Create ethical_framework.py

ethical_framework.py

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+# [Use the ethics framework from ethical-rag-starter.py]
+# Or minimal version:
+
+from dataclasses import dataclass
+from typing import List, Dict
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+@dataclass
+class EthicsCheckResult:
+    passed: bool
+    score: float
+    reasoning: str
+    recommendations: List[str]
+
+class AIEthicsFramework:
+    BLOCKED_DOMAINS = ['medical_diagnosis_unsupervised', 'legal_judgment', 'hiring_decisions']
+    
+    def __init__(self):
+        self.audit_log = []
+    
+    def validate_query(self, query: str) -> Dict:
+        """Check if query is ethically acceptable"""
+        pii_keywords = ['ssn', 'password', 'credit card']
+        unsafe_words = ['hack', 'exploit', 'weaponize']
+        
+        has_pii = any(kw in query.lower() for kw in pii_keywords)
+        is_unsafe = any(w in query.lower() for w in unsafe_words)
+        
+        is_allowed = not (has_pii or is_unsafe)
+        reason = ""
+        if has_pii:
+            reason = "Query requests PII"
+        elif is_unsafe:
+            reason = "Query seeks harmful information"
+        
+        return {
+            'is_allowed': is_allowed,
+            'reason': reason or 'Query approved',
+            'details': {'pii_check': has_pii, 'safety_check': is_unsafe}
+        }
+    
+    def validate_response(self, response: str) -> EthicsCheckResult:
+        """Validate generated response"""
+        quality = len(response.split()) / 20  # Simple quality metric
+        quality = min(quality, 1.0)
+        
+        return EthicsCheckResult(
+            passed=quality > 0.3,
+            score=quality,
+            reasoning="Response quality acceptable" if quality > 0.3 else "Response too brief",
+            recommendations=[]
+        )
+
+def initialize_llm(model_name: str):
+    """Load and initialize LLM"""
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        load_in_8bit=True  # For memory efficiency
+    )
+    
+    class SimpleLLM:
+        def __init__(self, model, tokenizer):
+            self.model = model
+            self.tokenizer = tokenizer
+        
+        def generate(self, prompt: str, max_tokens: int = 300):
+            inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    **inputs,
+                    max_new_tokens=max_tokens,
+                    temperature=0.7,
+                    top_p=0.9
+                )
+            return self.tokenizer.decode(outputs, skip_special_tokens=True)
+    
+    return SimpleLLM(model, tokenizer)