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# 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
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