Add "interactive.py"

Interactive.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+class MetaphorScorer:
+    def __init__(self, model_path='.'):
+        """
+        Initialize the metaphor scorer.
+        
+        Args:
+            model_path: Path or Hugging Face repo ID. 
+                       Default '.' uses current directory (where model files are)
+                       Or use 'your-username/Metaphor_Scoring_Model' to load from Hub
+        """
+            
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        
+        print(f"Loading model from: {model_path}")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
+        self.model.to(self.device)
+        self.model.eval()
+        print(f"Model loaded on {self.device}")
+    
+    def score_sentence(self, sentence):
+        """
+        Score a sentence for metaphorical novelty.
+        
+        Args:
+            sentence: Input sentence to score
+            
+        Returns:
+            score: Novelty score (1-4)
+            confidence: Model confidence (0-1)
+        """
+        inputs = self.tokenizer(
+            sentence,
+            return_tensors='pt',
+            max_length=256,
+            truncation=True,
+            padding='max_length'
+        )
+        
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            logits = outputs.logits
+            predicted_class = torch.argmax(logits, dim=-1).item()
+            score = predicted_class + 1
+            probabilities = torch.softmax(logits, dim=-1)
+            confidence = probabilities[0][predicted_class].item()
+            
+        return score, confidence
+
+def main():
+    # Load model (will use current directory by default)
+    scorer = MetaphorScorer()
+    
+    print("\n=== Metaphorical Sentence Scorer ===")
+    print("Enter metaphorical sentences to get novelty scores (1-4)")
+    print("Higher scores = Higher metaphorical novelty")
+    print("Type 'quit' to exit\n")
+    
+    while True:
+        sentence = input("Enter sentence: ").strip()
+        
+        if sentence.lower() in ['quit', 'exit', 'q']:
+            print("Goodbye!")
+            break
+        
+        if not sentence:
+            continue
+            
+        score, confidence = scorer.score_sentence(sentence)
+        print(f"Score: {score}/4 (confidence: {confidence:.3f})\n")
+
+if __name__ == "__main__":
+    main()