app.py

import asyncio
import logging
from typing import Dict, Any
from functools import partial

from flask import Flask, request, jsonify
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader

logging.basicConfig(level=logging.INFO)

# Define core component classes
class Task:
    def __init__(self, task_name: str, input_data: Any, agent_name: str):
        self.task_name = task_name
        self.input_data = input_data
        self.agent_name = agent_name

class ModelManager:
    def __init__(self):
        self.model = None

    async def start(self):
        logging.info("Starting model.")
        await asyncio.sleep(1)  # Simulate loading time

    async def stop(self):
        logging.info("Unloading model.")

class CodeArchitect:
    def __init__(self, model_manager: ModelManager, model=None):
        self.model_manager = model_manager
        self.generator = model if model else pipeline("text-generation", model="gpt2")

    async def start(self):
        await self.model_manager.start()

    async def stop(self):
        await self.model_manager.stop()

    async def generate_code(self, text_input: str) -> str:
        response = self.generator(text_input, max_length=5000, num_return_sequences=1)[0]['generated_text']
        return response

class UIUXWizard:
    def __init__(self, model_manager: ModelManager, vector_store=None):
        self.model_manager = model_manager
        self.vector_store = vector_store
        self.conversation_chain = ConversationChain(
            llm=OpenAI(temperature=0.7),
            memory=ConversationBufferMemory(),
        )

    async def start(self):
        await self.model_manager.start()

    async def stop(self):
        await self.model_manager.stop()

    def get_memory_response(self, query):
        if self.vector_store is None:
            return "No memory available."
        else:
            results = self.vector_store.similarity_search(query, k=3)
            return "\n".join(results)

    def get_conversation_response(self, query):
        return self.conversation_chain.run(query)

# Define VersionControl class
class VersionControl:
    def __init__(self, system_name: str):
        self.system_name = system_name

    async def start(self):
        logging.info(f"Starting version control system: {self.system_name}")
        await asyncio.sleep(1)  # Simulate initialization time

    async def stop(self):
        logging.info(f"Stopping version control system: {self.system_name}")

# Define Documentation class
class Documentation:
    def __init__(self, system_name: str):
        self.system_name = system_name

    async def start(self):
        logging.info(f"Starting documentation system: {self.system_name}")
        await asyncio.sleep(1)  # Simulate initialization time

    async def stop(self):
        logging.info(f"Stopping documentation system: {self.system_name}")

class BuildAutomation:
    def __init__(self, system_name: str):
        self.system_name = system_name

    async def start(self):
        logging.info(f"Starting build automation system: {self.system_name}")
        await asyncio.sleep(1)  # Simulate initialization time

    async def stop(self):
        logging.info(f"Stopping build automation system: {self.system_name}")

# Define EliteDeveloperCluster class
class EliteDeveloperCluster:
    def __init__(self, config: Dict[str, Any], model):
        self.config = config
        self.model_manager = ModelManager()
        self.code_architect = CodeArchitect(self.model_manager, model)
        self.uiux_wizard = UIUXWizard(self.model_manager)
        self.version_control = VersionControl(config["version_control_system"])
        self.documentation = Documentation(config["documentation_system"])
        self.build_automation = BuildAutomation(config["build_automation_system"])
        self.task_queue = asyncio.Queue()

    async def start(self):
        await self.code_architect.start()
        await self.uiux_wizard.start()
        await self.version_control.start()
        await self.documentation.start()
        await self.build_automation.start()

    async def stop(self):
        await self.code_architect.stop()
        await self.uiux_wizard.stop()
        await self.version_control.stop()
        await self.documentation.stop()
        await self.build_automation.stop()

    async def process_task(self, task: Task):
        if task.task_name == "generate_code":
            response = await self.code_architect.generate_code(task.input_data)
            return response
        elif task.task_name == "get_memory_response":
            response = self.uiux_wizard.get_memory_response(task.input_data)
            return response
        elif task.task_name == "get_conversation_response":
            response = self.uiux_wizard.get_conversation_response(task.input_data)
            return response
        else:
            return f"Unknown task: {task.task_name}"

    async def process_tasks(self):
        while True:
            task = await self.task_queue.get()
            response = await self.process_task(task)
            logging.info(f"Processed task: {task.task_name} for agent: {task.agent_name}")
            self.task_queue.task_done()
            yield response

    def route_request(self, query: str) -> str:
        # TODO: Implement logic to determine the appropriate agent based on query
        # For now, assume all requests are for the UIUXWizard
        return self.uiux_wizard.get_conversation_response(query)

# Flask App for handling agent requests
app = Flask(__name__)

@app.route('/agent', methods=['POST'])
def agent_request():
    data = request.get_json()
    if data.get('input_value'):
        # Process request from any agent (Agent 2, Agent 3, etc.)
        task = Task(f"Process request from {data.get('agent_name', 'unknown agent')}", data.get('input_value'), data.get('agent_name', 'unknown agent'))
        cluster.task_queue.put_nowait(task)
        return jsonify({'response': 'Received input: from an agent, task added to queue.'})
    else:
        return jsonify({'response': 'Invalid input'})

# Chat Interface
def get_response(query: str) -> str:
    return cluster.route_request(query)

def response_streaming(text: str):
    try:
        for char in text:
            yield char
    except Exception as e:
        logging.error(f"Error in response streaming: {e}")
        yield "Error occurred while streaming the response."

class ChatApp:
    def __init__(self, cluster: EliteDeveloperCluster):
        self.cluster = cluster

    async def start(self):
        await self.cluster.start()

    async def stop(self):
        await self.cluster.stop()

    async def handle_request(self, query: str) -> str:
        response = await self.cluster.process_tasks()
        return response

# Configuration
config = {
    "version_control_system": "Git",
    "testing_framework": "PyTest",
    "documentation_system": "Sphinx",
    "build_automation_system": "Jenkins",
    "redis_host": "localhost",
    "redis_port": 6379,
    "max_workers": 4,
}

if __name__ == "__main__":
    # Initialize the cluster
    cluster = EliteDeveloperCluster(config, model=None)

    # Start the cluster and task processing loop
    asyncio.run(cluster.start())
    asyncio.run(cluster.process_tasks())

    # Run Flask app
    app.run(debug=True)