"""
GenerateCodeNode Module
"""
from typing import Any, Dict, List, Optional
from langchain.prompts import PromptTemplate
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_community.chat_models import ChatOllama
import ast
import sys
from io import StringIO
from bs4 import BeautifulSoup
import re
from tqdm import tqdm
from .base_node import BaseNode
from pydantic import ValidationError
from ..utils import (transform_schema,
                    extract_code,
                    syntax_focused_analysis, syntax_focused_code_generation,
                    execution_focused_analysis, execution_focused_code_generation,
                    validation_focused_analysis, validation_focused_code_generation,
                    semantic_focused_analysis, semantic_focused_code_generation,
                    are_content_equal)
from jsonschema import validate, ValidationError
import json
from ..prompts import (
    TEMPLATE_INIT_CODE_GENERATION, TEMPLATE_SEMANTIC_COMPARISON
)

class GenerateCodeNode(BaseNode):
    """
    A node that generates Python code for a function that extracts data from HTML based on a output schema.

    Attributes:
        llm_model: An instance of a language model client, configured for generating answers.
        verbose (bool): A flag indicating whether to show print statements during execution.

    Args:
        input (str): Boolean expression defining the input keys needed from the state.
        output (List[str]): List of output keys to be updated in the state.
        node_config (dict): Additional configuration for the node.
        node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
    """

    def __init__(
        self,
        input: str,
        output: List[str],
        node_config: Optional[dict] = None,
        node_name: str = "GenerateCode",
    ):
        super().__init__(node_name, "node", input, output, 2, node_config)

        self.llm_model = node_config["llm_model"]

        if isinstance(node_config["llm_model"], ChatOllama):
            self.llm_model.format="json"

        self.verbose = (
            True if node_config is None else node_config.get("verbose", False)
        )
        self.force = (
            False if node_config is None else node_config.get("force", False)
        )
        self.script_creator = (
            False if node_config is None else node_config.get("script_creator", False)
        )
        self.is_md_scraper = (
            False if node_config is None else node_config.get("is_md_scraper", False)
        )

        self.additional_info = node_config.get("additional_info")
        
        self.max_iterations = node_config.get("max_iterations", {
            "overall": 10,
            "syntax": 3,
            "execution": 3,
            "validation": 3,
            "semantic": 3
        })
        
        self.output_schema = node_config.get("schema")

    def execute(self, state: dict) -> dict:
        """
        Generates Python code for a function that extracts data from HTML based on a output schema.

        Args:
            state (dict): The current state of the graph. The input keys will be used
                            to fetch the correct data from the state.

        Returns:
            dict: The updated state with the output key containing the generated answer.

        Raises:
            KeyError: If the input keys are not found in the state, indicating
                      that the necessary information for generating an answer is missing.
            RuntimeError: If the maximum number of iterations is reached without obtaining the desired code.
        """
        
        self.logger.info(f"--- Executing {self.node_name} Node ---")

        input_keys = self.get_input_keys(state)
        
        input_data = [state[key] for key in input_keys]
        
        user_prompt = input_data[0]
        refined_prompt = input_data[1]
        html_info = input_data[2]
        reduced_html = input_data[3]
        answer = input_data[4] 
        
        self.raw_html = state['original_html'][0].page_content
        
        simplefied_schema = str(transform_schema(self.output_schema.schema()))
        
        reasoning_state = {
            "user_input": user_prompt,
            "json_schema": simplefied_schema,
            "initial_analysis": refined_prompt,
            "html_code": reduced_html,
            "html_analysis": html_info,
            "generated_code": "",
            "execution_result": None,
            "reference_answer": answer,
            "errors": {
                "syntax": [],
                "execution": [],
                "validation": [],
                "semantic": []
            },
            "iteration": 0
        }
    
    
        final_state = self.overall_reasoning_loop(reasoning_state)
        
        state.update({self.output[0]: final_state["generated_code"]})
        return state
    
    def overall_reasoning_loop(self, state: dict) -> dict:
        self.logger.info(f"--- (Generating Code) ---")
        state["generated_code"] = self.generate_initial_code(state)
        state["generated_code"] = extract_code(state["generated_code"])
        
        while state["iteration"] < self.max_iterations["overall"]:
            state["iteration"] += 1
            if self.verbose:
                self.logger.info(f"--- Iteration {state['iteration']} ---")
            
            self.logger.info(f"--- (Checking Code Syntax) ---")
            state = self.syntax_reasoning_loop(state)
            if state["errors"]["syntax"]:
                continue
            
            self.logger.info(f"--- (Executing the Generated Code) ---")
            state = self.execution_reasoning_loop(state)
            if state["errors"]["execution"]:
                continue
            
            self.logger.info(f"--- (Validate the Code Output Schema) ---")
            state = self.validation_reasoning_loop(state)
            if state["errors"]["validation"]:
                continue
            
            self.logger.info(f"--- (Checking if the informations exctrcated are the ones Requested) ---")
            state = self.semantic_comparison_loop(state)
            if state["errors"]["semantic"]:
                continue            
            break
        
        if state["iteration"] == self.max_iterations["overall"] and (state["errors"]["syntax"] or state["errors"]["execution"] or state["errors"]["validation"] or state["errors"]["semantic"]):
            raise RuntimeError("Max iterations reached without obtaining the desired code.")
        
        self.logger.info(f"--- (Code Generated Correctly) ---")
        
        return state
    
    def syntax_reasoning_loop(self, state: dict) -> dict:
        for _ in range(self.max_iterations["syntax"]):
            syntax_valid, syntax_message = self.syntax_check(state["generated_code"])
            if syntax_valid:
                state["errors"]["syntax"] = []
                return state
            
            state["errors"]["syntax"] = [syntax_message]
            self.logger.info(f"--- (Synax Error Found: {syntax_message}) ---")
            analysis = syntax_focused_analysis(state, self.llm_model)
            self.logger.info(f"--- (Regenerating Code to fix the Error) ---")
            state["generated_code"] = syntax_focused_code_generation(state, analysis, self.llm_model)
            state["generated_code"] = extract_code(state["generated_code"])
        return state
    
    def execution_reasoning_loop(self, state: dict) -> dict:
        for _ in range(self.max_iterations["execution"]):
            execution_success, execution_result = self.create_sandbox_and_execute(state["generated_code"])
            if execution_success:
                state["execution_result"] = execution_result
                state["errors"]["execution"] = []
                return state
            
            state["errors"]["execution"] = [execution_result]
            self.logger.info(f"--- (Code Execution Error: {execution_result}) ---")
            analysis = execution_focused_analysis(state, self.llm_model)
            self.logger.info(f"--- (Regenerating Code to fix the Error) ---")
            state["generated_code"] = execution_focused_code_generation(state, analysis, self.llm_model)
            state["generated_code"] = extract_code(state["generated_code"])
        return state
    
    def validation_reasoning_loop(self, state: dict) -> dict:
        for _ in range(self.max_iterations["validation"]):
            validation, errors = self.validate_dict(state["execution_result"], self.output_schema.schema())
            if validation:
                state["errors"]["validation"] = []
                return state
            
            state["errors"]["validation"] = errors
            self.logger.info(f"--- (Code Output not compliant to the deisred Output Schema) ---")
            analysis = validation_focused_analysis(state, self.llm_model)
            self.logger.info(f"--- (Regenerating Code to make the Output compliant to the deisred Output Schema) ---")
            state["generated_code"] = validation_focused_code_generation(state, analysis, self.llm_model)
            state["generated_code"] = extract_code(state["generated_code"])
        return state
    
    def semantic_comparison_loop(self, state: dict) -> dict:
        for _ in range(self.max_iterations["semantic"]):
            comparison_result = self.semantic_comparison(state["execution_result"], state["reference_answer"])
            if comparison_result["are_semantically_equivalent"]:
                state["errors"]["semantic"] = []
                return state
            
            state["errors"]["semantic"] = comparison_result["differences"]
            self.logger.info(f"--- (The informations exctrcated are not the all ones requested) ---")
            analysis = semantic_focused_analysis(state, comparison_result, self.llm_model)
            self.logger.info(f"--- (Regenerating Code to obtain all the infromation requested) ---")
            state["generated_code"] = semantic_focused_code_generation(state, analysis, self.llm_model)
            state["generated_code"] = extract_code(state["generated_code"])
        return state
    
    def generate_initial_code(self, state: dict) -> str:
        prompt = PromptTemplate(
            template=TEMPLATE_INIT_CODE_GENERATION,
            partial_variables={
                "user_input": state["user_input"],
                "json_schema": state["json_schema"],
                "initial_analysis": state["initial_analysis"],
                "html_code": state["html_code"],
                "html_analysis": state["html_analysis"]
            })

        output_parser = StrOutputParser()

        chain =  prompt | self.llm_model | output_parser
        generated_code = chain.invoke({})
        return generated_code
    
    def semantic_comparison(self, generated_result: Any, reference_result: Any) -> Dict[str, Any]:
        reference_result_dict = self.output_schema(**reference_result).dict()
        
        # Check if generated result and reference result are actually equal
        if are_content_equal(generated_result, reference_result_dict):
            return {
                "are_semantically_equivalent": True,
                "differences": [],
                "explanation": "The generated result and reference result are exactly equal."
            }
        
        response_schemas = [
            ResponseSchema(name="are_semantically_equivalent", description="Boolean indicating if the results are semantically equivalent"),
            ResponseSchema(name="differences", description="List of semantic differences between the results, if any"),
            ResponseSchema(name="explanation", description="Detailed explanation of the comparison and reasoning")
        ]
        output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

        prompt = PromptTemplate(
            template=TEMPLATE_SEMANTIC_COMPARISON,
            input_variables=["generated_result", "reference_result"],
            partial_variables={"format_instructions": output_parser.get_format_instructions()}
        )

        chain = prompt | self.llm_model | output_parser
        return chain.invoke({
            "generated_result": json.dumps(generated_result, indent=2),
            "reference_result": json.dumps(reference_result_dict, indent=2)
        })
    
    def syntax_check(self, code):
        try:
            ast.parse(code)
            return True, "Syntax is correct."
        except SyntaxError as e:
            return False, f"Syntax error: {str(e)}"

    def create_sandbox_and_execute(self, function_code):
        # Create a sandbox environment
        sandbox_globals = {
            'BeautifulSoup': BeautifulSoup,
            're': re,
            '__builtins__': __builtins__,
        }
        
        old_stdout = sys.stdout
        sys.stdout = StringIO()
        
        try:
            exec(function_code, sandbox_globals)
            
            extract_data = sandbox_globals.get('extract_data')
            
            if not extract_data:
                raise NameError("Function 'extract_data' not found in the generated code.")
            
            result = extract_data(self.raw_html)
            
            return True, result
        except Exception as e:
            return False, f"Error during execution: {str(e)}"
        finally:
            sys.stdout = old_stdout
            
    def validate_dict(self, data: dict, schema):
        try:
            validate(instance=data, schema=schema)
            return True, None
        except ValidationError as e:
            errors = e.errors()
            return False, errors