", "module": "on", "name": "uvicorn.lifespan.on", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:42:07.732229+05:30", "timestamp": 1762204327.732229}}}
+{"text": "Logging system cleanup completed\n", "record": {"elapsed": {"repr": "0:01:33.516366", "seconds": 93.516366}, "exception": null, "extra": {}, "file": {"name": "logger.py", "path": "/Users/itobuz/projects/office/text_auth/utils/logger.py"}, "function": "cleanup", "level": {"icon": "ℹ️", "name": "INFO", "no": 20}, "line": 522, "message": "Logging system cleanup completed", "module": "logger", "name": "utils.logger", "process": {"id": 3009, "name": "SpawnProcess-1"}, "thread": {"id": 8707055360, "name": "MainThread"}, "time": {"repr": "2025-11-04 02:42:07.732957+05:30", "timestamp": 1762204327.732957}}}

metrics/multi_perturbation_stability.py

@@ -59,6 +59,7 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                 self.gpt_model, self.gpt_tokenizer = gpt_result
                 # Move model to appropriate device
                 self.gpt_model.to(self.device)
+                logger.success("✓ GPT-2 model loaded for MultiPerturbationStability")
            
             else:
                 logger.error("Failed to load GPT-2 model for MultiPerturbationStability")
@@ -76,9 +77,20 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                 if (self.mask_tokenizer.pad_token is None):
                     self.mask_tokenizer.pad_token = self.mask_tokenizer.eos_token or '[PAD]'
 
+                # Ensure tokenizer has mask token
+                if not hasattr(self.mask_tokenizer, 'mask_token') or self.mask_tokenizer.mask_token is None:
+                    self.mask_tokenizer.mask_token = "<mask>"
+                
+                logger.success("✓ DistilRoBERTa model loaded for MultiPerturbationStability")
+
             else:
                 logger.warning("Failed to load mask model, using GPT-2 only")
             
+            # Verify model loading
+            if not self._verify_model_loading():
+                logger.error("Model verification failed")
+                return False
+            
             self.is_initialized = True
             
             logger.success("MultiPerturbationStability metric initialized successfully")
@@ -89,12 +101,51 @@ class MultiPerturbationStabilityMetric(BaseMetric):
             return False
     
 
+    def _verify_model_loading(self) -> bool:
+        """
+        Verify that models are properly loaded and working
+        """
+        try:
+            test_text = "This is a test sentence for model verification."
+            
+            # Test GPT-2 model
+            if self.gpt_model and self.gpt_tokenizer:
+                gpt_likelihood = self._calculate_likelihood(text = test_text)
+                logger.info(f"GPT-2 test - Likelihood: {gpt_likelihood:.4f}")
+            
+            else:
+                logger.error("GPT-2 model not loaded")
+                return False
+            
+            # Test DistilRoBERTa model if available
+            if self.mask_model and self.mask_tokenizer:
+                # Test mask token
+                if hasattr(self.mask_tokenizer, 'mask_token') and self.mask_tokenizer.mask_token:
+                    logger.info(f"DistilRoBERTa mask token: '{self.mask_tokenizer.mask_token}'")
+                    
+                    # Test basic tokenization
+                    inputs = self.mask_tokenizer(test_text, return_tensors = "pt")
+                    logger.info(f"DistilRoBERTa tokenization test - Input shape: {inputs['input_ids'].shape}")
+                
+                else:
+                    logger.warning("DistilRoBERTa mask token not available")
+            
+            else:
+                logger.warning("DistilRoBERTa model not loaded")
+            
+            return True
+            
+        except Exception as e:
+            logger.error(f"Model verification failed: {e}")
+            return False
+    
+
     def compute(self, text: str, **kwargs) -> MetricResult:
         """
         Compute MultiPerturbationStability analysis with FULL DOMAIN THRESHOLD INTEGRATION
         """
         try:
-            if ((not text) or (len(text.strip()) < 100)):
+            if ((not text) or (len(text.strip()) < 50)):
                 return MetricResult(metric_name       = self.name,
                                     ai_probability    = 0.5,
                                     human_probability = 0.5,
@@ -121,13 +172,16 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                                    )
             
             # Calculate MultiPerturbationStability features
-            features                        = self._calculate_stability_features(text)
+            features                        = self._calculate_stability_features(text = text)
             
             # Calculate raw MultiPerturbationStability score (0-1 scale)
-            raw_stability_score, confidence = self._analyze_stability_patterns(features)
+            raw_stability_score, confidence = self._analyze_stability_patterns(features = features)
             
             # Apply domain-specific thresholds to convert raw score to probabilities
-            ai_prob, human_prob, mixed_prob = self._apply_domain_thresholds(raw_stability_score, multi_perturbation_stability_thresholds, features)
+            ai_prob, human_prob, mixed_prob = self._apply_domain_thresholds(raw_score  = raw_stability_score, 
+                                                                            thresholds = multi_perturbation_stability_thresholds, 
+                                                                            features   = features,
+                                                                           )
             
             # Apply confidence multiplier from domain thresholds
             confidence                     *= multi_perturbation_stability_thresholds.confidence_multiplier
@@ -211,54 +265,75 @@ class MultiPerturbationStabilityMetric(BaseMetric):
 
     def _calculate_stability_features(self, text: str) -> Dict[str, Any]:
         """
-        Calculate comprehensive MultiPerturbationStability features
+        Calculate comprehensive MultiPerturbationStability features with diagnostic logging
         """
         if not self.gpt_model or not self.gpt_tokenizer:
             return self._get_default_features()
         
         try:
             # Preprocess text for better analysis
-            processed_text        = self._preprocess_text_for_analysis(text)
+            processed_text        = self._preprocess_text_for_analysis(text = text)
             
             # Calculate original text likelihood
-            original_likelihood   = self._calculate_likelihood(processed_text)
+            original_likelihood   = self._calculate_likelihood(text = processed_text)
+            logger.debug(f"Original likelihood: {original_likelihood:.4f}")
             
             # Generate perturbations and calculate perturbed likelihoods
-            perturbations         = self._generate_perturbations(processed_text, num_perturbations = 5)
+            perturbations         = self._generate_perturbations(text              = processed_text,
+                                                                 num_perturbations = 10,
+                                                                )
+            logger.debug(f"Generated {len(perturbations)} perturbations")
+
             perturbed_likelihoods = list()
             
-            for perturbed_text in perturbations:
+            for idx, perturbed_text in enumerate(perturbations):
                 if (perturbed_text and (perturbed_text != processed_text)):
-                    likelihood = self._calculate_likelihood(perturbed_text)
+                    likelihood = self._calculate_likelihood(text = perturbed_text)
                     
                     if (likelihood > 0):
                         perturbed_likelihoods.append(likelihood)
+                        logger.debug(f"Perturbation {idx}: likelihood={likelihood:.4f}")
+            
+            logger.info(f"Valid perturbations: {len(perturbed_likelihoods)}/{len(perturbations)}")
             
             # Calculate stability metrics
             if perturbed_likelihoods:
-                stability_score          = self._calculate_stability_score(original_likelihood, perturbed_likelihoods)
-                curvature_score          = self._calculate_curvature_score(original_likelihood, perturbed_likelihoods)
-                variance_score           = np.var(perturbed_likelihoods) if len(perturbed_likelihoods) > 1 else 0.0
+                stability_score          = self._calculate_stability_score(original_likelihood   = original_likelihood, 
+                                                                           perturbed_likelihoods = perturbed_likelihoods,
+                                                                          )
+
+                curvature_score          = self._calculate_curvature_score(original_likelihood   = original_likelihood, 
+                                                                           perturbed_likelihoods = perturbed_likelihoods,
+                                                                          )
+
+                variance_score           = np.var(perturbed_likelihoods) if (len(perturbed_likelihoods) > 1) else 0.0
                 avg_perturbed_likelihood = np.mean(perturbed_likelihoods)
+                
+                logger.info(f"Stability: {stability_score:.3f}, Curvature: {curvature_score:.3f}")
             
             else:
-                stability_score          = 0.5
-                curvature_score          = 0.5
-                variance_score           = 0.1
-                avg_perturbed_likelihood = original_likelihood
+                # Use meaningful defaults when perturbations fail
+                stability_score          = 0.3  # Assume more human-like when no perturbations work
+                curvature_score          = 0.3
+                variance_score           = 0.05
+                avg_perturbed_likelihood = original_likelihood * 0.9  # Assume some drop
+                logger.warning("No valid perturbations, using fallback values")
             
             # Calculate likelihood ratio
-            likelihood_ratio            = original_likelihood / avg_perturbed_likelihood if avg_perturbed_likelihood > 0 else 1.0
+            likelihood_ratio             = original_likelihood / avg_perturbed_likelihood if avg_perturbed_likelihood > 0 else 1.0
             
             # Chunk-based analysis for whole-text understanding
-            chunk_stabilities           = self._calculate_chunk_stability(processed_text, chunk_size=150)
-            stability_variance          = np.var(chunk_stabilities) if chunk_stabilities else 0.0
-            avg_chunk_stability         = np.mean(chunk_stabilities) if chunk_stabilities else stability_score
+            chunk_stabilities            = self._calculate_chunk_stability(text       = processed_text, 
+                                                                           chunk_size = 150,
+                                                                          )
+
+            stability_variance           = np.var(chunk_stabilities) if chunk_stabilities else 0.1 
+            avg_chunk_stability          = np.mean(chunk_stabilities) if chunk_stabilities else stability_score
             
-            # Normalize scores to 0-1 range
-            normalized_stability        = min(1.0, max(0.0, stability_score))
-            normalized_curvature        = min(1.0, max(0.0, curvature_score))
-            normalized_likelihood_ratio = min(2.0, likelihood_ratio) / 2.0    # Normalize to 0-1
+            # Better normalization to prevent extreme values
+            normalized_stability         = min(1.0, max(0.0, stability_score))
+            normalized_curvature         = min(1.0, max(0.0, curvature_score))
+            normalized_likelihood_ratio  = min(3.0, max(0.33, likelihood_ratio)) / 3.0
             
             return {"original_likelihood"         : round(original_likelihood, 4),
                     "avg_perturbed_likelihood"    : round(avg_perturbed_likelihood, 4),
@@ -281,59 +356,87 @@ class MultiPerturbationStabilityMetric(BaseMetric):
 
     def _calculate_likelihood(self, text: str) -> float:
         """
-        Calculate log-likelihood of text using GPT-2 with robust error handling
+        Calculate proper log-likelihood using token probabilities
+        Inspired by DetectGPT's likelihood calculation approach
         """
         try:
             # Check text length before tokenization
             if (len(text.strip()) < 10):
-                return 0.0
+                return 2.0  # Return reasonable baseline
+
+            if not self.gpt_model or not self.gpt_tokenizer:
+                logger.warning("GPT model not available for likelihood calculation")
+                return 2.0
 
-            # Configure tokenizer for proper padding
-            tokenizer      = self._configure_tokenizer_padding(self.gpt_tokenizer)
+            # Ensure tokenizer has pad token
+            if self.gpt_tokenizer.pad_token is None:
+                self.gpt_tokenizer.pad_token = self.gpt_tokenizer.eos_token
             
             # Tokenize text with proper settings
-            encodings      = tokenizer(text, 
-                                       return_tensors = 'pt', 
-                                       truncation     = True,
-                                       max_length     = 512,
-                                       padding        = True,
-                                       return_attention_mask = True,
-                                      )
+            encodings      = self.gpt_tokenizer(text, 
+                                                return_tensors        = 'pt', 
+                                                truncation            = True,
+                                                max_length            = 256,
+                                                padding               = True,
+                                                return_attention_mask = True,
+                                               )
 
             input_ids      = encodings.input_ids.to(self.device)
             attention_mask = encodings.attention_mask.to(self.device)
             
             # Minimum tokens for meaningful analysis
-            if ((input_ids.numel() == 0) or (input_ids.size(1) < 5)):
-                return 0.0
+            if ((input_ids.numel() == 0) or (input_ids.size(1) < 3)):
+                return 2.0
             
-            # Calculate negative log likelihood
+            # Calculate proper log-likelihood using token probabilities
             with torch.no_grad():
-                outputs = self.gpt_model(input_ids, 
-                                         attention_mask = attention_mask, 
-                                         labels         = input_ids,
-                                        )
+                outputs        = self.gpt_model(input_ids, 
+                                                attention_mask = attention_mask,
+                                               )
+                
+                logits         = outputs.logits
+                
+                # Calculate log probabilities for each token
+                log_probs      = torch.nn.functional.log_softmax(logits, dim = -1)
+                
+                # Get the log probability of each actual token
+                log_likelihood = 0.0
+                token_count    = 0
+                
+                for i in range(input_ids.size(1) - 1):
+                    # Only consider non-padding tokens
+                    if (attention_mask[0, i] == 1):       
+                        token_id        = input_ids[0, i + 1]  # Next token prediction
+                        log_prob        = log_probs[0, i, token_id]
+                        log_likelihood += log_prob.item()
+                        token_count    += 1
+                
+                # Normalize by token count to get average log likelihood per token
+                if (token_count > 0):
+                    avg_log_likelihood = log_likelihood / token_count
 
-                loss    = outputs.loss
+                else:
+                    avg_log_likelihood = 0.0
             
-            # Convert to positive log likelihood (higher = more likely)
-            log_likelihood = -loss.item()
-
-            # Reasonable range check (typical values are between -10 and 10)
-            if (abs(log_likelihood) > 100):
-                logger.warning(f"Extreme likelihood value detected: {log_likelihood}")
-                return 0.0
+            # Convert to positive scale and normalize
+            # Typical GPT-2 log probabilities range from ~-10 to ~-2
+            # Higher normalized value = more likely text
+            normalized_likelihood = max(0.5, min(10.0, -avg_log_likelihood))
             
-            return log_likelihood
+            return normalized_likelihood
             
         except Exception as e:
             logger.warning(f"Likelihood calculation failed: {repr(e)}")
-            return 0.0
+            return 2.0  # Return reasonable baseline on error
     
 
     def _generate_perturbations(self, text: str, num_perturbations: int = 5) -> List[str]:
         """
-        Generate perturbed versions of the text with robust error handling
+        Generate perturbed versions of the text using multiple techniques:
+        1. Word deletion (simple but effective)
+        2. Word swapping (preserve meaning)
+        3. DistilRoBERTa masked prediction (DetectGPT-inspired, using lighter model than T5)
+        4. Synonym replacement (fallback)
         """
         perturbations = list()
         
@@ -383,33 +486,37 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                         logger.debug(f"Word swapping perturbation failed: {e}")
                         continue
             
-            # Method 3: RoBERTa-specific masked word replacement
+            # Method 3: DistilRoBERTa-based masked word replacement (DetectGPT-inspired)
             if (self.mask_model and self.mask_tokenizer and (len(words) > 4) and len(perturbations) < num_perturbations):
                 
                 try:
-                    roberta_perturbations = self._generate_roberta_masked_perturbations(processed_text, 
-                                                                                        words, 
-                                                                                        num_perturbations - len(perturbations))
+                    roberta_perturbations = self._generate_roberta_masked_perturbations(text              = processed_text, 
+                                                                                        words             = words, 
+                                                                                        max_perturbations = num_perturbations - len(perturbations),
+                                                                                       )
                     perturbations.extend(roberta_perturbations)
                     
                 except Exception as e:
-                    logger.warning(f"RoBERTa masked perturbation failed: {repr(e)}")
+                    logger.warning(f"DistilRoBERTa masked perturbation failed: {repr(e)}")
             
             # Method 4: Synonym replacement as fallback
             if (len(perturbations) < num_perturbations):
                 try:
-                    synonym_perturbations = self._generate_synonym_perturbations(processed_text, 
-                                                                                 words, 
-                                                                                 num_perturbations - len(perturbations))
+                    synonym_perturbations = self._generate_synonym_perturbations(text              = processed_text, 
+                                                                                 words             = words, 
+                                                                                 max_perturbations = num_perturbations - len(perturbations),
+                                                                                )
                     perturbations.extend(synonym_perturbations)
                     
                 except Exception as e:
-                    logger.debug(f"Synonym replacement failed: {e}")
+                    logger.debug(f"Synonym replacement failed: {repr(e)}")
             
             # Ensure we have at least some perturbations
             if not perturbations:
                 # Fallback: create simple variations
-                fallback_perturbations = self._generate_fallback_perturbations(processed_text, words)
+                fallback_perturbations = self._generate_fallback_perturbations(text  = processed_text, 
+                                                                               words = words,
+                                                                              )
                 perturbations.extend(fallback_perturbations)
             
             # Remove duplicates and ensure we don't exceed requested number
@@ -423,19 +530,23 @@ class MultiPerturbationStabilityMetric(BaseMetric):
             
         except Exception as e:
             logger.warning(f"Perturbation generation failed: {repr(e)}")
-            # Return at least the original text as fallback
-            return [text]
+            return [text]  # Return at least the original text as fallback
     
 
     def _generate_roberta_masked_perturbations(self, text: str, words: List[str], max_perturbations: int) -> List[str]:
         """
-        Generate perturbations using RoBERTa mask filling
+        Generate perturbations using DistilRoBERTa mask filling
+        This is inspired by DetectGPT but uses a lighter model (DistilRoBERTa instead of T5)
         """
         perturbations = list()
         
         try:
-            # RoBERTa uses <mask> token
-            roberta_mask_token  = "<mask>"
+            # Use the proper DistilRoBERTa mask token from tokenizer
+            if hasattr(self.mask_tokenizer, 'mask_token') and self.mask_tokenizer.mask_token:
+                roberta_mask_token = self.mask_tokenizer.mask_token
+            
+            else:
+                roberta_mask_token = "<mask>"  # Fallback
             
             # Select words to mask (avoid very short words and punctuation)
             candidate_positions = [i for i, word in enumerate(words) if (len(word) > 3) and word.isalpha() and word.lower() not in ['the', 'and', 'but', 'for', 'with']]
@@ -448,7 +559,7 @@ class MultiPerturbationStabilityMetric(BaseMetric):
             
             # Try multiple mask positions
             attempts          = min(max_perturbations * 2, len(candidate_positions))
-            positions_to_try  = np.random.choice(candidate_positions, min(attempts, len(candidate_positions)), replace=False)
+            positions_to_try  = np.random.choice(candidate_positions, min(attempts, len(candidate_positions)), replace = False)
             
             for pos in positions_to_try:
                 if (len(perturbations) >= max_perturbations):
@@ -461,15 +572,15 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                     masked_words[pos] = roberta_mask_token
                     masked_text       = ' '.join(masked_words)
                     
-                    # RoBERTa works better with proper sentence structure
+                    # DistilRoBERTa works better with proper sentence structure
                     if not masked_text.endswith(('.', '!', '?')):
                         masked_text += '.'
                     
-                    # Tokenize with RoBERTa-specific settings
+                    # Tokenize with DistilRoBERTa-specific settings
                     inputs = self.mask_tokenizer(masked_text,
                                                  return_tensors = "pt",
                                                  truncation     = True,
-                                                 max_length     = min(128, self.mask_tokenizer.model_max_length),  # Conservative length
+                                                 max_length     = min(128, self.mask_tokenizer.model_max_length),
                                                  padding        = True,
                                                 )
                     
@@ -508,15 +619,14 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                             
                             if (self._is_valid_perturbation(new_text, text)):
                                 perturbations.append(new_text)
-                                # Use first valid prediction
-                                break  
+                                break  # Use first valid prediction
                     
                 except Exception as e:
-                    logger.debug(f"RoBERTa mask filling failed for position {pos}: {e}")
+                    logger.debug(f"DistilRoBERTa mask filling failed for position {pos}: {e}")
                     continue
                     
         except Exception as e:
-            logger.warning(f"RoBERTa masked perturbations failed: {e}")
+            logger.warning(f"DistilRoBERTa masked perturbations failed: {e}")
         
         return perturbations
     
@@ -559,7 +669,7 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                         perturbations.append(new_text)
                         
         except Exception as e:
-            logger.debug(f"Synonym replacement failed: {e}")
+            logger.debug(f"Synonym replacement failed: {repr(e)}")
         
         return perturbations
     
@@ -585,41 +695,72 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                 perturbations.append(text.capitalize())
                 
         except Exception as e:
-            logger.debug(f"Fallback perturbation failed: {e}")
+            logger.debug(f"Fallback perturbation failed: {repr(e)}")
         
         return [p for p in perturbations if p and p != text][:3]
     
 
     def _calculate_stability_score(self, original_likelihood: float, perturbed_likelihoods: List[float]) -> float:
         """
-        Calculate text stability score under perturbations : AI text tends to be less stable (larger likelihood drops)
+        Calculate text stability score with improved normalization : AI text typically shows higher stability (larger drops) than human text
         """
         if ((not perturbed_likelihoods) or (original_likelihood <= 0)):
-            return 0.5
+            # Assume more human-like when no data
+            return 0.3  
         
-        # Calculate average likelihood drop
-        likelihood_drops = [(original_likelihood - pl) / original_likelihood for pl in perturbed_likelihoods]
-        avg_drop         = np.mean(likelihood_drops) if likelihood_drops else 0.0
+        # Calculate relative likelihood drops
+        relative_drops = list()
         
-        # Higher drop = less stable = more AI-like : Normalize to 0-1 scale (assume max drop of 50%)
-        stability_score  = min(1.0, avg_drop / 0.5)
+        for pl in perturbed_likelihoods:
+            if (pl > 0):
+                # Use relative drop to handle scale differences
+                relative_drop = (original_likelihood - pl) / original_likelihood
+                
+                # Clamp to [0, 1]
+                relative_drops.append(max(0.0, min(1.0, relative_drop))) 
+        
+        if not relative_drops:
+            return 0.3
+        
+        avg_relative_drop = np.mean(relative_drops)
+        
+        # Normalization based on empirical observations : AI text typically shows 20-60% drops, human text shows 10-30% drops
+        if (avg_relative_drop > 0.5):
+            # Strong AI indicator
+            stability_score = 0.9  
 
-        return stability_score
+        elif (avg_relative_drop > 0.3):
+            # 0.6 to 0.9
+            stability_score = 0.6 + (avg_relative_drop - 0.3) * 1.5  
+
+        elif (avg_relative_drop > 0.15):
+            # 0.3 to 0.6
+            stability_score = 0.3 + (avg_relative_drop - 0.15) * 2.0  
+        
+        else:
+            # 0.0 to 0.3
+            stability_score = avg_relative_drop * 2.0  
+
+        return min(1.0, max(0.0, stability_score))
     
 
     def _calculate_curvature_score(self, original_likelihood: float, perturbed_likelihoods: List[float]) -> float:
         """
-        Calculate likelihood curvature score : AI text often has different curvature properties
+        Calculate likelihood curvature score with better scaling : Measures how "curved" the likelihood surface is around the text
         """
         if ((not perturbed_likelihoods) or (original_likelihood <= 0)):
-            return 0.5
+            return 0.3
         
         # Calculate variance of likelihood changes
         likelihood_changes = [abs(original_likelihood - pl) for pl in perturbed_likelihoods]
-        change_variance    = np.var(likelihood_changes) if len(likelihood_changes) > 1 else 0.0
         
-        # Higher variance = more curvature = potentially more AI-like : Normalize based on typical variance ranges
-        curvature_score    = min(1.0, change_variance * 10.0)  # Adjust scaling factor as needed
+        if (len(likelihood_changes) < 2):
+            return 0.3
+            
+        change_variance = np.var(likelihood_changes)
+        
+        # Typical variance for meaningful analysis is around 0.1-0.5 : Adjusted scaling
+        curvature_score = min(1.0, change_variance * 3.0)  
         
         return curvature_score
     
@@ -637,7 +778,7 @@ class MultiPerturbationStabilityMetric(BaseMetric):
             
             if (len(chunk) > 50):
                 try:
-                    chunk_likelihood = self._calculate_likelihood(chunk)
+                    chunk_likelihood = self._calculate_likelihood(text = chunk)
                     
                     if (chunk_likelihood > 0):
                         # Generate a simple perturbation for this chunk
@@ -649,11 +790,12 @@ class MultiPerturbationStabilityMetric(BaseMetric):
                             indices_to_keep      = np.random.choice(len(chunk_words), len(chunk_words) - delete_count, replace=False)
                             perturbed_chunk      = ' '.join([chunk_words[i] for i in sorted(indices_to_keep)])
                             
-                            perturbed_likelihood = self._calculate_likelihood(perturbed_chunk)
+                            perturbed_likelihood = self._calculate_likelihood(text = perturbed_chunk)
 
                             if (perturbed_likelihood > 0):
                                 stability = (chunk_likelihood - perturbed_likelihood) / chunk_likelihood
                                 stabilities.append(min(1.0, max(0.0, stability)))
+
                 except Exception:
                     continue
         
@@ -662,7 +804,7 @@ class MultiPerturbationStabilityMetric(BaseMetric):
 
     def _analyze_stability_patterns(self, features: Dict[str, Any]) -> tuple:
         """
-        Analyze MultiPerturbationStability patterns to determine RAW MultiPerturbationStability score (0-1 scale) : Higher score = more AI-like
+        Analyze MultiPerturbationStability patterns with better feature weighting
         """
         # Check feature validity first
         required_features = ['stability_score', 'curvature_score', 'normalized_likelihood_ratio', 'stability_variance', 'perturbation_variance']
@@ -675,61 +817,76 @@ class MultiPerturbationStabilityMetric(BaseMetric):
 
 
         # Initialize ai_indicator list
-        ai_indicators = list()
+        ai_indicators    = list()
+        
+        # Better weighting based on feature reliability
+        stability_weight = 0.3
+        curvature_weight = 0.25
+        ratio_weight     = 0.25
+        variance_weight  = 0.2
         
         # High stability score suggests AI (larger likelihood drops)
-        if (features['stability_score'] > 0.6):
-            ai_indicators.append(0.8)
-
-        elif (features['stability_score'] > 0.3):
-            ai_indicators.append(0.5)
+        stability = features['stability_score']
+        if (stability > 0.7):
+            ai_indicators.append(0.9 * stability_weight)
+        
+        elif (stability > 0.5):
+            ai_indicators.append(0.7 * stability_weight)
+        
+        elif (stability > 0.3):
+            ai_indicators.append(0.5 * stability_weight)
         
         else:
-            ai_indicators.append(0.2)
+            ai_indicators.append(0.2 * stability_weight)
         
         # High curvature score suggests AI
-        if (features['curvature_score'] > 0.7):
-            ai_indicators.append(0.7)
-
-        elif (features['curvature_score'] > 0.4):
-            ai_indicators.append(0.4)
-
+        curvature = features['curvature_score']
+        if (curvature > 0.7):
+            ai_indicators.append(0.8 * curvature_weight)
+        
+        elif (curvature > 0.5):
+            ai_indicators.append(0.6 * curvature_weight)
+        
+        elif (curvature > 0.3):
+            ai_indicators.append(0.4 * curvature_weight)
+        
         else:
-            ai_indicators.append(0.2)
+            ai_indicators.append(0.2 * curvature_weight)
         
         # High likelihood ratio suggests AI (original much more likely than perturbations)
-        if (features['normalized_likelihood_ratio'] > 0.8):
-            ai_indicators.append(0.9)
+        ratio = features['normalized_likelihood_ratio']
+        if (ratio > 0.8):
+            ai_indicators.append(0.9 * ratio_weight)
+        
+        elif (ratio > 0.6):
+            ai_indicators.append(0.7 * ratio_weight)
+        
+        elif (ratio > 0.4):
+            ai_indicators.append(0.5 * ratio_weight)
         
-        elif (features['normalized_likelihood_ratio'] > 0.6):
-            ai_indicators.append(0.6)
-
         else:
-            ai_indicators.append(0.3)
+            ai_indicators.append(0.3 * ratio_weight)
         
         # Low stability variance suggests AI (consistent across chunks)
-        if (features['stability_variance'] < 0.05):
-            ai_indicators.append(0.7)
-
-        elif (features['stability_variance'] < 0.1):
-            ai_indicators.append(0.4)
-
-        else:
-            ai_indicators.append(0.2)
+        stability_var = features['stability_variance']
+        if (stability_var < 0.05):
+            ai_indicators.append(0.8 * variance_weight)
         
-        # High perturbation variance suggests AI
-        if (features['perturbation_variance'] > 0.1):
-            ai_indicators.append(0.6)
-
-        elif (features['perturbation_variance'] > 0.05):
-            ai_indicators.append(0.4)
+        elif (stability_var < 0.1):
+            ai_indicators.append(0.5 * variance_weight)
 
         else:
-            ai_indicators.append(0.2)
+            ai_indicators.append(0.2 * variance_weight)
         
         # Calculate raw score and confidence
-        raw_score  = np.mean(ai_indicators) if ai_indicators else 0.5
-        confidence = 1.0 - (np.std(ai_indicators) / 0.5) if ai_indicators else 0.5
+        if ai_indicators:
+            raw_score  = sum(ai_indicators)
+            confidence = 0.5 + (0.5 * (1.0 - (np.std([x / (weights := [stability_weight, curvature_weight, ratio_weight, variance_weight])[i] for i, x in enumerate(ai_indicators)]) if len(ai_indicators) > 1 else 0.5)))
+       
+        else:
+            raw_score  = 0.5
+            confidence = 0.3
+            
         confidence = max(0.1, min(0.9, confidence))
         
         return raw_score, confidence
@@ -770,16 +927,16 @@ class MultiPerturbationStabilityMetric(BaseMetric):
 
     def _get_default_features(self) -> Dict[str, Any]:
         """
-        Return default features when analysis is not possible
+        Return more meaningful default features
         """
         return {"original_likelihood"         : 2.0,
                 "avg_perturbed_likelihood"    : 1.8,
                 "likelihood_ratio"            : 1.1,
                 "normalized_likelihood_ratio" : 0.55,
-                "stability_score"             : 0.5,
-                "curvature_score"             : 0.5,
+                "stability_score"             : 0.3, 
+                "curvature_score"             : 0.3,
                 "perturbation_variance"       : 0.05,
-                "avg_chunk_stability"         : 0.5,
+                "avg_chunk_stability"         : 0.3,
                 "stability_variance"          : 0.1,
                 "num_perturbations"           : 0,
                 "num_valid_perturbations"     : 0,
@@ -814,14 +971,14 @@ class MultiPerturbationStabilityMetric(BaseMetric):
         # Normalize whitespace
         text = ' '.join(text.split())
         
-        # RoBERTa works better with proper punctuation
+        # DistilRoBERTa works better with proper punctuation
         if not text.endswith(('.', '!', '?')):
             text += '.'
         
         # Truncate to safe length
         if (len(text) > 1000):
             sentences = text.split('. ')
-            if len(sentences) > 1:
+            if (len(sentences) > 1):
                 # Keep first few sentences
                 text = '. '.join(sentences[:3]) + '.'
             
@@ -831,50 +988,54 @@ class MultiPerturbationStabilityMetric(BaseMetric):
         return text
     
 
-    def _configure_tokenizer_padding(self, tokenizer) -> Any:
-        """
-        Configure tokenizer for proper padding
-        """
-        if tokenizer.pad_token is None:
-            if tokenizer.eos_token is not None:
-                tokenizer.pad_token = tokenizer.eos_token
-            
-            else:
-                tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-        
-        tokenizer.padding_side = "left"
-        
-        return tokenizer
-    
-
     def _clean_roberta_token(self, token: str) -> str:
         """
-        Clean tokens from RoBERTa tokenizer
+        Clean tokens from DistilRoBERTa tokenizer
         """
         if not token:
             return ""
         
-        # Remove RoBERTa-specific artifacts
+        # Remove DistilRoBERTa-specific artifacts
         token = token.replace('Ġ', ' ')  # RoBERTa space marker
         token = token.replace('</s>', '')
         token = token.replace('<s>', '')
         token = token.replace('<pad>', '')
+        token = token.replace('<mask>', '')
         
-        # Remove leading/trailing whitespace and punctuation
-        token = token.strip(' .,!?;:"\'')
+        # Remove leading/trailing whitespace
+        token = token.strip()
         
-        return token
+        # Only remove punctuation if token is ONLY punctuation
+        if token and not token.replace('.', '').replace(',', '').replace('!', '').replace('?', '').strip():
+            return ""
+        
+        # Keep the token if it has at least 2 alphanumeric characters
+        if sum(c.isalnum() for c in token) >= 2:
+            return token
+        
+        return ""
     
 
     def _is_valid_perturbation(self, perturbed_text: str, original_text: str) -> bool:
         """
-        Check if a perturbation is valid
+        Check if a perturbation is valid (more lenient validation)
         """
-        # Not too short
-        return (perturbed_text and 
-                len(perturbed_text.strip()) > 10 and 
-                perturbed_text != original_text and
-                len(perturbed_text) > len(original_text) * 0.5)  
+        if (not perturbed_text or not perturbed_text.strip()):
+            return False
+        
+        # Must be different from original
+        if (perturbed_text == original_text):
+            return False
+        
+        # Lenient length check
+        if (len(perturbed_text) < len(original_text) * 0.3):
+            return False
+        
+        # Must have some actual content
+        if len(perturbed_text.strip()) < 5:
+            return False
+        
+        return True
     
 
     def cleanup(self):

models/model_manager.py

@@ -21,6 +21,7 @@ from transformers import AutoTokenizer
 from transformers import GPT2LMHeadModel
 from config.model_config import ModelType
 from config.model_config import ModelConfig
+from transformers import AutoModelForCausalLM
 from transformers import AutoModelForMaskedLM
 from config.model_config import MODEL_REGISTRY
 from config.model_config import get_model_config
@@ -237,6 +238,12 @@ class ModelManager:
             elif (model_config.model_type == ModelType.TRANSFORMER):
                 model = self._load_transformer(config = model_config)
 
+            elif (model_config.model_type == ModelType.CAUSAL_LM):
+                model = self._load_causal_lm(config = model_config)
+
+            elif (model_config.model_type == ModelType.MASKED_LM):
+                model = self._load_masked_lm(config = model_config)
+
             elif (model_config.model_type == ModelType.RULE_BASED):
                 # Check if it's a spaCy model
                 if model_config.additional_params.get("is_spacy_model", False):
@@ -288,7 +295,13 @@ class ModelManager:
         logger.info(f"Loading tokenizer for: {model_name}")
         
         try:
-            if (model_config.model_type in [ModelType.GPT, ModelType.CLASSIFIER, ModelType.SEQUENCE_CLASSIFICATION, ModelType.TRANSFORMER]):
+            if (model_config.model_type in [ModelType.GPT, 
+                                            ModelType.CLASSIFIER, 
+                                            ModelType.SEQUENCE_CLASSIFICATION, 
+                                            ModelType.TRANSFORMER,
+                                            ModelType.CAUSAL_LM,
+                                            ModelType.MASKED_LM]):
+                
                 tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = model_config.model_id,
                                                           cache_dir                     = str(self.cache_dir),
                                                          )
@@ -339,6 +352,54 @@ class ModelManager:
         return (model, tokenizer)
 
     
+    def _load_causal_lm(self, config: ModelConfig) -> tuple:
+        """
+        Load causal language model (like GPT-2) for text generation
+        """
+        model     = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path = config.model_id,
+                                                         cache_dir                     = str(self.cache_dir),
+                                                        )
+
+        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = config.model_id,
+                                                  cache_dir                     = str(self.cache_dir),
+                                                 )
+        
+        # Move to device
+        model     = model.to(self.device)
+
+        model.eval()
+        
+        # Apply quantization if enabled
+        if (settings.USE_QUANTIZATION and config.quantizable):
+            model = self._quantize_model(model = model)
+        
+        return (model, tokenizer)
+
+    
+    def _load_masked_lm(self, config: ModelConfig) -> tuple:
+        """
+        Load masked language model (like RoBERTa) for fill-mask tasks
+        """
+        model     = AutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path = config.model_id,
+                                                         cache_dir                     = str(self.cache_dir),
+                                                        )
+
+        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = config.model_id,
+                                                  cache_dir                     = str(self.cache_dir),
+                                                 )
+        
+        # Move to device
+        model     = model.to(self.device)
+
+        model.eval()
+        
+        # Apply quantization if enabled
+        if (settings.USE_QUANTIZATION and config.quantizable):
+            model = self._quantize_model(model = model)
+        
+        return (model, tokenizer)
+
+    
     def _load_classifier(self, config: ModelConfig) -> Any:
         """
         Load classification model (for zero-shot, etc.)
@@ -483,7 +544,7 @@ class ModelManager:
         logger.info(f"Downloading model: {model_name} ({model_config.model_id})")
         
         try:
-            if model_config.model_type == ModelType.SENTENCE_TRANSFORMER:
+            if (model_config.model_type == ModelType.SENTENCE_TRANSFORMER):
                 SentenceTransformer(model_name_or_path = model_config.model_id,
                                     cache_folder       = str(self.cache_dir),
                                    )
@@ -506,6 +567,24 @@ class ModelManager:
                                               cache_dir                     = str(self.cache_dir),
                                              )
 
+            elif (model_config.model_type == ModelType.CAUSAL_LM):
+                AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path = model_config.model_id,
+                                                     cache_dir                     = str(self.cache_dir),
+                                                    )
+
+                AutoTokenizer.from_pretrained(pretrained_model_name_or_path = model_config.model_id,
+                                              cache_dir                     = str(self.cache_dir),
+                                             )
+
+            elif (model_config.model_type == ModelType.MASKED_LM):
+                AutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path = model_config.model_id,
+                                                     cache_dir                     = str(self.cache_dir),
+                                                    )
+
+                AutoTokenizer.from_pretrained(pretrained_model_name_or_path = model_config.model_id,
+                                              cache_dir                     = str(self.cache_dir),
+                                             )
+
             elif (model_config.model_type == ModelType.RULE_BASED):
                 if model_config.additional_params.get("is_spacy_model", False):
                     subprocess.run(["python", "-m", "spacy", "download", model_config.model_id], check = True)

reporter/report_generator.py

@@ -79,6 +79,9 @@ class ReportGenerator:
         --------
                 { dict }          : Dictionary mapping format to filepath
         """
+        # Convert DetectionResult to dict for consistent access
+        detection_dict = detection_result.to_dict() if hasattr(detection_result, 'to_dict') else detection_result
+        
         # Generate detailed reasoning
         reasoning        = self.reasoning_generator.generate(ensemble_result    = detection_result.ensemble_result,
                                                              metric_results     = detection_result.metric_results,
@@ -88,7 +91,7 @@ class ReportGenerator:
                                                             )
         
         # Extract detailed metrics from ACTUAL detection results
-        detailed_metrics = self._extract_detailed_metrics(detection_result)
+        detailed_metrics = self._extract_detailed_metrics(detection_dict)
         
         # Timestamp for filenames
         timestamp        = datetime.now().strftime("%Y%m%d_%H%M%S")
@@ -97,7 +100,7 @@ class ReportGenerator:
         
         # Generate requested formats
         if ("json" in formats):  
-            json_path               = self._generate_json_report(detection_result      = detection_result, 
+            json_path               = self._generate_json_report(detection_dict        = detection_dict, 
                                                                  reasoning             = reasoning, 
                                                                  detailed_metrics      = detailed_metrics, 
                                                                  attribution_result    = attribution_result,
@@ -108,7 +111,7 @@ class ReportGenerator:
         
         if ("pdf" in formats):
             try:
-                pdf_path               = self._generate_pdf_report(detection_result      = detection_result, 
+                pdf_path               = self._generate_pdf_report(detection_dict        = detection_dict, 
                                                                    reasoning             = reasoning, 
                                                                    detailed_metrics      = detailed_metrics, 
                                                                    attribution_result    = attribution_result,
@@ -126,26 +129,29 @@ class ReportGenerator:
         return generated_files
 
 
-    def _extract_detailed_metrics(self, detection_result: DetectionResult) -> List[DetailedMetric]:
+    def _extract_detailed_metrics(self, detection_dict: Dict) -> List[DetailedMetric]:
         """
         Extract detailed metrics with sub-metrics from ACTUAL detection result
         """
         detailed_metrics = list()
-        metric_results   = detection_result.metric_results
-        ensemble_result  = detection_result.ensemble_result
+        metrics_data     = detection_dict.get("metrics", {})
+        ensemble_data    = detection_dict.get("ensemble", {})
         
         # Get actual metric weights from ensemble
-        metric_weights   = getattr(ensemble_result, 'metric_weights', {})
+        metric_weights   = ensemble_data.get("metric_contributions", {})
         
         # Extract actual metric data
-        for metric_name, metric_result in metric_results.items():
-            if metric_result.error is not None:
+        for metric_name, metric_result in metrics_data.items():
+            if not isinstance(metric_result, dict):
+                continue
+                
+            if metric_result.get("error") is not None:
                 continue
                 
             # Get actual probabilities and confidence
-            ai_prob    = metric_result.ai_probability * 100
-            human_prob = metric_result.human_probability * 100
-            confidence = metric_result.confidence * 100
+            ai_prob    = metric_result.get("ai_probability", 0) * 100
+            human_prob = metric_result.get("human_probability", 0) * 100
+            confidence = metric_result.get("confidence", 0) * 100
             
             # Determine verdict based on actual probability
             if (ai_prob >= 60):
@@ -158,7 +164,9 @@ class ReportGenerator:
                 verdict = "MIXED (AI + HUMAN)"
             
             # Get actual weight or use default
-            weight                = metric_weights.get(metric_name, 0.0) * 100
+            weight = 0.0
+            if metric_name in metric_weights:
+                weight = metric_weights[metric_name].get("weight", 0.0) * 100
             
             # Extract actual detailed metrics from metric result
             detailed_metrics_data = self._extract_metric_details(metric_name   = metric_name, 
@@ -182,22 +190,22 @@ class ReportGenerator:
         return detailed_metrics
 
 
-    def _extract_metric_details(self, metric_name: str, metric_result) -> Dict[str, float]:
+    def _extract_metric_details(self, metric_name: str, metric_result: Dict) -> Dict[str, float]:
         """
         Extract detailed sub-metrics from metric result
         """
         details = dict()
         
         # Try to get details from metric result
-        if ((hasattr(metric_result, 'details')) and metric_result.details):
-            details = metric_result.details.copy()
+        if metric_result.get("details"):
+            details = metric_result["details"].copy()
         
         # If no details available, provide basic calculated values
         if not details:
-            details = {"ai_probability"    : metric_result.ai_probability * 100,
-                       "human_probability" : metric_result.human_probability * 100,
-                       "confidence"        : metric_result.confidence * 100,
-                       "score"             : getattr(metric_result, 'score', 0.0) * 100,
+            details = {"ai_probability"    : metric_result.get("ai_probability", 0) * 100,
+                       "human_probability" : metric_result.get("human_probability", 0) * 100,
+                       "confidence"        : metric_result.get("confidence", 0) * 100,
+                       "score"             : metric_result.get("score", 0) * 100,
                       }
         
         return details
@@ -218,7 +226,7 @@ class ReportGenerator:
         return descriptions.get(metric_name, "Advanced text analysis metric.")
 
 
-    def _generate_json_report(self, detection_result: DetectionResult, reasoning: DetailedReasoning, detailed_metrics: List[DetailedMetric], 
+    def _generate_json_report(self, detection_dict: Dict, reasoning: DetailedReasoning, detailed_metrics: List[DetailedMetric], 
                               attribution_result: Optional[AttributionResult], highlighted_sentences: Optional[List] = None, filename: str = None) -> Path:
         """
         Generate JSON format report with detailed metrics
@@ -251,7 +259,7 @@ class ReportGenerator:
                                          "index"          : sent.index,
                                        })
 
-        # Attribution data - use attribution_result
+        # Attribution data
         attribution_data = None
         
         if attribution_result:
@@ -264,30 +272,32 @@ class ReportGenerator:
                                 "metric_contributions": attribution_result.metric_contributions,
                                }
         
-        # Use ACTUAL detection results with ensemble integration
-        ensemble_result = detection_result.ensemble_result
+        # Use ACTUAL detection results from dictionary
+        ensemble_data = detection_dict.get("ensemble", {})
+        analysis_data = detection_dict.get("analysis", {})
+        metrics_data_dict = detection_dict.get("metrics", {})
+        performance_data = detection_dict.get("performance", {})
         
         report_data     = {"report_metadata"     : {"generated_at" : datetime.now().isoformat(),
                                                     "version"      : "1.0.0",
                                                     "format"       : "json",
                                                     "report_id"    : filename.replace('.json', ''),
                                                    },
-                           "overall_results"     : {"final_verdict"      : ensemble_result.final_verdict,
-                                                    "ai_probability"     : round(ensemble_result.ai_probability, 4),
-                                                    "human_probability"  : round(ensemble_result.human_probability, 4),
-                                                    "mixed_probability"  : round(ensemble_result.mixed_probability, 4),
-                                                    "overall_confidence" : round(ensemble_result.overall_confidence, 4),
-                                                    "uncertainty_score"  : round(ensemble_result.uncertainty_score, 4),
-                                                    "consensus_level"    : round(ensemble_result.consensus_level, 4),
-                                                    "domain"             : detection_result.domain_prediction.primary_domain.value,
-                                                    "domain_confidence"  : round(detection_result.domain_prediction.confidence, 4),
-                                                    "text_length"        : detection_result.processed_text.word_count,
-                                                    "sentence_count"     : detection_result.processed_text.sentence_count,
+                           "overall_results"     : {"final_verdict"      : ensemble_data.get("final_verdict", "Unknown"),
+                                                    "ai_probability"     : ensemble_data.get("ai_probability", 0),
+                                                    "human_probability"  : ensemble_data.get("human_probability", 0),
+                                                    "mixed_probability"  : ensemble_data.get("mixed_probability", 0),
+                                                    "overall_confidence" : ensemble_data.get("overall_confidence", 0),
+                                                    "uncertainty_score"  : ensemble_data.get("uncertainty_score", 0),
+                                                    "consensus_level"    : ensemble_data.get("consensus_level", 0),
+                                                    "domain"             : analysis_data.get("domain", "general"),
+                                                    "domain_confidence"  : analysis_data.get("domain_confidence", 0),
+                                                    "text_length"        : analysis_data.get("text_length", 0),
+                                                    "sentence_count"     : analysis_data.get("sentence_count", 0),
                                                    },
                            "ensemble_analysis"   : {"method_used"     : "confidence_calibrated",
-                                                    "metric_weights"  : {name: round(weight, 4) for name, weight in ensemble_result.metric_weights.items()},
-                                                    "weighted_scores" : {name: round(score, 4) for name, score in ensemble_result.weighted_scores.items()},
-                                                    "reasoning"       : ensemble_result.reasoning,
+                                                    "metric_weights"  : ensemble_data.get("metric_contributions", {}),
+                                                    "reasoning"       : ensemble_data.get("reasoning", []),
                                                    },
                            "detailed_metrics"    : metrics_data,
                            "detection_reasoning" : {"summary"                : reasoning.summary,
@@ -303,10 +313,10 @@ class ReportGenerator:
                                                    },
                            "highlighted_text"    : highlighted_data,
                            "model_attribution"   : attribution_data,
-                           "performance_metrics" : {"total_processing_time"  : round(detection_result.processing_time, 3),
-                                                    "metrics_execution_time" : {name: round(time, 3) for name, time in detection_result.metrics_execution_time.items()},
-                                                    "warnings"               : detection_result.warnings,
-                                                    "errors"                 : detection_result.errors,
+                           "performance_metrics" : {"total_processing_time"  : performance_data.get("total_time", 0),
+                                                    "metrics_execution_time" : performance_data.get("metrics_time", {}),
+                                                    "warnings"               : detection_dict.get("warnings", []),
+                                                    "errors"                 : detection_dict.get("errors", []),
                                                    }
                           }
         
@@ -323,7 +333,7 @@ class ReportGenerator:
         return output_path
 
 
-    def _generate_pdf_report(self, detection_result: DetectionResult, reasoning: DetailedReasoning, detailed_metrics: List[DetailedMetric], 
+    def _generate_pdf_report(self, detection_dict: Dict, reasoning: DetailedReasoning, detailed_metrics: List[DetailedMetric], 
                              attribution_result: Optional[AttributionResult], highlighted_sentences: Optional[List] = None, filename: str = None) -> Path:
         """
         Generate PDF format report with detailed metrics
@@ -378,8 +388,9 @@ class ReportGenerator:
                                        spaceAfter = 8,
                                       )
         
-        # Use detection results with ensemble integration
-        ensemble_result = detection_result.ensemble_result
+        # Use detection results from dictionary
+        ensemble_data = detection_dict.get("ensemble", {})
+        analysis_data = detection_dict.get("analysis", {})
         
         # Title and main sections
         elements.append(Paragraph("AI Text Detection Analysis Report", title_style))
@@ -388,13 +399,13 @@ class ReportGenerator:
         
         # Verdict section with ensemble metrics
         elements.append(Paragraph("Detection Summary", heading_style))
-        verdict_data = [['Final Verdict:', ensemble_result.final_verdict],
-                        ['AI Probability:', f"{ensemble_result.ai_probability:.1%}"],
-                        ['Human Probability:', f"{ensemble_result.human_probability:.1%}"],
-                        ['Mixed Probability:', f"{ensemble_result.mixed_probability:.1%}"],
-                        ['Overall Confidence:', f"{ensemble_result.overall_confidence:.1%}"],
-                        ['Uncertainty Score:', f"{ensemble_result.uncertainty_score:.1%}"],
-                        ['Consensus Level:', f"{ensemble_result.consensus_level:.1%}"],
+        verdict_data = [['Final Verdict:', ensemble_data.get("final_verdict", "Unknown")],
+                        ['AI Probability:', f"{ensemble_data.get('ai_probability', 0):.1%}"],
+                        ['Human Probability:', f"{ensemble_data.get('human_probability', 0):.1%}"],
+                        ['Mixed Probability:', f"{ensemble_data.get('mixed_probability', 0):.1%}"],
+                        ['Overall Confidence:', f"{ensemble_data.get('overall_confidence', 0):.1%}"],
+                        ['Uncertainty Score:', f"{ensemble_data.get('uncertainty_score', 0):.1%}"],
+                        ['Consensus Level:', f"{ensemble_data.get('consensus_level', 0):.1%}"],
                        ]
         
         verdict_table = Table(verdict_data, colWidths=[2*inch, 3*inch])
@@ -410,11 +421,11 @@ class ReportGenerator:
         
         # Content analysis
         elements.append(Paragraph("Content Analysis", heading_style))
-        content_data = [['Content Domain:', detection_result.domain_prediction.primary_domain.value.title()],
-                        ['Domain Confidence:', f"{detection_result.domain_prediction.confidence:.1%}"],
-                        ['Word Count:', str(detection_result.processed_text.word_count)],
-                        ['Sentence Count:', str(detection_result.processed_text.sentence_count)],
-                        ['Processing Time:', f"{detection_result.processing_time:.2f}s"],
+        content_data = [['Content Domain:', analysis_data.get("domain", "general").title()],
+                        ['Domain Confidence:', f"{analysis_data.get('domain_confidence', 0):.1%}"],
+                        ['Word Count:', str(analysis_data.get("text_length", 0))],
+                        ['Sentence Count:', str(analysis_data.get("sentence_count", 0))],
+                        ['Processing Time:', f"{detection_dict.get('performance', {}).get('total_time', 0):.2f}s"],
                        ]
         
         content_table = Table(content_data, colWidths=[2*inch, 3*inch])
@@ -428,14 +439,16 @@ class ReportGenerator:
         
         # Ensemble Analysis
         elements.append(Paragraph("Ensemble Analysis", heading_style))
-        elements.append(Paragraph(f"Method: Confidence Calibrated Aggregation", styles['Normal']))
+        elements.append(Paragraph("Method: Confidence Calibrated Aggregation", styles['Normal']))
         elements.append(Spacer(1, 0.1*inch))
         
         # Metric weights table
-        if hasattr(ensemble_result, 'metric_weights') and ensemble_result.metric_weights:
+        metric_contributions = ensemble_data.get("metric_contributions", {})
+        if metric_contributions:
             elements.append(Paragraph("Metric Weights", styles['Heading3']))
             weight_data = [['Metric', 'Weight']]
-            for metric, weight in ensemble_result.metric_weights.items():
+            for metric, contribution in metric_contributions.items():
+                weight = contribution.get("weight", 0)
                 weight_data.append([metric.title(), f"{weight:.1%}"])
             
             weight_table = Table(weight_data, colWidths=[3*inch, 1*inch])
@@ -578,8 +591,8 @@ class ReportGenerator:
         
         # Footer
         elements.append(Spacer(1, 0.3*inch))
-        elements.append(Paragraph(f"Generated by AI Text Detector v2.0 | Processing Time: {detection_result.processing_time:.2f}s", 
-                                ParagraphStyle('Footer', parent=styles['Normal'], fontSize=8, textColor=colors.gray)))
+        elements.append(Paragraph(f"Generated by AI Text Detector v2.0 | Processing Time: {detection_dict.get('performance', {}).get('total_time', 0):.2f}s", 
+                        ParagraphStyle('Footer', parent=styles['Normal'], fontSize=8, textColor=colors.gray)))
         
         # Build PDF
         doc.build(elements)

requirements.txt

@@ -1,51 +1,56 @@
 # Core Framework
-fastapi==0.104.1
-uvicorn[standard]==0.24.0
-pydantic==2.5.0
-pydantic-settings==2.1.0
-python-multipart==0.0.6
-
-# Machine Learning & Transformers 
-torch==2.1.0
-transformers==4.35.2
-sentence-transformers==2.3.1  
-tokenizers==0.15.0
-huggingface-hub==0.20.3 
+fastapi==0.115.6
+uvicorn==0.34.0
+pydantic==2.11.4
+pydantic-settings==2.11.0
+python-multipart==0.0.20
+
+# Machine Learning & Transformers
+torch==2.3.1
+transformers==4.48.0
+sentence-transformers==3.3.1
+tokenizers==0.21.0
+huggingface-hub==0.27.0
 
 # NLP Libraries
-spacy==3.7.2
-nltk==3.8.1
-textstat==0.7.3
+spacy==3.8.3
+nltk==3.9.1
+textstat==0.7.10
 
 # Scientific Computing
-numpy==1.24.3
-scipy==1.11.4
-scikit-learn==1.3.2
-pandas==2.1.3
+numpy==1.23.5
+scipy==1.12.0
+scikit-learn==1.6.0
+pandas==2.2.3
 
 # Text Processing
-python-docx==1.1.0
+python-docx==1.1.2
 PyPDF2==3.0.1
-pdfplumber==0.10.3
-pymupdf==1.23.8
+pdfplumber==0.11.5
+pymupdf==1.25.5
 python-magic==0.4.27
 
 # Language Detection
 langdetect==1.0.9
 
 # Visualization & Reporting
-matplotlib==3.8.2
+matplotlib==3.8.0
 seaborn==0.13.0
-reportlab==4.0.7
+reportlab==4.2.2
 
 # Utilities
-python-dotenv==1.0.0
+python-dotenv==1.0.1
 aiofiles==23.2.1
-httpx==0.25.2
-tenacity==8.2.3
+httpx==0.27.0
+tenacity==9.1.2
 
 # Logging & Monitoring
-loguru==0.7.2
+loguru==0.7.3
 
 # Caching
-diskcache==5.6.3
+diskcache==5.6.3
+
+# Additional packages from your working environment
+safetensors==0.4.4
+accelerate==1.2.1
+protobuf==4.25.4

ui/static/index.html

@@ -273,7 +273,6 @@ body {
     padding: 2rem;
     border: 1px solid var(--border);
     backdrop-filter: blur(10px);
-    /* Changed from fixed height to use available space */
     height: 850px;
     overflow: hidden;
     display: flex;
@@ -621,7 +620,7 @@ input[type="checkbox"] {
     color: var(--text-secondary);
     line-height: 1.7;
 }
-/* Enhanced Reasoning Styles */
+/* Reasoning Styles */
 .reasoning-box.enhanced {
     background: linear-gradient(135deg, rgba(30, 41, 59, 0.95) 0%, rgba(15, 23, 42, 0.95) 100%);
     border: 1px solid rgba(71, 85, 105, 0.5);
@@ -703,7 +702,7 @@ input[type="checkbox"] {
 .metric-indicator {
     display: flex;
     justify-content: space-between;
-    align-items: center;
+    align-items: left;
     padding: 0.75rem;
     margin-bottom: 0.5rem;
     border-radius: 8px;
@@ -714,7 +713,7 @@ input[type="checkbox"] {
     transform: translateX(4px);
 }
 .metric-name {
-    font-weight: 600;
+    font-weight: 400;
     color: var(--text-primary);
     min-width: 140px;
 }
@@ -795,6 +794,23 @@ input[type="checkbox"] {
     font-weight: 700;
     color: var(--primary);
 }
+.attribution-confidence {
+    margin-top: 0.75rem;
+    font-size: 0.85rem;
+    color: var(--text-secondary);
+}
+.attribution-uncertain {
+    color: var(--text-muted);
+    font-style: italic;
+    margin-top: 0.5rem;
+    font-size: 0.9rem;
+}
+.attribution-reasoning {
+    color: var(--text-secondary);
+    margin-top: 1rem;
+    font-size: 0.9rem;
+    line-height: 1.4;
+}
 /* Download Actions */
 .download-actions {
     display: flex;
@@ -959,21 +975,16 @@ input[type="checkbox"] {
 }
 .metrics-carousel-content {
     flex: 1;
-    /* Removed padding and centering to allow content to fill space */
     padding: 0;
-    /* Removed align-items: center; justify-content: center; to let content take natural space */
     display: flex;
     align-items: flex-start;
     justify-content: flex-start;
     overflow-y: auto;
-    /* Added some internal spacing for readability */
     padding: 1rem;
-    /* min-height: 600px; */
 }
 .metric-slide {
     display: none;
     width: 100%;
-    /* Reduced padding to make card tighter */
     padding: 1rem;
 }
 .metric-slide.active {
@@ -1011,6 +1022,43 @@ input[type="checkbox"] {
     color: var(--text-secondary);
     font-weight: 600;
 }
+/* Info Card Text Styles */
+.verdict-text {
+    font-size: 1.2rem !important;
+}
+.domain-text {
+    font-size: 1.1rem !important;
+}
+
+.verdict-mixed {
+    background: rgba(168, 85, 247, 0.2);
+    color: #a855f7;
+    border: 1px solid rgba(168, 85, 247, 0.3);
+}
+
+/* Reasoning Bullet Points */
+.reasoning-bullet-points {
+    margin: 1.5rem 0;
+    line-height: 1.6;
+    text-align: left;
+}
+
+.bullet-point {
+    margin-bottom: 0.75rem;
+    padding-left: 0.5rem;
+    color: var(--text-secondary);
+    font-size: 0.95rem;
+    text-align: left;
+}
+
+.bullet-point:last-child {
+    margin-bottom: 0;
+}
+
+.bullet-point strong {
+    color: var(--text-primary);
+}
+
 /* Responsive */
 @media (max-width: 1200px) {
     .interface-grid {
@@ -1222,7 +1270,7 @@ html {
                     id="text-input" 
                     class="text-input" 
                     placeholder="Paste your text here for analysis...
-The more text you provide (minimum 50 characters), the more accurate the detection will be. Our system analyzes linguistic patterns, statistical features, and semantic structures to determine authenticity."
+The more text you provide (minimum 50 characters), the more accurate the detection will be."
                 ></textarea>
             </div>
             <div id="upload-tab" class="tab-content">
@@ -1351,18 +1399,21 @@ const API_BASE = '';
 let currentAnalysisData = null;
 let currentMetricIndex = 0;
 let totalMetrics = 0;
+
 // Navigation
 function showLanding() {
     document.getElementById('landing-page').style.display = 'block';
     document.getElementById('analysis-interface').style.display = 'none';
     window.scrollTo(0, 0);
 }
+
 function showAnalysis() {
     document.getElementById('landing-page').style.display = 'none';
     document.getElementById('analysis-interface').style.display = 'block';
     window.scrollTo(0, 0);
     resetAnalysisInterface();
 }
+
 // Reset analysis interface
 function resetAnalysisInterface() {
     // Clear text input
@@ -1419,6 +1470,7 @@ function resetAnalysisInterface() {
     currentMetricIndex = 0;
     totalMetrics = 0;
 }
+
 // Input Tab Switching
 document.querySelectorAll('.input-tab').forEach(tab => {
     tab.addEventListener('click', () => {
@@ -1431,6 +1483,7 @@ document.querySelectorAll('.input-tab').forEach(tab => {
         document.getElementById(`${tabName}-tab`).classList.add('active');
     });
 });
+
 // Report Tab Switching
 document.querySelectorAll('.report-tab').forEach(tab => {
     tab.addEventListener('click', () => {
@@ -1443,24 +1496,30 @@ document.querySelectorAll('.report-tab').forEach(tab => {
         document.getElementById(`${reportName}-report`).classList.add('active');
     });
 });
+
 // File Upload Handling
 const fileInput = document.getElementById('file-input');
 const fileUploadArea = document.getElementById('file-upload-area');
 const fileNameDisplay = document.getElementById('file-name-display');
+
 fileUploadArea.addEventListener('click', () => {
     fileInput.click();
 });
+
 fileInput.addEventListener('change', (e) => {
     handleFileSelect(e.target.files[0]);
 });
+
 // Drag and Drop
 fileUploadArea.addEventListener('dragover', (e) => {
     e.preventDefault();
     fileUploadArea.classList.add('drag-over');
 });
+
 fileUploadArea.addEventListener('dragleave', () => {
     fileUploadArea.classList.remove('drag-over');
 });
+
 fileUploadArea.addEventListener('drop', (e) => {
     e.preventDefault();
     fileUploadArea.classList.remove('drag-over');
@@ -1470,6 +1529,7 @@ fileUploadArea.addEventListener('drop', (e) => {
         handleFileSelect(file);
     }
 });
+
 function handleFileSelect(file) {
     if (!file) return;
     const allowedTypes = ['.txt', '.pdf', '.docx', '.doc', '.md'];
@@ -1488,16 +1548,19 @@ function handleFileSelect(file) {
         <span style="color: var(--text-muted);">(${formatFileSize(file.size)})</span>
     `;
 }
+
 function formatFileSize(bytes) {
     if (bytes < 1024) return bytes + ' B';
     if (bytes < 1024 * 1024) return (bytes / 1024).toFixed(1) + ' KB';
     return (bytes / (1024 * 1024)).toFixed(1) + ' MB';
 }
+
 // Analyze Button
 document.getElementById('analyze-btn').addEventListener('click', async () => {
     const activeTab = document.querySelector('.input-tab.active').dataset.tab;
     const textInput = document.getElementById('text-input').value.trim();
     const fileInput = document.getElementById('file-input').files[0];
+
     if (activeTab === 'paste' && !textInput) {
         alert('Please paste some text to analyze (minimum 50 characters).');
         return;
@@ -1510,21 +1573,26 @@ document.getElementById('analyze-btn').addEventListener('click', async () => {
         alert('Please select a file to upload.');
         return;
     }
+
     await performAnalysis(activeTab, textInput, fileInput);
 });
+
 // Refresh Button - clears everything and shows empty state
 document.getElementById('refresh-btn').addEventListener('click', () => {
     resetAnalysisInterface();
 });
+
 // Try Next Button - same as refresh but keeps the interface ready
 document.getElementById('try-next-btn').addEventListener('click', () => {
     resetAnalysisInterface();
 });
+
 async function performAnalysis(mode, text, file) {
     const analyzeBtn = document.getElementById('analyze-btn');
     analyzeBtn.disabled = true;
     analyzeBtn.innerHTML = '⏳ Analyzing...';
     showLoading();
+
     try {
         let response;
         if (mode === 'paste') {
@@ -1542,12 +1610,14 @@ async function performAnalysis(mode, text, file) {
         analyzeBtn.innerHTML = '🔍 Analyze Text';
     }
 }
+
 async function analyzeText(text) {
     const domain = document.getElementById('domain-select').value || null;
     const enableAttribution = document.getElementById('enable-attribution').checked;
     const enableHighlighting = document.getElementById('enable-highlighting').checked;
     const useSentenceLevel = document.getElementById('use-sentence-level').checked;
     const includeMetricsSummary = document.getElementById('include-metrics-summary').checked;
+
     const response = await fetch(`${API_BASE}/api/analyze`, {
         method: 'POST',
         headers: { 'Content-Type': 'application/json' },
@@ -1561,17 +1631,20 @@ async function analyzeText(text) {
             skip_expensive_metrics: false
         })
     });
+
     if (!response.ok) {
         const error = await response.json();
         throw new Error(error.error || 'Analysis failed');
     }
     return await response.json();
 }
+
 async function analyzeFile(file) {
     const domain = document.getElementById('domain-select').value || null;
     const enableAttribution = document.getElementById('enable-attribution').checked;
     const useSentenceLevel = document.getElementById('use-sentence-level').checked;
     const includeMetricsSummary = document.getElementById('include-metrics-summary').checked;
+
     const formData = new FormData();
     formData.append('file', file);
     if (domain) formData.append('domain', domain);
@@ -1579,16 +1652,19 @@ async function analyzeFile(file) {
     formData.append('use_sentence_level', useSentenceLevel.toString());
     formData.append('include_metrics_summary', includeMetricsSummary.toString());
     formData.append('skip_expensive_metrics', 'false');
+
     const response = await fetch(`${API_BASE}/api/analyze/file`, {
         method: 'POST',
         body: formData
     });
+
     if (!response.ok) {
         const error = await response.json();
         throw new Error(error.error || 'File analysis failed');
     }
     return await response.json();
 }
+
 function showLoading() {
     document.getElementById('summary-report').innerHTML = `
         <div class="loading">
@@ -1600,6 +1676,7 @@ function showLoading() {
         </div>
     `;
 }
+
 function showError(message) {
     document.getElementById('summary-report').innerHTML = `
         <div class="empty-state">
@@ -1609,6 +1686,7 @@ function showError(message) {
         </div>
     `;
 }
+
 function displayResults(data) {
     console.log('Response data:', data);
     // Handle different response structures
@@ -1618,13 +1696,16 @@ function displayResults(data) {
         console.error('Full response:', data);
         return;
     }
+
     // Extract data based on your actual API structure
     const ensemble = detection.ensemble_result || detection.ensemble;
     const prediction = detection.prediction || {};
     const metrics = detection.metric_results || detection.metrics;
     const analysis = detection.analysis || {};
+
     // Display Summary with enhanced reasoning
     displaySummary(ensemble, prediction, analysis, data.attribution, data.reasoning);
+
     // Display Highlighted Text with enhanced features
     if (data.highlighted_html) {
         displayHighlightedText(data.highlighted_html);
@@ -1635,6 +1716,7 @@ function displayResults(data) {
             </div>
         `;
     }
+
     // Display Metrics with carousel
     if (metrics && Object.keys(metrics).length > 0) {
         displayMetricsCarousel(metrics, analysis, ensemble);
@@ -1646,10 +1728,48 @@ function displayResults(data) {
         `;
     }
 }
+
 function displaySummary(ensemble, prediction, analysis, attribution, reasoning) {
-    // Use ensemble values from your actual API response
+    // Extract and validate data with fallbacks
+    const {
+        aiProbability,
+        humanProbability,
+        mixedProbability,
+        verdict,
+        confidence,
+        domain,
+        isAI,
+        gaugeColor,
+        gaugeDegree,
+        confidenceLevel,
+        confidenceClass
+    } = extractSummaryData(ensemble, analysis);
+
+    // Generate attribution HTML with proper filtering
+    const attributionHTML = generateAttributionHTML(attribution);
+
+    document.getElementById('summary-report').innerHTML = `
+        <div class="result-summary">
+            ${createGaugeSection(aiProbability, humanProbability, mixedProbability, gaugeColor, gaugeDegree)}
+            ${createInfoGrid(verdict, confidence, confidenceClass, domain, mixedProbability)}
+            ${createEnhancedReasoningHTML(ensemble, analysis, reasoning)}
+            ${attributionHTML}
+            ${createDownloadActions()}
+        </div>
+    `;
+}
+
+// Helper function to extract and validate summary data
+function extractSummaryData(ensemble, analysis) {
     const aiProbability = ensemble.ai_probability !== undefined ? 
         (ensemble.ai_probability * 100).toFixed(0) : '0';
+    
+    const humanProbability = ensemble.human_probability !== undefined ? 
+        (ensemble.human_probability * 100).toFixed(0) : '0';
+    
+    const mixedProbability = ensemble.mixed_probability !== undefined ? 
+        (ensemble.mixed_probability * 100).toFixed(0) : '0';
+    
     const verdict = ensemble.final_verdict || 'Unknown';
     const confidence = ensemble.overall_confidence !== undefined ?
         (ensemble.overall_confidence * 100).toFixed(1) : '0';
@@ -1657,81 +1777,289 @@ function displaySummary(ensemble, prediction, analysis, attribution, reasoning)
     const isAI = verdict.toLowerCase().includes('ai');
     const gaugeColor = isAI ? 'var(--danger)' : 'var(--success)';
     const gaugeDegree = aiProbability * 3.6;
-    const confidenceLevel = parseFloat(confidence) >= 70 ? 'HIGH' :
-                           parseFloat(confidence) >= 40 ? 'MEDIUM' : 'LOW';
-    const confidenceClass = confidenceLevel === 'HIGH' ? 'confidence-high' : 
-                           confidenceLevel === 'MEDIUM' ? 'confidence-medium' : 'confidence-low';
-    let attributionHTML = '';
-    if (attribution && attribution.predicted_model) {
-        const modelName = attribution.predicted_model.replace(/_/g, ' ').replace(/-/g, ' ').toUpperCase();
-        const modelConf = attribution.confidence ? 
-            (attribution.confidence * 100).toFixed(1) : 'N/A';
-        let topModels = '';
-        if (attribution.model_probabilities) {
-            const sorted = Object.entries(attribution.model_probabilities)
-                .sort((a, b) => b[1] - a[1])
-                .slice(0, 3);
-            topModels = sorted.map(([model, prob]) => 
-                `<div class="model-match" style="margin-top: 0.5rem;">
-                    <span class="model-name">${model.replace(/_/g, ' ').replace(/-/g, ' ').toUpperCase()}</span>
-                    <span class="model-confidence">${(prob * 100).toFixed(1)}%</span>
-                </div>`
-            ).join('');
-        }
-        attributionHTML = `
+    
+    const confidenceLevel = getConfidenceLevel(parseFloat(confidence));
+    const confidenceClass = getConfidenceClass(confidenceLevel);
+
+    return {
+        aiProbability,
+        humanProbability,
+        mixedProbability,
+        verdict,
+        confidence,
+        domain,
+        isAI,
+        gaugeColor,
+        gaugeDegree,
+        confidenceLevel,
+        confidenceClass
+    };
+}
+
+// Helper function to determine confidence level
+function getConfidenceLevel(confidence) {
+    if (confidence >= 70) return 'HIGH';
+    if (confidence >= 40) return 'MEDIUM';
+    return 'LOW';
+}
+
+// Helper function to get confidence CSS class
+function getConfidenceClass(confidenceLevel) {
+    const classMap = {
+        'HIGH': 'confidence-high',
+        'MEDIUM': 'confidence-medium', 
+        'LOW': 'confidence-low'
+    };
+    return classMap[confidenceLevel] || 'confidence-low';
+}
+
+// Helper function to generate attribution HTML with filtering
+function generateAttributionHTML(attribution) {
+    if (!attribution || !attribution.predicted_model) {
+        return '';
+    }
+
+    const modelName = formatModelName(attribution.predicted_model);
+    const modelConf = attribution.confidence ? 
+        (attribution.confidence * 100).toFixed(1) : 'N/A';
+    
+    const topModelsHTML = generateTopModelsHTML(attribution.model_probabilities);
+    const reasoningHTML = generateAttributionReasoningHTML(attribution.reasoning);
+
+    // Only show attribution if confidence is meaningful (>30%)
+    if (attribution.confidence > 0.3) {
+        return `
             <div class="attribution-section">
                 <div class="attribution-title">🤖 AI Model Attribution</div>
-                ${topModels}
-                ${attribution.reasoning && attribution.reasoning.length > 0 ? 
-                    `<p style="color: var(--text-secondary); margin-top: 1rem; font-size: 0.9rem;">${attribution.reasoning[0]}</p>` : ''}
+                ${topModelsHTML}
+                <div class="attribution-confidence">
+                    Attribution Confidence: <strong>${modelConf}%</strong>
+                </div>
+                ${reasoningHTML}
             </div>
         `;
     }
-    document.getElementById('summary-report').innerHTML = `
-        <div class="result-summary">
-            <div class="gauge-container">
-                <div class="gauge-circle" style="--gauge-color: ${gaugeColor}; --gauge-degree: ${gaugeDegree}deg;">
-                    <div class="gauge-inner">
-                        <div class="gauge-value">${aiProbability}%</div>
-                        <div class="gauge-label">AI Probability</div>
-                    </div>
-                </div>
+    
+    return '';
+}
+
+// Helper function to generate top models HTML with filtering
+function generateTopModelsHTML(modelProbabilities) {
+    if (!modelProbabilities) {
+        return '<div class="attribution-uncertain">Model probabilities not available</div>';
+    }
+
+    // Filter and sort models
+    const meaningfulModels = Object.entries(modelProbabilities)
+        .sort((a, b) => b[1] - a[1])
+        .filter(([model, prob]) => prob > 0.15) // Only show models with >15% probability
+        .slice(0, 3); // Show top 3
+
+    if (meaningfulModels.length === 0) {
+        return `
+            <div class="attribution-uncertain">
+                Model attribution uncertain - text patterns don't strongly match any specific AI model
             </div>
-            <div class="result-info-grid">
-                <div class="info-card">
-                    <div class="info-label">Verdict</div>
-                    <div class="info-value" style="font-size: 1.2rem;">${verdict}</div>
-                </div>
-                <div class="info-card">
-                    <div class="info-label">Confidence Level</div>
-                    <div class="info-value">
-                        <span class="confidence-badge ${confidenceClass}">${confidence}%</span>
-                    </div>
+        `;
+    }
+
+    return meaningfulModels.map(([model, prob]) => 
+        `<div class="model-match">
+            <span class="model-name">${formatModelName(model)}</span>
+            <span class="model-confidence">${(prob * 100).toFixed(1)}%</span>
+        </div>`
+    ).join('');
+}
+
+// Helper function to format model names
+function formatModelName(modelName) {
+    return modelName.replace(/_/g, ' ').replace(/-/g, ' ').toUpperCase();
+}
+
+// Helper function to generate attribution reasoning HTML
+function generateAttributionReasoningHTML(reasoning) {
+    if (!reasoning || !Array.isArray(reasoning) || reasoning.length === 0) {
+        return '';
+    }
+
+    return `
+        <div class="attribution-reasoning">
+            ${reasoning[0]}
+        </div>
+    `;
+}
+
+// Helper function to create single-progress gauge section
+function createGaugeSection(aiProbability, humanProbability, mixedProbability, gaugeColor, gaugeDegree) {
+    // Determine which probability is highest
+    let maxValue, maxColor, maxLabel;
+    
+    if (aiProbability >= humanProbability && aiProbability >= mixedProbability) {
+        maxValue = aiProbability;
+        maxColor = 'var(--danger)';
+        maxLabel = 'AI Probability';
+    } else if (humanProbability >= aiProbability && humanProbability >= mixedProbability) {
+        maxValue = humanProbability;
+        maxColor = 'var(--success)';
+        maxLabel = 'Human Probability';
+    } else {
+        maxValue = mixedProbability;
+        maxColor = 'var(--primary)';
+        maxLabel = 'Mixed Probability';
+    }
+    
+    // Calculate the degree for the progress (maxValue% of 360 degrees)
+    const progressDegree = (maxValue / 100) * 360;
+    
+    return `
+        <div class="gauge-container">
+            <div class="single-progress-gauge" style="
+                background: conic-gradient(
+                    ${maxColor} 0deg,
+                    ${maxColor} ${progressDegree}deg,
+                    rgba(51, 65, 85, 0.3) ${progressDegree}deg,
+                    rgba(51, 65, 85, 0.3) 360deg
+                );
+            ">
+                <div class="gauge-inner">
+                    <div class="gauge-value" style="color: ${maxColor}">${maxValue}%</div>
+                    <div class="gauge-label">${maxLabel}</div>
                 </div>
-                <div class="info-card">
-                    <div class="info-label">Content Domain</div>
-                    <div class="info-value" style="font-size: 1.1rem;">${formatDomainName(domain)}</div>
+            </div>
+        </div>
+        <div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 1rem; margin: 1.5rem 0;">
+            <div style="text-align: center; padding: 1rem; background: rgba(239, 68, 68, 0.1); border-radius: 8px; border: 1px solid rgba(239, 68, 68, 0.3);">
+                <div style="font-size: 0.85rem; color: var(--danger); margin-bottom: 0.25rem; font-weight: 600;">AI</div>
+                <div style="font-size: 1.4rem; font-weight: 700; color: var(--danger);">${aiProbability}%</div>
+            </div>
+            <div style="text-align: center; padding: 1rem; background: rgba(16, 185, 129, 0.1); border-radius: 8px; border: 1px solid rgba(16, 185, 129, 0.3);">
+                <div style="font-size: 0.85rem; color: var(--success); margin-bottom: 0.25rem; font-weight: 600;">Human</div>
+                <div style="font-size: 1.4rem; font-weight: 700; color: var(--success);">${humanProbability}%</div>
+            </div>
+            <div style="text-align: center; padding: 1rem; background: rgba(6, 182, 212, 0.1); border-radius: 8px; border: 1px solid rgba(6, 182, 212, 0.3);">
+                <div style="font-size: 0.85rem; color: var(--primary); margin-bottom: 0.25rem; font-weight: 600;">Mixed</div>
+                <div style="font-size: 1.4rem; font-weight: 700; color: var(--primary);">${mixedProbability}%</div>
+            </div>
+        </div>
+        <style>
+            .single-progress-gauge {
+                width: 220px;
+                height: 220px;
+                margin: 0 auto 2rem;
+                position: relative;
+                border-radius: 50%;
+                box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+            }
+            
+            .gauge-inner {
+                position: absolute;
+                width: 170px;
+                height: 170px;
+                background: var(--bg-panel);
+                border-radius: 50%;
+                top: 50%;
+                left: 50%;
+                transform: translate(-50%, -50%);
+                display: flex;
+                flex-direction: column;
+                align-items: center;
+                justify-content: center;
+            }
+            
+            .gauge-value {
+                font-size: 3rem;
+                font-weight: 800;
+            }
+            
+            .gauge-label {
+                font-size: 0.9rem;
+                color: var(--text-secondary);
+                margin-top: 0.25rem;
+            }
+        </style>
+    `;
+}
+
+
+// Helper function to create info grid
+function createInfoGrid(verdict, confidence, confidenceClass, domain, mixedProbability) {
+    const mixedContentInfo = mixedProbability > 10 ? 
+        `<div style="margin-top: 0.5rem; font-size: 0.85rem; color: var(--primary);">
+            🔀 ${mixedProbability}% Mixed Content Detected
+        </div>` : '';
+
+    return `
+        <div class="result-info-grid">
+            <div class="info-card">
+                <div class="info-label">Verdict</div>
+                <div class="info-value verdict-text">${verdict}</div>
+                ${mixedContentInfo}
+            </div>
+            <div class="info-card">
+                <div class="info-label">Confidence Level</div>
+                <div class="info-value">
+                    <span class="confidence-badge ${confidenceClass}">${confidence}%</span>
                 </div>
             </div>
-            ${createEnhancedReasoningHTML(ensemble, analysis, reasoning)}
-            ${attributionHTML}
-            <div class="download-actions">
-                <button class="download-btn" onclick="downloadReport('json')">
-                    📄 Download JSON
-                </button>
-                <button class="download-btn" onclick="downloadReport('pdf')">
-                    📑 Download PDF Report
-                </button>
+            <div class="info-card">
+                <div class="info-label">Content Domain</div>
+                <div class="info-value domain-text">${formatDomainName(domain)}</div>
             </div>
         </div>
     `;
 }
+
+// Helper function to create download actions
+function createDownloadActions() {
+    return `
+        <div class="download-actions">
+            <button class="download-btn" onclick="downloadReport('json')">
+                📄 Download JSON
+            </button>
+            <button class="download-btn" onclick="downloadReport('pdf')">
+                📑 Download PDF Report
+            </button>
+        </div>
+    `;
+}
+
+// Helper function to format domain names
+function formatDomainName(domain) {
+    return domain.split('_').map(w => w.charAt(0).toUpperCase() + w.slice(1)).join(' ');
+}
+
 function createEnhancedReasoningHTML(ensemble, analysis, reasoning) {
-    // Use actual reasoning data if available
+    // Use reasoning data if available
     if (reasoning && reasoning.summary) {
-        // Process markdown-style *text* to <strong> tags
-        let processedSummary = reasoning.summary;
-        processedSummary = processedSummary.replace(/\*([^*]+)\*/g, '<strong>$1</strong>');
+        // Process the summary into bullet points
+        const bulletPoints = formatSummaryAsBulletPoints(reasoning.summary, ensemble, analysis);
+        
+        // Process key indicators with markdown formatting
+        let processedIndicators = [];
+        if (reasoning.key_indicators && reasoning.key_indicators.length > 0) {
+            processedIndicators = reasoning.key_indicators.map(indicator => {
+                let processedIndicator = indicator;
+                
+                // Remove HTML entities
+                processedIndicator = processedIndicator.replace(/&ast;/g, '*')
+                                                      .replace(/&#42;/g, '*');
+                
+                // Process bold formatting
+                processedIndicator = processedIndicator.replace(/\*\*([^*]+)\*\*/g, '<strong>$1</strong>')
+                                                      .replace(/\*([^*]+)\*/g, '<strong>$1</strong>');
+                
+                // Clean up remaining asterisks
+                processedIndicator = processedIndicator.replace(/\*\*/g, '')
+                                                      .replace(/\*(?![^<]*>)/g, '');
+
+                // Replace underscores with spaces
+                processedIndicator = processedIndicator.replace(/_/g, ' ');
+
+                
+                return processedIndicator;
+            });
+        }
+        
         return `
             <div class="reasoning-box enhanced">
                 <div class="reasoning-header">
@@ -1745,23 +2073,41 @@ function createEnhancedReasoningHTML(ensemble, analysis, reasoning) {
                     <div class="verdict-text">${ensemble.final_verdict}</div>
                     <div class="probability">AI Probability: <span class="probability-value">${(ensemble.ai_probability * 100).toFixed(2)}%</span></div>
                 </div>
-                <div class="reasoning-text-content">
-                    ${processedSummary}
+                <div class="reasoning-bullet-points">
+                    ${bulletPoints}
                 </div>
-                ${reasoning.key_indicators && reasoning.key_indicators.length > 0 ? `
+                ${processedIndicators.length > 0 ? `
                     <div class="metrics-breakdown">
-                        <div class="breakdown-header">Key Indicators</div>
-                        ${reasoning.key_indicators.map(indicator => {
-                            let processedIndicator = indicator;
-                            processedIndicator = processedIndicator.replace(/\*([^*]+)\*/g, '<strong>$1</strong>');
-                            return `
-                                <div class="metric-indicator">
-                                    <div class="metric-name">${processedIndicator.split(':')[0]}</div>
-                                    <div class="metric-details">
-                                        <span class="reasoning-text-content">${processedIndicator.split(':')[1]}</span>
+                        <div class="breakdown-header" style="text-align: center; font-weight: 700; color: var(--text-secondary); margin-bottom: 1rem;">
+                            KEY INDICATORS
+                        </div>
+                        ${processedIndicators.map(indicator => {
+                            // Split indicator into metric name and sub-metric details
+                            const colonIndex = indicator.indexOf(':');
+                            if (colonIndex !== -1) {
+                                const metricName = indicator.substring(0, colonIndex).trim();
+                                const metricDetails = indicator.substring(colonIndex + 1).trim();
+                                
+                                return `
+                                    <div style="margin-bottom: 1rem; text-align: left;">
+                                        <div style="font-weight: 700; color: #fff; text-align: center; margin-bottom: 0.5rem; font-size: 1rem;">
+                                            ${metricName}
+                                        </div>
+                                        <div style="color: var(--text-secondary); font-size: 0.9rem; line-height: 1.4; text-align: left;">
+                                            ${metricDetails}
+                                        </div>
                                     </div>
-                                </div>
-                            `;
+                                `;
+                            } else {
+                                // If no colon, treat as general indicator
+                                return `
+                                    <div style="margin-bottom: 1rem; text-align: left;">
+                                        <div style="color: var(--text-secondary); font-size: 0.9rem; line-height: 1.4;">
+                                            ${indicator}
+                                        </div>
+                                    </div>
+                                `;
+                            }
                         }).join('')}
                     </div>
                 ` : ''}
@@ -1778,7 +2124,7 @@ function createEnhancedReasoningHTML(ensemble, analysis, reasoning) {
     return `
         <div class="reasoning-box">
             <div class="reasoning-title">💡 Detection Reasoning</div>
-            <p class="reasoning-text">
+            <p class="reasoning-text" style="text-align: left;">
                 Analysis based on 6-metric ensemble with domain-aware calibration. 
                 The system evaluated linguistic patterns, statistical features, and semantic structures 
                 to determine content authenticity with ${(ensemble.overall_confidence * 100).toFixed(1)}% confidence.
@@ -1786,6 +2132,58 @@ function createEnhancedReasoningHTML(ensemble, analysis, reasoning) {
         </div>
     `;
 }
+
+// Helper function to format summary as bullet points
+function formatSummaryAsBulletPoints(summary, ensemble, analysis) {
+    let processedSummary = summary;
+    
+    // Remove any existing HTML entities for asterisks first
+    processedSummary = processedSummary.replace(/&ast;/g, '*')
+                                      .replace(/&#42;/g, '*');
+    
+    // Process markdown bold formatting
+    processedSummary = processedSummary.replace(/\*\*([^*]+)\*\*/g, '<strong>$1</strong>')
+                                      .replace(/\*([^*]+)\*/g, '<strong>$1</strong>');
+    
+    // Final cleanup: remove any remaining standalone asterisks that weren't processed
+    processedSummary = processedSummary.replace(/\*\*/g, '')
+                                      .replace(/\*(?![^<]*>)/g, '');
+    
+    // Split the summary into sentences/phrases for bullet points
+    const sentences = processedSummary.split(/\.\s+/);
+    
+    // Create bullet points from key information
+    const bulletPoints = [];
+    
+    // Add confidence level as first bullet
+    const confidenceLevel = ensemble.overall_confidence >= 0.7 ? 'High Confidence' :
+                           ensemble.overall_confidence >= 0.4 ? 'Medium Confidence' : 'Low Confidence';
+    bulletPoints.push(`<div class="bullet-point">• ${confidenceLevel}</div>`);
+    
+    // Add verdict as second bullet
+    bulletPoints.push(`<div class="bullet-point">• ${ensemble.final_verdict}</div>`);
+    
+    // Add AI probability as third bullet
+    bulletPoints.push(`<div class="bullet-point">• AI Probability: ${(ensemble.ai_probability * 100).toFixed(2)}%</div>`);
+    
+    // Add the main analysis sentences as individual bullets
+    sentences.forEach(sentence => {
+        if (sentence.trim() && 
+            !sentence.includes('confidence') && 
+            !sentence.includes(ensemble.final_verdict) &&
+            !sentence.includes('AI probability')) {
+            // Clean up the sentence and add as bullet
+            let cleanSentence = sentence.trim();
+            if (!cleanSentence.endsWith('.')) {
+                cleanSentence += '.';
+            }
+            bulletPoints.push(`<div class="bullet-point">• ${cleanSentence}</div>`);
+        }
+    });
+    
+    return bulletPoints.join('');
+}
+
 function displayHighlightedText(html) {
     document.getElementById('highlighted-report').innerHTML = `
         ${createDefaultLegend()}
@@ -1795,6 +2193,7 @@ function displayHighlightedText(html) {
         ${getHighlightStyles()}
     `;
 }
+
 function createDefaultLegend() {
     return `
         <div class="highlight-legend">
@@ -1833,6 +2232,7 @@ function createDefaultLegend() {
         </div>
     `;
 }
+
 function getHighlightStyles() {
     return `
         <style>
@@ -1888,10 +2288,12 @@ function getHighlightStyles() {
         </style>
     `;
 }
+
 function displayMetricsCarousel(metrics, analysis, ensemble) {
     const metricOrder = ['structural', 'perplexity', 'entropy', 'semantic_analysis', 'linguistic', 'multi_perturbation_stability'];
     const availableMetrics = metricOrder.filter(key => metrics[key]);
     totalMetrics = availableMetrics.length;
+
     if (totalMetrics === 0) {
         document.getElementById('metrics-report').innerHTML = `
             <div class="empty-state">
@@ -1900,24 +2302,39 @@ function displayMetricsCarousel(metrics, analysis, ensemble) {
         `;
         return;
     }
+
     let carouselHTML = `
         <div class="metrics-carousel-container">
             <div class="metrics-carousel-content">
     `;
+
     availableMetrics.forEach((metricKey, index) => {
         const metric = metrics[metricKey];
         if (!metric) return;
+
         const aiProb = (metric.ai_probability * 100).toFixed(1);
         const humanProb = (metric.human_probability * 100).toFixed(1);
+        const mixedProb = (metric.mixed_probability * 100).toFixed(1);
         const confidence = (metric.confidence * 100).toFixed(1);
         const weight = ensemble.metric_contributions && ensemble.metric_contributions[metricKey] ?
-              (ensemble.metric_contributions[metricKey].weight * 100).toFixed(1) : '0.0';
-        const color = metric.ai_probability >= 0.6 ? 'var(--danger)' : 
-                     metric.ai_probability >= 0.4 ? 'var(--warning)' : 'var(--success)';
-        const verdictText = metric.ai_probability >= 0.6 ? 'AI' :
-                           metric.ai_probability >= 0.4 ? 'UNCERTAIN' : 'HUMAN';
-        const verdictClass = verdictText === 'AI' ? 'verdict-ai' :
-                            verdictText === 'UNCERTAIN' ? 'verdict-uncertain' : 'verdict-human';
+            (ensemble.metric_contributions[metricKey].weight * 100).toFixed(1) : '0.0';
+        
+        // Determine verdict based on probabilities
+        let verdictText, verdictClass;
+        if (metric.mixed_probability > 0.3) {
+            verdictText = 'MIXED';
+            verdictClass = 'verdict-mixed';
+        } else if (metric.ai_probability >= 0.6) {
+            verdictText = 'AI';
+            verdictClass = 'verdict-ai';
+        } else if (metric.ai_probability >= 0.4) {
+            verdictText = 'UNCERTAIN';
+            verdictClass = 'verdict-uncertain';
+        } else {
+            verdictText = 'HUMAN';
+            verdictClass = 'verdict-human';
+        }
+
         carouselHTML += `
             <div class="metric-slide ${index === 0 ? 'active' : ''}" data-metric-index="${index}">
                 <div class="metric-result-card">
@@ -1927,22 +2344,32 @@ function displayMetricsCarousel(metrics, analysis, ensemble) {
                     <div class="metric-description">
                         ${getMetricDescription(metricKey)}
                     </div>
-                    <div style="display: flex; gap: 1rem; margin: 1rem 0;">
-                        <div style="flex: 1;">
+                    
+                    <!-- Enhanced Probability Display with Mixed -->
+                    <div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 1rem; margin: 1rem 0;">
+                        <div style="text-align: center;">
                             <div style="font-size: 0.75rem; color: var(--text-muted); margin-bottom: 0.25rem;">AI</div>
                             <div style="background: rgba(51, 65, 85, 0.5); height: 8px; border-radius: 4px; overflow: hidden;">
                                 <div style="background: var(--danger); height: 100%; width: ${aiProb}%; transition: width 0.5s;"></div>
                             </div>
                             <div style="font-size: 0.85rem; font-weight: 600; margin-top: 0.25rem;">${aiProb}%</div>
                         </div>
-                        <div style="flex: 1;">
+                        <div style="text-align: center;">
                             <div style="font-size: 0.75rem; color: var(--text-muted); margin-bottom: 0.25rem;">Human</div>
                             <div style="background: rgba(51, 65, 85, 0.5); height: 8px; border-radius: 4px; overflow: hidden;">
                                 <div style="background: var(--success); height: 100%; width: ${humanProb}%; transition: width 0.5s;"></div>
                             </div>
                             <div style="font-size: 0.85rem; font-weight: 600; margin-top: 0.25rem;">${humanProb}%</div>
                         </div>
+                        <div style="text-align: center;">
+                            <div style="font-size: 0.75rem; color: var(--text-muted); margin-bottom: 0.25rem;">Mixed</div>
+                            <div style="background: rgba(51, 65, 85, 0.5); height: 8px; border-radius: 4px; overflow: hidden;">
+                                <div style="background: var(--primary); height: 100%; width: ${mixedProb}%; transition: width 0.5s;"></div>
+                            </div>
+                            <div style="font-size: 0.85rem; font-weight: 600; margin-top: 0.25rem;">${mixedProb}%</div>
+                        </div>
                     </div>
+                    
                     <div style="display: flex; justify-content: space-between; align-items: center; margin: 0.75rem 0;">
                         <span class="metric-verdict ${verdictClass}">${verdictText}</span>
                         <span style="font-size: 0.85rem; color: var(--text-secondary);">Confidence: ${confidence}% | Weight: ${weight}%</span>
@@ -1953,6 +2380,7 @@ function displayMetricsCarousel(metrics, analysis, ensemble) {
             </div>
         `;
     });
+
     carouselHTML += `
             </div>
             <div class="metrics-carousel-nav">
@@ -1962,9 +2390,11 @@ function displayMetricsCarousel(metrics, analysis, ensemble) {
             </div>
         </div>
     `;
+
     document.getElementById('metrics-report').innerHTML = carouselHTML;
     updateCarouselButtons();
 }
+
 function navigateMetrics(direction) {
     const newMetricIndex = currentMetricIndex + direction;
     if (newMetricIndex >= 0 && newMetricIndex < totalMetrics) {
@@ -1972,6 +2402,7 @@ function navigateMetrics(direction) {
         updateMetricCarousel();
     }
 }
+
 function updateMetricCarousel() {
     const slides = document.querySelectorAll('.metric-slide');
     slides.forEach((slide, index) => {
@@ -1988,6 +2419,7 @@ function updateMetricCarousel() {
         positionElement.textContent = `${currentMetricIndex + 1} / ${totalMetrics}`;
     }
 }
+
 function updateCarouselButtons() {
     const prevBtn = document.querySelector('.prev-btn');
     const nextBtn = document.querySelector('.next-btn');
@@ -1998,8 +2430,10 @@ function updateCarouselButtons() {
         nextBtn.disabled = currentMetricIndex === totalMetrics - 1;
     }
 }
+
 function renderMetricDetails(metricName, details) {
     if (!details || Object.keys(details).length === 0) return '';
+
     // Key metrics to show for each type
     const importantKeys = {
         'structural': ['burstiness_score', 'length_uniformity', 'avg_sentence_length', 'std_sentence_length'],
@@ -2007,29 +2441,47 @@ function renderMetricDetails(metricName, details) {
         'entropy': ['token_diversity', 'sequence_unpredictability', 'char_entropy'],
         'semantic_analysis': ['coherence_score', 'consistency_score', 'repetition_score'],
         'linguistic': ['pos_diversity', 'syntactic_complexity', 'grammatical_consistency'],
-        'multi_perturbation_stability': ['stability_score', 'curvature_score', 'likelihood_ratio']
+        'multi_perturbation_stability': ['stability_score', 'curvature_score', 'likelihood_ratio', 'perturbation_variance', 'mixed_probability']
     };
+
     const keysToShow = importantKeys[metricName] || Object.keys(details).slice(0, 6);
+
     let detailsHTML = '<div style="margin-top: 1rem; padding-top: 1rem; border-top: 1px solid var(--border);">';
     detailsHTML += '<div style="font-size: 0.9rem; font-weight: 600; color: var(--text-secondary); margin-bottom: 0.75rem;">📈 Detailed Metrics:</div>';
     detailsHTML += '<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); gap: 0.75rem; font-size: 0.85rem;">';
+
     keysToShow.forEach(key => {
         if (details[key] !== undefined && details[key] !== null) {
-            const value = typeof details[key] === 'number' ? 
-                (details[key] < 1 && details[key] > 0 ? (details[key] * 100).toFixed(2) + '%' : details[key].toFixed(2)) :
-                details[key];
+            let value = details[key];
+            let displayValue;
+            
+            // Format values appropriately
+            if (typeof value === 'number') {
+                if (key.includes('score') || key.includes('ratio') || key.includes('probability')) {
+                    displayValue = (value * 100).toFixed(2) + '%';
+                } else if (value < 1 && value > 0) {
+                    displayValue = value.toFixed(4);
+                } else {
+                    displayValue = value.toFixed(2);
+                }
+            } else {
+                displayValue = value;
+            }
+            
             const label = key.replace(/_/g, ' ').replace(/\b\w/g, c => c.toUpperCase());
             detailsHTML += `
                 <div style="background: rgba(15, 23, 42, 0.6); padding: 0.5rem; border-radius: 6px;">
                     <div style="color: var(--text-muted); font-size: 0.75rem; margin-bottom: 0.25rem;">${label}</div>
-                    <div style="color: var(--primary); font-weight: 700;">${value}</div>
+                    <div style="color: var(--primary); font-weight: 700;">${displayValue}</div>
                 </div>
             `;
         }
     });
+
     detailsHTML += '</div></div>';
     return detailsHTML;
 }
+
 function getMetricDescription(metricName) {
     const descriptions = {
         structural: 'Analyzes sentence structure, length patterns, and statistical features.',
@@ -2041,6 +2493,7 @@ function getMetricDescription(metricName) {
     };
     return descriptions[metricName] || 'Metric analysis complete.';
 }
+
 function formatMetricName(name) {
     const names = {
         structural: 'Structural Analysis',
@@ -2052,17 +2505,17 @@ function formatMetricName(name) {
     };
     return names[name] || name.split('_').map(w => w.charAt(0).toUpperCase() + w.slice(1)).join(' ');
 }
-function formatDomainName(domain) {
-    return domain.split('_').map(w => w.charAt(0).toUpperCase() + w.slice(1)).join(' ');
-}
+
 async function downloadReport(format) {
     if (!currentAnalysisData) {
         alert('No analysis data available');
         return;
     }
+
     try {
         const analysisId = currentAnalysisData.analysis_id;
         const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
+
         // For JSON, download directly from current data
         if (format === 'json') {
             const data = {
@@ -2077,6 +2530,7 @@ async function downloadReport(format) {
             await downloadBlob(blob, filename);
             return;
         }
+
         // Get the original text for report generation
         const activeTab = document.querySelector('.input-tab.active').dataset.tab;
         let textToSend = '';
@@ -2086,19 +2540,23 @@ async function downloadReport(format) {
             textToSend = currentAnalysisData.detection_result?.processed_text?.text || 
                         'Uploaded file content - see analysis for details';
         }
+
         // For PDF, request from server
         const formData = new FormData();
         formData.append('analysis_id', analysisId);
         formData.append('text', textToSend);
         formData.append('formats', format);
         formData.append('include_highlights', document.getElementById('enable-highlighting').checked.toString());
+
         const response = await fetch(`${API_BASE}/api/report/generate`, {
             method: 'POST',
             body: formData
         });
+
         if (!response.ok) {
             throw new Error('Report generation failed');
         }
+
         const result = await response.json();
         if (result.reports && result.reports[format]) {
             const filename = result.reports[format];
@@ -2117,6 +2575,7 @@ async function downloadReport(format) {
         alert('Failed to download report. Please try again.');
     }
 }
+
 async function downloadBlob(blob, filename) {
     try {
         const url = URL.createObjectURL(blob);
@@ -2136,6 +2595,7 @@ async function downloadBlob(blob, filename) {
         alert('Download failed. Please try again.');
     }
 }
+
 function showDownloadSuccess(filename) {
     const notification = document.createElement('div');
     notification.style.cssText = `
@@ -2158,6 +2618,7 @@ function showDownloadSuccess(filename) {
         </div>
     `;
     document.body.appendChild(notification);
+
     if (!document.querySelector('#download-animation')) {
         const style = document.createElement('style');
         style.id = 'download-animation';
@@ -2169,12 +2630,14 @@ function showDownloadSuccess(filename) {
         `;
         document.head.appendChild(style);
     }
+
     setTimeout(() => {
         if (notification.parentNode) {
             notification.parentNode.removeChild(notification);
         }
     }, 3000);
 }
+
 // Smooth scrolling for anchor links
 document.querySelectorAll('a[href^="#"]').forEach(anchor => {
     anchor.addEventListener('click', function (e) {
@@ -2188,6 +2651,7 @@ document.querySelectorAll('a[href^="#"]').forEach(anchor => {
         }
     });
 });
+
 // Initialize - show landing page by default
 showLanding();
 </script>