"""
This module contains the functions that are used to generate the prompts for the code error analysis.
"""
from typing import Any, Dict
import json
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from ..prompts import (
    TEMPLATE_SYNTAX_ANALYSIS, TEMPLATE_EXECUTION_ANALYSIS,
    TEMPLATE_VALIDATION_ANALYSIS, TEMPLATE_SEMANTIC_ANALYSIS
)

def syntax_focused_analysis(state: dict, llm_model) -> str:
    prompt = PromptTemplate(template=TEMPLATE_SYNTAX_ANALYSIS,
                            input_variables=["generated_code", "errors"])
    chain = prompt | llm_model | StrOutputParser()
    return chain.invoke({
        "generated_code": state["generated_code"],
        "errors": state["errors"]["syntax"]
    })

def execution_focused_analysis(state: dict, llm_model) -> str:
    prompt = PromptTemplate(template=TEMPLATE_EXECUTION_ANALYSIS,
                            input_variables=["generated_code", "errors",
                                              "html_code", "html_analysis"])
    chain = prompt | llm_model | StrOutputParser()
    return chain.invoke({
        "generated_code": state["generated_code"],
        "errors": state["errors"]["execution"],
        "html_code": state["html_code"],
        "html_analysis": state["html_analysis"]
    })

def validation_focused_analysis(state: dict, llm_model) -> str:
    prompt = PromptTemplate(template=TEMPLATE_VALIDATION_ANALYSIS,
                            input_variables=["generated_code", "errors", 
                                             "json_schema", "execution_result"])
    chain = prompt | llm_model | StrOutputParser()
    return chain.invoke({
        "generated_code": state["generated_code"],
        "errors": state["errors"]["validation"],
        "json_schema": state["json_schema"],
        "execution_result": state["execution_result"]
    })

def semantic_focused_analysis(state: dict, comparison_result: Dict[str, Any], llm_model) -> str:        
    prompt = PromptTemplate(template=TEMPLATE_SEMANTIC_ANALYSIS,
                            input_variables=["generated_code", 
                                             "differences", "explanation"])
    chain = prompt | llm_model | StrOutputParser()
    return chain.invoke({
        "generated_code": state["generated_code"],
        "differences": json.dumps(comparison_result["differences"], indent=2),
        "explanation": comparison_result["explanation"]
    })