import os
import pandas as pd
from .state import State , ValidationFormatter , CriticResponseFormatter
from .tools import Retrieval
from langgraph.prebuilt import create_react_agent
from src.genai.utils.models_loader import ideator_llm, critic_llm , normalizer_llm , validator_llm , judge1_llm , judge2_llm , simplifier_llm , moderator_llm
from langchain_core.messages import SystemMessage , HumanMessage, FunctionMessage
from .prompts import ideator_prompt ,critic_prompt, moderator_prompt , validator_prompt, judge_prompt, simplifier_prompt
from .schemas import ideation_json_schema , judge_response_json_schema



class RetrieverNode:
    def __init__(self):
        pass
    
    def run(self , state:State):
        influencers_data = 'Nothing.'
        # influencers_data = Retrieval(state.business_details[-1]).influencers_data()
        state.influencers_data.append(influencers_data)
        print('Retriever Node completed...')
        # imdb_data = Retrieval(state.business_details[-1]).imdb_ideas()
        # state.imdb_data.append(imdb_data)
        return state

class IdeatorNode:
    def __init__(self):
        self.llm = ideator_llm
    
    def run(self, state:State):
        template = ideator_prompt()
        messages = [SystemMessage(content=template),
                    HumanMessage(content=f'''The business_details is\n{state.business_details[-1]}\n 
                    The information of the image is:\n{state.image_caption[-1]}'''),]
                    # FunctionMessage(name='imdb_ideas_function', content=f'''The data of imdb movies description is:\n {state.imdb_data[-1]}\n''')]
        response = self.llm.invoke(messages)
        print('Ideator Response:', response.content)
        print('The scores are:',state.scores[-1])
        state.ideator_response.append(str(response.content))
        return state

class ModeratorNode:
    def __init__(self):
        self.llm = moderator_llm

    def run(self, state:State):
        template = moderator_prompt()
        messages = [SystemMessage(content=template),
                    HumanMessage(content=f'''The ideas generated by ideator are:\n{state.ideator_response[-1]}\n''' ),
                    FunctionMessage(name='moderator',content=f'''The scores are: \n {str(state.scores[-1])}''')]
        response = self.llm.invoke(messages)
        state.moderator_response.append(str(response.content))
        print('Moderator Response:', state.moderator_response[-1])
        return state

        
class SimplifierNode:
    def __init__(self):
        self.llm = simplifier_llm
    
    def run(self, state:State):
        template = simplifier_prompt()
        messages = [SystemMessage(content=template), HumanMessage(content=f'''The ideas generated by ideator are:\n{state.moderator_response[-1]}\n''')]
        response = self.llm.invoke(messages)
        print('Simplifier Response:', response.content)
        state.simplifier_response.append(str(response.content))
        df = pd.read_csv('src/genai/utils/ideas/ideas.csv')
        df = pd.concat([df, pd.DataFrame({
            'BusinessDetails': [state.business_details[-1]],
            'Ideas': [state.simplifier_response[-1]]
            })], ignore_index=True)
        df.to_csv('src/genai/utils/ideas/ideas.csv')
        print('Ideator Node executed')
        return state


class CriticNode:
    def __init__(self):
        self.llm = critic_llm
    
    def run(self,state:State):
        template = critic_prompt()
        messages = [SystemMessage(content=template),
                    HumanMessage(content=f'''The ideas generated by ideator are:\n{state.ideator_response[-1]}\n.
                                 The business_details is\n{state.business_details[-1]}\n 
                                The information of the image is:\n{state.image_caption[-1]}'''),]
                    # FunctionMessage(name='imdb_ideas_function', content=f'''The data of imdb movies description is:\n {state.imdb_data[-1]}\n''')]
        
        response = self.llm.invoke(messages)
        state.critic_response.append(str(response.content))
        print('Critic Response:', response.content)
        print('Critic Node executed')
        return state
    
class NormalizerNode:
    def __init__(self):
        self.llm = normalizer_llm
    
    def run(self, state:State):
        response = self.llm.with_structured_output(ideation_json_schema).invoke(str(state.simplifier_response[-1]))
        state.normalizer_response.append(response)
        print('Normalizer Executed')
        return state

    
class Judge:
    def __init__(self, llm):
        self.llm = llm
    
    def run (self, state:State):
        template = judge_prompt(state)
        messages = [SystemMessage(content=template),
                    HumanMessage(content=f'''The generated 10 ideas are:\n{state.normalizer_response[-1]}\n.
                    The business_details is\n{state.business_details[-1]}\n
                    The information of image is:{state.image_caption[-1]}\n''')]
        response = self.llm.with_structured_output(judge_response_json_schema).invoke(messages)
        return response

class JudgeNode1:
    def __init__(self):
        self.llm = judge1_llm

    def run (self, state:State):
        response = Judge(self.llm).run(state)
        return {'judge1_response':[response]}

class JudgeNode2:
    def __init__(self):
        self.llm = judge2_llm
    
    def run(self, state:State):
        response = Judge(self.llm).run(state)
        return {'judge2_response':[response]}


class Aggregrator:
    def __init__(self):
        self.unique_ideas = {}

    def run(self, state: State):
        # Combine ideas from both judges
        all_selected_ideas = [
            *state.judge1_response[-1]['selected_ideas'],
            *state.judge2_response[-1]['selected_ideas']
        ]

        print('All selected ideas:', all_selected_ideas)

        # Keep only unique ideas by title
        for idea in all_selected_ideas:
            title = idea['title']
            # If title not already added, store it
            if title not in self.unique_ideas:
                self.unique_ideas[title] = idea

        # Convert to list
        unique_ideas_list = list(self.unique_ideas.values())

        # Save unique ideas to state
        state.unique_selected_ideas.append(unique_ideas_list)


        return state

class ValidatorNode:
    def __init__(self):
        self.validator_llm1 = validator_llm
        self.validator_llm2 = validator_llm

    def get_response(self,state, validator_llm):
        template = validator_prompt(state)
        messages = [SystemMessage(content=template),
                    HumanMessage(content=f'''The business_details is:\n{state.business_details[-1]}''')]

        response = validator_llm.with_structured_output(ValidationFormatter).invoke(messages)
        return response
    
    
    def run(self, state:State): 
        response = self.get_response(state,self.validator_llm1)
        state.validator_response.append(response.result)
        if 'not validated' in response.result: state.disagreement_reason.append(response.reason)
        return state


class RoutingAfterValidation:
    def __init__(self):
        pass

    def route(self, state:State):
        return 'not validated' not in state.validator_response[-1]