from .prompts import tool_return_prompt , extract_user_reference_prompt , query_response_prompt,captioning_prompt
from langchain_core.messages import SystemMessage, HumanMessage, FunctionMessage
from src.genai.utils.models_loader import llm_gpt
from src.genai.utils.base_endpoint import base_url
from .state import State
from .tools import InfluencerRetrievalTool
from .schemas import ToolResponseFormatter , UserReferenceResponseFormatter
import os
import requests
from groq import Groq

retriever=InfluencerRetrievalTool()

class ImageCaptionNode:
    def __init__(self, api_key=os.environ.get('GROQ_API_KEY')):
        self.client = Groq(api_key=api_key)

    def run(self,state:State):
        if len(state['image_base64'])>0:
            print('Captioning image')
            chat_completion = self.client.chat.completions.create(
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": captioning_prompt(state['messages'])},
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": f"data:image/jpg;base64,{state['image_base64[-1]']}",
                                },
                            },
                        ],
                    }
                ],
                model="meta-llama/llama-4-scout-17b-16e-instruct",
                max_completion_tokens=50,
                temperature = 1
            )
            response=chat_completion.choices[0].message.content
            return {'image_caption': response}
        else:
            print('No image provided')
            return {'image_caption':None}


class ToolReturnNode:
    """Node for determining which tools to use based on user messages."""

    def __init__(self, llm=llm_gpt):
        self.llm = llm

    def run(self, state:State):
        if len(state["messages"]) > 23:
            state["messages"] = state["messages"][-18:]
        template = [SystemMessage(content=tool_return_prompt)] + state["messages"]
        response = self.llm.with_structured_output(ToolResponseFormatter, method='function_calling').invoke(template)
        print('The response is:', response)
        return {"messages": [{'role': 'assistant', 'content': f"Tool invoked: {response.tools}"}],
                "tools":response.tools}
                

class QueryResponseNode:
    def __init__(self):
        self.llm = llm_gpt
    
    def run(self,state:State):
        print('Entered to query response')
        if len(state['tools'])<1:
            print('Going for retrieval')
            retrieved_data=retriever.retrieve_for_orchestration(state['messages'])
            print('The data is retrieved.')
            template = [SystemMessage(content=query_response_prompt),
                        FunctionMessage(name='inf-data-retrieval',content=retrieved_data)] + state["messages"]
            response = self.llm.invoke(template)
            print('Query Response:', response)
            return {"messages": [{'role': 'assistant', 'content': response.content}],
                    "query_response":response.content}
        else:
            return{
                "query_response": f'''Okay i will perform {" ".join(state['tools'])} for you.'''
            }


class ExtractUserReferenceNode:
    """Node for extracting video idea and story from user's messages."""

    def __init__(self, llm=llm_gpt):
        self.llm = llm

    def run(self, state):
        latest_human_message = next(
            (msg for msg in reversed(state['messages']) if isinstance(msg, HumanMessage)),
            None
        )
        print('Latest human message:', latest_human_message)
        template = [SystemMessage(content=extract_user_reference_prompt),
                    HumanMessage(content=latest_human_message.content)]
        response = self.llm.with_structured_output(UserReferenceResponseFormatter, method='function_calling').invoke(template)
        print('The extracted reference:', response)
        return{
            'video_idea': response.video_idea,
            'video_story': response.video_story
        }


class InvokeToolNode:
    def __init__(self):
        self.base_url = base_url
        self.headers = {
            "Authorization": "Bearer YOUR_API_KEY",  # replace with your API key if needed
            "Content-Type": "application/json"
            }

    def run(self,state:State):
        latest_human_message = next(
            (msg for msg in reversed(state['messages']) if isinstance(msg, HumanMessage)),
            None
        )
        data_to_return=[]
        for tool in state['tools']:
            if 'analytics' in tool:
                url = f'''{self.base_url}{tool}'''
                response = requests.get(url, params=latest_human_message.content,headers=self.headers)
                return {
                    'analytics_response':response.json()
                }