# processor.py

import logging
import hashlib
import time
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Dict, List

import requests
from langchain_core.messages import AIMessage

from config import ResearchConfig
from knowledge_graph import QuantumKnowledgeGraph

logger = logging.getLogger(__name__)

class CognitiveProcessor:
    """
    Executes API requests to the backend using triple redundancy and consolidates results via a consensus mechanism.
    """
    def __init__(self) -> None:
        self.executor = ThreadPoolExecutor(max_workers=ResearchConfig.MAX_CONCURRENT_REQUESTS)
        self.session_id = hashlib.sha256(datetime.now().isoformat().encode()).hexdigest()[:12]

    def process_query(self, prompt: str) -> Dict:
        futures = [self.executor.submit(self._execute_api_request, prompt) for _ in range(3)]
        results = []
        for future in as_completed(futures):
            try:
                results.append(future.result())
            except Exception as e:
                logger.exception("Error during API request execution.")
        return self._consensus_check(results)

    def _execute_api_request(self, prompt: str) -> Dict:
        headers = {
            "Authorization": f"Bearer {ResearchConfig.DEEPSEEK_API_KEY}",
            "Content-Type": "application/json",
            "X-Research-Session": self.session_id
        }
        payload = {
            "model": "deepseek-chat",
            "messages": [{
                "role": "user",
                "content": f"Respond as a Senior AI Researcher and Technical Writer:\n{prompt}"
            }],
            "temperature": 0.7,
            "max_tokens": 1500,
            "top_p": 0.9
        }
        try:
            response = requests.post(
                "https://api.deepseek.com/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=45
            )
            response.raise_for_status()
            logger.info("Backend API request successful.")
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.exception("Backend API request failed.")
            return {"error": str(e)}

    def _consensus_check(self, results: List[Dict]) -> Dict:
        valid_results = [r for r in results if "error" not in r]
        if not valid_results:
            logger.error("All API requests failed.")
            return {"error": "All API requests failed"}
        # Choose the result with the longest response content as a simple consensus metric
        return max(valid_results, key=lambda x: len(x.get('choices', [{}])[0].get('message', {}).get('content', '')))

class EnhancedCognitiveProcessor(CognitiveProcessor):
    """
    Extends CognitiveProcessor with ensemble processing and knowledge graph integration.
    """
    def __init__(self) -> None:
        super().__init__()
        self.knowledge_graph = QuantumKnowledgeGraph()
        self.ensemble_models = ["deepseek-chat", "deepseek-coder"]

    def process_query(self, prompt: str) -> Dict:
        futures = [self.executor.submit(self._execute_api_request, prompt, model) for model in self.ensemble_models]
        results = []
        for future in as_completed(futures):
            try:
                results.append(future.result())
            except Exception as e:
                logger.error(f"Model processing error: {str(e)}")
        best_response = self._consensus_check(results)
        self._update_knowledge_graph(best_response)
        return best_response

    def _execute_api_request(self, prompt: str, model: str) -> Dict:
        headers = {
            "Authorization": f"Bearer {ResearchConfig.DEEPSEEK_API_KEY}",
            "Content-Type": "application/json",
            "X-Research-Session": self.session_id
        }
        payload = {
            "model": model,
            "messages": [{
                "role": "user",
                "content": f"Respond as a Senior AI Researcher and Technical Writer:\n{prompt}"
            }],
            "temperature": ResearchConfig.ENSEMBLE_MODELS[model]["temp"],
            "max_tokens": ResearchConfig.ENSEMBLE_MODELS[model]["max_tokens"],
            "top_p": 0.9
        }
        try:
            response = requests.post(
                "https://api.deepseek.com/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=45
            )
            response.raise_for_status()
            logger.info(f"API request successful for model {model}.")
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.exception(f"API request failed for model {model}.")
            return {"error": str(e)}

    def _update_knowledge_graph(self, response: Dict) -> None:
        content = response.get('choices', [{}])[0].get('message', {}).get('content', '')
        node_id = self.knowledge_graph.create_node({"content": content}, "analysis")
        if self.knowledge_graph.node_counter > 1:
            self.knowledge_graph.create_relation(node_id - 1, node_id, "evolution", strength=0.8)