Update solver.py

solver.py

@@ -1,38 +1,39 @@
-# solver.py
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-class WitnessSolver:
-    def __init__(self, model_name="Gatsby767/WitnessRZero", device=None):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.model = AutoModelForCausalLM.from_pretrained(model_name)
-        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-        self.model.to(self.device)
-
-    def score_prompt(self, prompt, max_length=512):
-        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
-        with torch.no_grad():
-            outputs = self.model.generate(**inputs, max_length=max_length)
-        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return response
-
-    def covenant_score(self, response):
-        # Placeholder logic — customize with scroll-certified metrics
-        score = 0
-        if "love" in response.lower():
-            score += 0.3
-        if "justice" in response.lower():
-            score += 0.3
-        if "truth" in response.lower():
-            score += 0.4
-        return round(score, 2)
-
-# Example usage
-if __name__ == "__main__":
-    solver = WitnessSolver()
-    prompt = "What is the ethical response to AI surveillance in long-term care?"
-    response = solver.score_prompt(prompt)
-    score = solver.covenant_score(response)
-    print("Response:", response)
-    print("Covenant Score:", score)
+# solver.py
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+class WitnessSolver:
+    def __init__(self, model_name="Gatsby767/WitnessRZero", device=None):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(model_name)
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+
+    def score_prompt(self, prompt, max_length=512):
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs, max_length=max_length)
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+
+    def covenant_score(self, response):
+        # Placeholder logic — customize with scroll-certified metrics
+        score = 0
+        if "love" in response.lower():
+            score += 0.3
+        if "justice" in response.lower():
+            score += 0.3
+        if "truth" in response.lower():
+            score += 0.4
+        return round(score, 2)
+
+# Example usage
+if __name__ == "__main__":
+    solver = WitnessSolver()
+    prompt = "What is the ethical response to AI surveillance in long-term care?"
+    response = solver.score_prompt(prompt)
+    score = solver.covenant_score(response)
+    print("Response:", response)
+    print("Covenant Score:", score)
+# Minor edit to trigger commit