Update app.py

app.py

@@ -176,201 +176,113 @@ class TextClassifier:
             'num_windows': len(predictions)
         }
 
-    def detailed_scan(self, text: str) -> Dict:
-        """Original prediction method with modified window handling"""
-        if self.model is None or self.tokenizer is None:
-            self.load_model()
-
-        self.model.eval()
-        sentences = self.processor.split_into_sentences(text)
-        if not sentences:
-            return {}
-
-        # Create centered windows for each sentence
-        windows, window_sentence_indices = self.processor.create_centered_windows(sentences, WINDOW_SIZE)
-
-        # Track scores for each sentence
-        sentence_appearances = {i: 0 for i in range(len(sentences))}
-        sentence_scores = {i: {'human_prob': 0.0, 'ai_prob': 0.0} for i in range(len(sentences))}
-
-        # Process windows in batches
-        batch_size = 16
-        for i in range(0, len(windows), batch_size):
-            batch_windows = windows[i:i + batch_size]
-            batch_indices = window_sentence_indices[i:i + batch_size]
-
-            inputs = self.tokenizer(
-                batch_windows,
-                truncation=True,
-                padding=True,
-                max_length=MAX_LENGTH,
-                return_tensors="pt"
-            ).to(self.device)
-
-            with torch.no_grad():
-                outputs = self.model(**inputs)
-                probs = F.softmax(outputs.logits, dim=-1)
-
-                # Attribute predictions more carefully
-                for window_idx, indices in enumerate(batch_indices):
-                    center_idx = len(indices) // 2
-                    center_weight = 0.7  # Higher weight for center sentence
-                    edge_weight = 0.3 / (len(indices) - 1)  # Distribute remaining weight
-
-                    for pos, sent_idx in enumerate(indices):
-                        # Apply higher weight to center sentence
-                        weight = center_weight if pos == center_idx else edge_weight
-                        sentence_appearances[sent_idx] += weight
-                        sentence_scores[sent_idx]['human_prob'] += weight * probs[window_idx][1].item()
-                        sentence_scores[sent_idx]['ai_prob'] += weight * probs[window_idx][0].item()
-
-            del inputs, outputs, probs
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        # Calculate final predictions
-        sentence_predictions = []
-        for i in range(len(sentences)):
-            if sentence_appearances[i] > 0:
-                human_prob = sentence_scores[i]['human_prob'] / sentence_appearances[i]
-                ai_prob = sentence_scores[i]['ai_prob'] / sentence_appearances[i]
-
-                # Only apply minimal smoothing at prediction boundaries
-                if i > 0 and i < len(sentences) - 1:
-                    prev_human = sentence_scores[i-1]['human_prob'] / sentence_appearances[i-1]
-                    prev_ai = sentence_scores[i-1]['ai_prob'] / sentence_appearances[i-1]
-                    next_human = sentence_scores[i+1]['human_prob'] / sentence_appearances[i+1]
-                    next_ai = sentence_scores[i+1]['ai_prob'] / sentence_appearances[i+1]
-
-                    # Check if we're at a prediction boundary
-                    current_pred = 'human' if human_prob > ai_prob else 'ai'
-                    prev_pred = 'human' if prev_human > prev_ai else 'ai'
-                    next_pred = 'human' if next_human > next_ai else 'ai'
-
-                    if current_pred != prev_pred or current_pred != next_pred:
-                        # Small adjustment at boundaries
-                        smooth_factor = 0.1
-                        human_prob = (human_prob * (1 - smooth_factor) + 
-                                    (prev_human + next_human) * smooth_factor / 2)
-                        ai_prob = (ai_prob * (1 - smooth_factor) + 
-                                (prev_ai + next_ai) * smooth_factor / 2)
-
-                sentence_predictions.append({
-                    'sentence': sentences[i],
-                    'human_prob': human_prob,
-                    'ai_prob': ai_prob,
-                    'prediction': 'human' if human_prob > ai_prob else 'ai',
-                    'confidence': max(human_prob, ai_prob)
-                })
-
-        return {
-            'sentence_predictions': sentence_predictions,
-            'highlighted_text': self.format_predictions_html(sentence_predictions),
-            'full_text': text,
-            'overall_prediction': self.aggregate_predictions(sentence_predictions)
-        }
-
-    def detailed_scan(self, text: str) -> Dict:
-        """Perform a detailed scan with improved sentence-level analysis."""
-        if not text.strip():
-            return {
-                'sentence_predictions': [],
-                'highlighted_text': '',
-                'full_text': '',
-                'overall_prediction': {
-                    'prediction': 'unknown',
-                    'confidence': 0.0,
-                    'num_sentences': 0
+        def detailed_scan(self, text: str) -> Dict:
+            """Original prediction method with modified window handling"""
+            # Clean up trailing whitespace
+            text = text.rstrip()
+            
+            if not text.strip():
+                return {
+                    'sentence_predictions': [],
+                    'highlighted_text': '',
+                    'full_text': '',
+                    'overall_prediction': {
+                        'prediction': 'unknown',
+                        'confidence': 0.0,
+                        'num_sentences': 0
+                    }
                 }
+        
+            self.model.eval()
+            sentences = self.processor.split_into_sentences(text)
+            if not sentences:
+                return {}
+        
+            # Create centered windows for each sentence
+            windows, window_sentence_indices = self.processor.create_centered_windows(sentences, WINDOW_SIZE)
+        
+            # Track scores for each sentence
+            sentence_appearances = {i: 0 for i in range(len(sentences))}
+            sentence_scores = {i: {'human_prob': 0.0, 'ai_prob': 0.0} for i in range(len(sentences))}
+        
+            # Process windows in batches
+            batch_size = 16
+            for i in range(0, len(windows), batch_size):
+                batch_windows = windows[i:i + batch_size]
+                batch_indices = window_sentence_indices[i:i + batch_size]
+        
+                inputs = self.tokenizer(
+                    batch_windows,
+                    truncation=True,
+                    padding=True,
+                    max_length=MAX_LENGTH,
+                    return_tensors="pt"
+                ).to(self.device)
+        
+                with torch.no_grad():
+                    outputs = self.model(**inputs)
+                    probs = F.softmax(outputs.logits, dim=-1)
+        
+                    # Attribute predictions with weighted scoring
+                    for window_idx, indices in enumerate(batch_indices):
+                        center_idx = len(indices) // 2
+                        center_weight = 0.7  # Higher weight for center sentence
+                        edge_weight = 0.3 / (len(indices) - 1)  # Distribute remaining weight
+        
+                        for pos, sent_idx in enumerate(indices):
+                            # Apply higher weight to center sentence
+                            weight = center_weight if pos == center_idx else edge_weight
+                            sentence_appearances[sent_idx] += weight
+                            sentence_scores[sent_idx]['human_prob'] += weight * probs[window_idx][1].item()
+                            sentence_scores[sent_idx]['ai_prob'] += weight * probs[window_idx][0].item()
+        
+                # Clean up memory
+                del inputs, outputs, probs
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
+        
+            # Calculate final predictions with boundary smoothing
+            sentence_predictions = []
+            for i in range(len(sentences)):
+                if sentence_appearances[i] > 0:
+                    human_prob = sentence_scores[i]['human_prob'] / sentence_appearances[i]
+                    ai_prob = sentence_scores[i]['ai_prob'] / sentence_appearances[i]
+        
+                    # Only apply minimal smoothing at prediction boundaries
+                    if i > 0 and i < len(sentences) - 1:
+                        prev_human = sentence_scores[i-1]['human_prob'] / sentence_appearances[i-1]
+                        prev_ai = sentence_scores[i-1]['ai_prob'] / sentence_appearances[i-1]
+                        next_human = sentence_scores[i+1]['human_prob'] / sentence_appearances[i+1]
+                        next_ai = sentence_scores[i+1]['ai_prob'] / sentence_appearances[i+1]
+        
+                        # Check if we're at a prediction boundary
+                        current_pred = 'human' if human_prob > ai_prob else 'ai'
+                        prev_pred = 'human' if prev_human > prev_ai else 'ai'
+                        next_pred = 'human' if next_human > next_ai else 'ai'
+        
+                        if current_pred != prev_pred or current_pred != next_pred:
+                            # Small adjustment at boundaries
+                            smooth_factor = 0.1
+                            human_prob = (human_prob * (1 - smooth_factor) + 
+                                        (prev_human + next_human) * smooth_factor / 2)
+                            ai_prob = (ai_prob * (1 - smooth_factor) + 
+                                    (prev_ai + next_ai) * smooth_factor / 2)
+        
+                    sentence_predictions.append({
+                        'sentence': sentences[i],
+                        'human_prob': human_prob,
+                        'ai_prob': ai_prob,
+                        'prediction': 'human' if human_prob > ai_prob else 'ai',
+                        'confidence': max(human_prob, ai_prob)
+                    })
+        
+            return {
+                'sentence_predictions': sentence_predictions,
+                'highlighted_text': self.format_predictions_html(sentence_predictions),
+                'full_text': text,
+                'overall_prediction': self.aggregate_predictions(sentence_predictions)
             }
 
-        sentences = self.processor.split_into_sentences(text)
-        if not sentences:
-            return {}
-
-        # Create centered windows for each sentence
-        windows, window_sentence_indices = self.processor.create_centered_windows(sentences, WINDOW_SIZE)
-
-        # Track scores for each sentence
-        sentence_appearances = {i: 0 for i in range(len(sentences))}
-        sentence_scores = {i: {'human_prob': 0.0, 'ai_prob': 0.0} for i in range(len(sentences))}
-
-        # Process windows in batches
-        for i in range(0, len(windows), BATCH_SIZE):
-            batch_windows = windows[i:i + BATCH_SIZE]
-            batch_indices = window_sentence_indices[i:i + BATCH_SIZE]
-
-            inputs = self.tokenizer(
-                batch_windows,
-                truncation=True,
-                padding=True,
-                max_length=MAX_LENGTH,
-                return_tensors="pt"
-            ).to(self.device)
-
-            with torch.no_grad():
-                outputs = self.model(**inputs)
-                probs = F.softmax(outputs.logits, dim=-1)
-
-                # Attribute predictions with weighted scoring
-                for window_idx, indices in enumerate(batch_indices):
-                    center_idx = len(indices) // 2
-                    center_weight = 0.7  # Higher weight for center sentence
-                    edge_weight = 0.3 / (len(indices) - 1)  # Distribute remaining weight
-
-                    for pos, sent_idx in enumerate(indices):
-                        # Apply higher weight to center sentence
-                        weight = center_weight if pos == center_idx else edge_weight
-                        sentence_appearances[sent_idx] += weight
-                        sentence_scores[sent_idx]['human_prob'] += weight * probs[window_idx][1].item()
-                        sentence_scores[sent_idx]['ai_prob'] += weight * probs[window_idx][0].item()
-
-            # Clean up memory
-            del inputs, outputs, probs
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-        # Calculate final predictions with boundary smoothing
-        sentence_predictions = []
-        for i in range(len(sentences)):
-            if sentence_appearances[i] > 0:
-                human_prob = sentence_scores[i]['human_prob'] / sentence_appearances[i]
-                ai_prob = sentence_scores[i]['ai_prob'] / sentence_appearances[i]
-
-                # Apply minimal smoothing at prediction boundaries
-                if i > 0 and i < len(sentences) - 1:
-                    prev_human = sentence_scores[i-1]['human_prob'] / sentence_appearances[i-1]
-                    prev_ai = sentence_scores[i-1]['ai_prob'] / sentence_appearances[i-1]
-                    next_human = sentence_scores[i+1]['human_prob'] / sentence_appearances[i+1]
-                    next_ai = sentence_scores[i+1]['ai_prob'] / sentence_appearances[i+1]
-
-                    # Check if we're at a prediction boundary
-                    current_pred = 'human' if human_prob > ai_prob else 'ai'
-                    prev_pred = 'human' if prev_human > prev_ai else 'ai'
-                    next_pred = 'human' if next_human > next_ai else 'ai'
-
-                    if current_pred != prev_pred or current_pred != next_pred:
-                        # Small adjustment at boundaries
-                        smooth_factor = 0.1
-                        human_prob = (human_prob * (1 - smooth_factor) + 
-                                    (prev_human + next_human) * smooth_factor / 2)
-                        ai_prob = (ai_prob * (1 - smooth_factor) + 
-                                (prev_ai + next_ai) * smooth_factor / 2)
-
-                sentence_predictions.append({
-                    'sentence': sentences[i],
-                    'human_prob': human_prob,
-                    'ai_prob': ai_prob,
-                    'prediction': 'human' if human_prob > ai_prob else 'ai',
-                    'confidence': max(human_prob, ai_prob)
-                })
-
-        return {
-            'sentence_predictions': sentence_predictions,
-            'highlighted_text': self.format_predictions_html(sentence_predictions),
-            'full_text': text,
-            'overall_prediction': self.aggregate_predictions(sentence_predictions)
-        }
 
     def format_predictions_html(self, sentence_predictions: List[Dict]) -> str:
         """Format predictions as HTML with color-coding."""