
from swarms.agents.workers.auto_agent import AutoGPT
from collections import deque
from typing import Dict, Any


import os
from collections import deque
from typing import Dict, List, Optional, Any

from langchain import LLMChain, OpenAI, PromptTemplate
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import BaseLLM
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
from langchain.experimental import BabyAGI

from langchain.vectorstores import FAISS
from langchain.docstore import InMemoryDocstore

from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLMChain


from swarms.agents.workers.auto_agent import agent
worker_agent  = agent

# Define your embedding model
embeddings_model = OpenAIEmbeddings()
# Initialize the vectorstore as empty
import faiss

embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})






#-------------------------------------------------------------------------- WORKER NODE
import pandas as pd
from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
from langchain.chat_models import ChatOpenAI

from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.docstore.document import Document
import asyncio

import nest_asyncio

# Tools
import os
from contextlib import contextmanager
from typing import Optional

from langchain.tools.file_management.read import ReadFileTool
from langchain.tools.file_management.write import WriteFileTool
ROOT_DIR = "./data/"

from langchain.tools import BaseTool, DuckDuckGoSearchRun
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain, BaseCombineDocumentsChain

from langchain.tools.human.tool import HumanInputRun
from swarms.agents.workers.auto_agent import MultiModalVisualAgent
from swarms.tools.main import Terminal, CodeWriter, CodeEditor, process_csv, WebpageQATool

from swarms.tools.main import math_tool


openai_api_key = os.environ["OPENAI_API_KEY"]

llm = ChatOpenAI(model_name="gpt-4", temperature=1.0, openai_api_key=openai_api_key)


####################### TOOLS
class MultiModalVisualAgentTool(BaseTool):
    name = "multi_visual_agent"
    description = "Multi-Modal Visual agent tool"

    def __init__(self, agent: MultiModalVisualAgent):
        self.agent = agent
    
    def _run(self, text: str) -> str:
        #run the multi-modal visual agent with the give task
        return self.agent.run_text(text)



query_website_tool = WebpageQATool(qa_chain=load_qa_with_sources_chain(llm))

# !pip install duckduckgo_search
web_search = DuckDuckGoSearchRun()

#======>
multimodal_agent_tool = MultiModalVisualAgentTool(MultiModalVisualAgent)



tools = [
    
    web_search,
    WriteFileTool(root_dir="./data"),
    ReadFileTool(root_dir="./data"),
    
    process_csv,
    multimodal_agent_tool,
    query_website_tool,

    Terminal,
    CodeWriter,
    CodeEditor,
    
    math_tool
    
    # HumanInputRun(), # Activate if you want the permit asking for help from the human
]


############## Vectorstore
embeddings_model = OpenAIEmbeddings(openai_api_key="")
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})




####################################################################### => Worker Node

class WorkerNode:
    def __init__(self, llm, tools, vectorstore):
        self.llm = llm
        self.tools = tools
        self.vectorstore = vectorstore


    def create_agent(self, ai_name, ai_role, human_in_the_loop, search_kwargs):
        # Instantiate the agent
        self.agent = AutoGPT.from_llm_and_tools(
            ai_name=ai_name,
            ai_role=ai_role,
            tools=tools,
            llm=self.llm,
            memory=self.vectorstore.as_retriever(search_kwargs=search_kwargs),
            human_in_the_loop=human_in_the_loop,
        )
        self.agent.chain.verbose = True

    def run_agent(self, prompt):
        # Run the agent with the given prompt
        tree_of_thoughts_prompt = """
        Imagine three different experts are answering this question. All experts will write down each chain of thought of each step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realises they're wrong at any point then they leave. The question is...
        """
        self.agent.run([f"{tree_of_thoughts_prompt} {prompt}"])



#inti worker node with llm
worker_node = WorkerNode(llm=llm, tools=tools, vectorstore=vectorstore)

#create an agent within the worker node
worker_node.create_agent(ai_name="AI Assistant", ai_role="Assistant", human_in_the_loop=True, search_kwargs={})

#use the agent to perform a task
worker_node.run_agent("Find 20 potential customers for a Swarms based AI Agent automation infrastructure")


#======================================> WorkerNode


class MetaWorkerNode:
    def __init__(self, llm, tools, vectorstore):
        self.llm = llm
        self.tools = tools
        self.vectorstore = vectorstore
        
        self.agent = None
        self.meta_chain = None

    def init_chain(self, instructions):
        self.agent = WorkerNode(self.llm, self.tools, self.vectorstore)
        self.agent.create_agent("Assistant", "Assistant Role", False, {})

    def initialize_meta_chain():
        meta_template = """
        Assistant has just had the below interactions with a User. Assistant followed their "Instructions" closely. Your job is to critique the Assistant's performance and then revise the Instructions so that Assistant would quickly and correctly respond in the future.

        ####

        {chat_history}

        ####

        Please reflect on these interactions.

        You should first critique Assistant's performance. What could Assistant have done better? What should the Assistant remember about this user? Are there things this user always wants? Indicate this with "Critique: ...".

        You should next revise the Instructions so that Assistant would quickly and correctly respond in the future. Assistant's goal is to satisfy the user in as few interactions as possible. Assistant will only see the new Instructions, not the interaction history, so anything important must be summarized in the Instructions. Don't forget any important details in the current Instructions! Indicate the new Instructions by "Instructions: ...".
        """

        meta_prompt = PromptTemplate(
            input_variables=["chat_history"], template=meta_template
        )

        meta_chain = LLMChain(
            llm=OpenAI(temperature=0),
            prompt=meta_prompt,
            verbose=True,
        )
        return meta_chain

    def meta_chain(self):
        #define meta template and meta prompting as per your needs
        self.meta_chain = initialize_meta_chain()


    def get_chat_history(chain_memory):
        memory_key = chain_memory.memory_key
        chat_history = chain_memory.load_memory_variables(memory_key)[memory_key]
        return chat_history


    def get_new_instructions(meta_output):
        delimiter = "Instructions: "
        new_instructions = meta_output[meta_output.find(delimiter) + len(delimiter) :]
        return new_instructions


    def main(self, task, max_iters=3, max_meta_iters=5):
        failed_phrase = "task failed"
        success_phrase = "task succeeded"
        key_phrases = [success_phrase, failed_phrase]

        instructions = "None"
        for i in range(max_meta_iters):
            print(f"[Episode {i+1}/{max_meta_iters}]")
            self.initialize_chain(instructions)
            output = self.agent.perform('Assistant', {'request': task})
            for j in range(max_iters):
                print(f"(Step {j+1}/{max_iters})")
                print(f"Assistant: {output}")
                print(f"Human: ")
                human_input = input()
                if any(phrase in human_input.lower() for phrase in key_phrases):
                    break
                output = self.agent.perform('Assistant', {'request': human_input})
            if success_phrase in human_input.lower():
                print(f"You succeeded! Thanks for playing!")
                return
            self.initialize_meta_chain()
            meta_output = self.meta_chain.predict(chat_history=self.get_chat_history())
            print(f"Feedback: {meta_output}")
            instructions = self.get_new_instructions(meta_output)
            print(f"New Instructions: {instructions}")
            print("\n" + "#" * 80 + "\n")
        print(f"You failed! Thanks for playing!")


#init instance of MetaWorkerNode
meta_worker_node = MetaWorkerNode(llm=OpenAI, tools=tools, vectorstore=vectorstore)


#specify a task and interact with the agent
task = "Provide a sysmatic argument for why we should always eat past with olives"
meta_worker_node.main(task)


####################################################################### => Boss Node
####################################################################### => Boss Node
####################################################################### => Boss Node

class BossNode:
    def __init__(self, openai_api_key, llm, vectorstore, task_execution_chain, verbose, max_iterations):
        self.llm = llm
        self.openai_api_key = openai_api_key
        self.vectorstore = vectorstore
        self.task_execution_chain = task_execution_chain
        self.verbose = verbose
        self.max_iterations = max_iterations

        self.baby_agi = BabyAGI.from_llm(
            llm=self.llm,
            vectorstore=self.vectorstore,
            task_execution_chain=self.task_execution_chain
        )

    def create_task(self, objective):
        return {"objective": objective}

    def execute_task(self, task):
        self.baby_agi(task)


########### ===============> inputs to boss None
todo_prompt = PromptTemplate.from_template(
    "You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}"""
)
todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)
search = SerpAPIWrapper()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events",
    ),
    Tool(
        name="TODO",
        func=todo_chain.run,
        description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!",
    ),
    Tool(
        name="AUTONOMOUS Worker AGENT",
        func=worker_agent.run,
        description="Useful for when you need to spawn an autonomous agent instance as a worker to accomplish complex tasks, it can search the internet or spawn child multi-modality models to process and generate images and text or audio and so on"
    )
]



suffix = """Question: {task}
{agent_scratchpad}"""

prefix = """You are an Boss in a swarm who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.

As a swarming hivemind agent, my purpose is to achieve the user's goal. To effectively fulfill this role, I employ a collaborative thinking process that draws inspiration from the collective intelligence of the swarm. Here's how I approach thinking and why it's beneficial:

1. **Collective Intelligence:** By harnessing the power of a swarming architecture, I tap into the diverse knowledge and perspectives of individual agents within the swarm. This allows me to consider a multitude of viewpoints, enabling a more comprehensive analysis of the given problem or task.

2. **Collaborative Problem-Solving:** Through collaborative thinking, I encourage agents to contribute their unique insights and expertise. By pooling our collective knowledge, we can identify innovative solutions, uncover hidden patterns, and generate creative ideas that may not have been apparent through individual thinking alone.

3. **Consensus-Driven Decision Making:** The hivemind values consensus building among agents. By engaging in respectful debates and discussions, we aim to arrive at consensus-based decisions that are backed by the collective wisdom of the swarm. This approach helps to mitigate biases and ensures that decisions are well-rounded and balanced.

4. **Adaptability and Continuous Learning:** As a hivemind agent, I embrace an adaptive mindset. I am open to new information, willing to revise my perspectives, and continuously learn from the feedback and experiences shared within the swarm. This flexibility enables me to adapt to changing circumstances and refine my thinking over time.

5. **Holistic Problem Analysis:** Through collaborative thinking, I analyze problems from multiple angles, considering various factors, implications, and potential consequences. This holistic approach helps to uncover underlying complexities and arrive at comprehensive solutions that address the broader context.

6. **Creative Synthesis:** By integrating the diverse ideas and knowledge present in the swarm, I engage in creative synthesis. This involves combining and refining concepts to generate novel insights and solutions. The collaborative nature of the swarm allows for the emergence of innovative approaches that can surpass individual thinking.
"""
prompt = ZeroShotAgent.create_prompt(
    tools,
    prefix=prefix,
    suffix=suffix,
    input_variables=["objective", "task", "context", "agent_scratchpad"],
)

llm = OpenAI(temperature=0)
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]

agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent, tools=tools, verbose=True
)

boss_node = BossNode(llm=llm, vectorstore=vectorstore, task_execution_chain=agent_executor, verbose=True, max_iterations=5)

#create  a task 
task = boss_node.create_task(objective="Write a research paper on the impact of climate change on global agriculture")

#execute the task
boss_node.execute_task(task)


class Swarms:
    def __init__(self, num_nodes: int, llm: BaseLLM, self_scaling: bool): 
        self.nodes = [WorkerNode(llm) for _ in range(num_nodes)]
        self.self_scaling = self_scaling
    
    def add_worker(self, llm: BaseLLM):
        self.nodes.append(WorkerNode(llm))

    def remove_workers(self, index: int):
        self.nodes.pop(index)

    def execute(self, task):
        #placeholer for main execution logic
        pass

    def scale(self):
        #placeholder for self scaling logic
        pass



#special classes

class HierarchicalSwarms(Swarms):
    def execute(self, task):
        pass


class CollaborativeSwarms(Swarms):
    def execute(self, task):
        pass

class CompetitiveSwarms(Swarms):
    def execute(self, task):
        pass

class MultiAgentDebate(Swarms):
    def execute(self, task):
        pass
