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mock_timbl.py

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+"""
+Mock TiMBL classifier for demonstration purposes.
+
+This mock implementation shows that the HuggingFace integration architecture
+works correctly. Replace this with real TiMBL backend for production use.
+"""
+
+import random
+from typing import Tuple
+
+
+class MockTimblClassifier:
+    """
+    Mock TiMBL classifier that returns plausible predictions.
+
+    This is for demonstration only. In production, use:
+    - python3-timbl Python bindings
+    - Working TiMBL CLI wrapper
+    - TiMBL server client
+    """
+
+    def __init__(self, fileprefix: str, timbloptions: str, format: str = "Tabbed"):
+        self.fileprefix = fileprefix
+        self.timbloptions = timbloptions
+        self.format = format
+        self.ibase_loaded = False
+
+        # Common English tokens for mock predictions
+        self.common_tokens = [
+            "the", ".", "and", "to", "of", "a", "in", "that", "is", "for",
+            "it", "with", "as", "was", "on", "be", "by", "at", "from", "this"
+        ]
+
+    def load(self):
+        """Simulate loading an instance base."""
+        print(f"[MOCK] Simulating load of {self.fileprefix}.ibase")
+        self.ibase_loaded = True
+
+    def classify(self, features: list, allowtopdistribution: bool = True) -> Tuple[str, str, float]:
+        """
+        Mock classification that returns plausible results.
+
+        Returns:
+            (predicted_token, distribution_string, distance)
+        """
+        if not self.ibase_loaded:
+            raise RuntimeError("Instance base not loaded")
+
+        # Choose a plausible prediction based on context
+        # This is completely fake but demonstrates the interface
+
+        # Look at the last context token
+        last_token = features[-1] if features and features[-1] != '_' else None
+
+        # Simple heuristic for more realistic mock predictions
+        if last_token and last_token.lower() in ['the', 'a', 'an']:
+            # After article, predict a noun-ish token
+            candidates = ['man', 'woman', 'house', 'cat', 'dog', 'book', 'world']
+        elif last_token and last_token in ['.', '!', '?']:
+            # After punctuation, predict capital word
+            candidates = ['The', 'I', 'He', 'She', 'It', 'We', 'They']
+        else:
+            candidates = self.common_tokens
+
+        # Pick top prediction
+        predicted = random.choice(candidates)
+
+        # Generate mock distribution (would come from k-NN in real TiMBL)
+        num_neighbors = random.randint(3, 7)
+        distribution_parts = []
+
+        # Top prediction gets highest score
+        distribution_parts.append(f"{predicted} 0.{random.randint(40, 70)}")
+
+        # Add some runner-ups
+        for _ in range(num_neighbors - 1):
+            token = random.choice([t for t in candidates if t != predicted])
+            score = f"0.{random.randint(5, 30):02d}"
+            distribution_parts.append(f"{token} {score}")
+
+        distribution = ", ".join(distribution_parts)
+
+        # Distance (0 = exact match, 1 = very different)
+        distance = random.uniform(0.1, 0.6)
+
+        return (predicted, distribution, distance)
+
+    def append(self, features: list, classlabel: str):
+        """
+        Simulate appending an instance.
+
+        Note: In production, this would add to the instance base.
+        For mock, we just log it.
+        """
+        print(f"[MOCK] Would append: {features} -> {classlabel}")