import time
from services.query_decomposition import QueryDecomposerService
from services.graph_planner import GraphPlannerService
from services.hybrid_retrieval import HybridRetrievalService
from services.context_organization import ContextOrganizationService
from services.legal_reasoner import LegalReasonerService
from services.quality_assurance import QualityAssuranceService
from services.neo4j import get_neo4j_driver
from core.search import SearchService
import json

class ConstitutionWorkflow:
    def __init__(self):
        self.decomposer = QueryDecomposerService()
        self.planner = GraphPlannerService()
        self.retriever_coordinator = HybridRetrievalService()
        self.context_organizer = ContextOrganizationService()
        self.reasoner = LegalReasonerService()
        self.qa_agent = QualityAssuranceService()
        
        self.neo4j = get_neo4j_driver()
        self.vector_search = SearchService() 

    def execute_neo4j(self, queries):
        results = []
        try:
            with self.neo4j.session() as session:
                for q in queries:
                    if "DELETE" in q or "DETACH" in q: continue 
                    res = session.run(q)
                    results.extend([r.data() for r in res])
        except Exception as e:
            print(f"Neo4j Execution Error: {e}")
        return results

    def run_flow(self, query: str) -> dict:
        start_time = time.time()
        
        # State Initialization
        state = {
            "query": query,
            "trace": [],
            "graph_results": [],
            "retrieval_plan": {},
            "retrieved_chunks": [],
            "context_data": {},
            "answer": {},
            "critique": {},
            "retry_count": 0,
            "max_retries": 1
        }

        # 1. CLASSIFY (Run once)
        state["trace"].append("Agent 1: Classification")
        classification = self.decomposer.decompose(query)
        print(f"DEBUG: Intent={classification.get('intent')}")
        
        # 2. PLAN (Run once)
        state["trace"].append("Agent 2: Graph Planning")
        plan = self.planner.generate_plan(classification)
        state["graph_results"] = self.execute_neo4j(plan.get("cypher_queries", []))
        print(f"DEBUG: Found {len(state['graph_results'])} graph entities")

        while state["retry_count"] <= state["max_retries"]:
            # 3. RETRIEVE
            state["trace"].append(f"Agent 3: Hybrid Retrieval (Attempt {state['retry_count']+1})")
            
            # If we are retrying and have specific feedback, mix it in
            search_query = query
            if state["retry_count"] > 0 and state["critique"].get("suggested_retrieval"):
                 # In a full complex system, we'd execute the Cypher suggestion.
                 # For now, we append the suggestion to the search query to bias the vector search
                 # or simply re-run vector search with the original query if logic was just "missed chunks".
                 # Better: Use missing entities to boost.
                 missing = state["critique"].get("missing_entities", {})
                 if missing.get("amendments"):
                     search_query += f" amendment {missing['amendments']}"
            
            # Fetch candidates (broaden search if retrying)
            limit = 20 if state["retry_count"] == 0 else 40
            raw_chunks = self.vector_search.search(search_query, limit=limit)
            
            # Coordinate & Rerank
            retrieval_output = self.retriever_coordinator.coordinate_retrieval(
                query, 
                state["graph_results"], 
                raw_chunks
            )
            state["retrieved_chunks"] = retrieval_output.get("final_selected_chunks", [])
            
            # 4. ORGANIZE
            state["trace"].append("Agent 4: Context Organization")
            # Extract the raw chunk dicts from the rerank result wrapper
            chunk_data = [c["full_chunk"] for c in state["retrieved_chunks"] if "full_chunk" in c]
            if not chunk_data: chunk_data = raw_chunks[:10] # Fallback
            
            state["context_data"] = self.context_organizer.organize_context(
                query, 
                chunk_data, 
                state["graph_results"]
            )

            # 5. REASON
            state["trace"].append("Agent 5: Legal Reasoning")
            state["answer"] = self.reasoner.generate_answer(query, state["context_data"])

            # 6. CRITIQUE
            state["trace"].append("Agent 6: Quality Assurance")
            state["critique"] = self.qa_agent.validate_answer(query, state["answer"], state["graph_results"])
            
            if state["critique"].get("quality_grade") == "PASS":
                break
            
            if state["critique"].get("quality_grade") == "REFINE":
                print(f"DEBUG: QA requested refinement. confidence={state['critique'].get('final_confidence')}")
                state["retry_count"] += 1
            else:
                # Review or unknown, usually accept but note it
                break

        return {
            "query": query,
            "answer": state["answer"].get("answer"),
            "constitutional_status": state["answer"].get("constitutional_status"),
            "confidence": state["critique"].get("final_confidence"),
            "sources": state["answer"].get("sources"),
            "quality_grade": state["critique"].get("quality_grade"),
            "execution_trace": state["trace"],
            "time_taken": time.time() - start_time
        }

    def close(self):
        self.neo4j.close()