#!/bin/bash

# Variables
REPO_URL="https://github.com/your-repo/mixture_of_agents.git"
PROJECT_DIR="mixture_of_agents"
PYTHON_VERSION="python3"
VENV_DIR="venv"
REQUIREMENTS_FILE="requirements.txt"

# Clone the repository
git clone $REPO_URL
cd $PROJECT_DIR

# Create a virtual environment
$PYTHON_VERSION -m venv $VENV_DIR

# Activate the virtual environment
source $VENV_DIR/bin/activate

# Create requirements.txt
cat <<EOL > $REQUIREMENTS_FILE
flask
transformers
datasets
numpy
pandas
EOL

# Install required libraries
pip install -r $REQUIREMENTS_FILE

# Create necessary directories
mkdir -p agents integration model dataset

# Create agent files
cat <<EOL > agents/front_end_agent.py
class FrontEndAgent:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    def process(self, task_data):
        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
        outputs = self.model.generate(**inputs)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
EOL

cat <<EOL > agents/back_end_agent.py
class BackEndAgent:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    def process(self, task_data):
        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
        outputs = self.model.generate(**inputs)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
EOL

cat <<EOL > agents/database_agent.py
class DatabaseAgent:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    def process(self, task_data):
        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
        outputs = self.model.generate(**inputs)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
EOL

cat <<EOL > agents/devops_agent.py
class DevOpsAgent:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    def process(self, task_data):
        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
        outputs = self.model.generate(**inputs)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
EOL

cat <<EOL > agents/project_management_agent.py
class ProjectManagementAgent:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    def process(self, task_data):
        inputs = self.tokenizer(task_data['task'], return_tensors='pt')
        outputs = self.model.generate(**inputs)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
EOL

# Create integration layer
cat <<EOL > integration/integration_layer.py
class IntegrationLayer:
    def __init__(self, front_end_agent, back_end_agent, database_agent, devops_agent, project_management_agent):
        self.agents = {
            'front_end': front_end_agent,
            'back_end': back_end_agent,
            'database': database_agent,
            'devops': devops_agent,
            'project_management': project_management_agent
        }

    def process_task(self, task_type, task_data):
        if task_type in self.agents:
            return self.agents[task_type].process(task_data)
        else:
            raise ValueError("Unknown task type")
EOL

# Create model files
cat <<EOL > model/load_pretrained_model.py
from transformers import AutoModelForCausalLM, AutoTokenizer

def load_model_and_tokenizer():
    model_name = "gpt-3"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return model, tokenizer
EOL

cat <<EOL > model/fine_tune_model.py
from datasets import load_dataset
from transformers import Trainer, TrainingArguments

def fine_tune_model(model, tokenizer, dataset_path):
    dataset = load_dataset('json', data_files=dataset_path)
    
    def preprocess_function(examples):
        return tokenizer(examples['input'], truncation=True, padding=True)

    tokenized_datasets = dataset.map(preprocess_function, batched=True)

    training_args = TrainingArguments(
        output_dir="./results",
        evaluation_strategy="epoch",
        learning_rate=2e-5,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        num_train_epochs=3,
        weight_decay=0.01,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_datasets['train'],
        eval_dataset=tokenized_datasets['validation']
    )

    trainer.train()
EOL

# Create dataset file
cat <<EOL > dataset/code_finetune_dataset.json
[
  {
    "task": "front_end",
    "input": "Create a responsive HTML layout with CSS",
    "output": "<!DOCTYPE html><html><head><style>body {margin: 0; padding: 0;}</style></head><body><div class='container'></div></body></html>"
  },
  {
    "task": "back_end",
    "input": "Develop a REST API endpoint in Node.js",
    "output": "const express = require('express'); const app = express(); app.get('/api', (req, res) => res.send('Hello World!')); app.listen(3000);"
  }
]
EOL

# Create app.py
cat <<EOL > app.py
from flask import Flask, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
from agents.front_end_agent import FrontEndAgent
from agents.back_end_agent import BackEndAgent
from agents.database_agent import DatabaseAgent
from agents.devops_agent import DevOpsAgent
from agents.project_management_agent import ProjectManagementAgent
from integration.integration_layer import IntegrationLayer

app = Flask(__name__)

# Load the model and tokenizer
model_name = "gpt-3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Initialize agents
front_end_agent = FrontEndAgent(model, tokenizer)
back_end_agent = BackEndAgent(model, tokenizer)
database_agent = DatabaseAgent(model, tokenizer)
devops_agent = DevOpsAgent(model, tokenizer)
project_management_agent = ProjectManagementAgent(model, tokenizer)
integration_layer = IntegrationLayer(front_end_agent, back_end_agent, database_agent, devops_agent, project_management_agent)

@app.route('/')
def home():
    return "Welcome to the Mixture of Agents Model API!"

@app.route('/process', methods=['POST'])
def process_task():
    data = request.json
    task_type = data.get('task_type')
    task_data = data.get('task_data')
    
    if not task_type or not task_data:
        return jsonify({"error": "task_type and task_data are required"}), 400

    try:
        result = integration_layer.process_task(task_type, task_data)
        return jsonify({"result": result})
    except ValueError as e:
        return jsonify({"error": str(e)}), 400

if __name__ == '__main__':
    app.run(debug=True)
EOL

# Provide instructions for running the app
echo -e "\nSetup complete. To run the application:\n"
echo "1. Activate the virtual environment:"
echo "   source $VENV_DIR/bin/activate"
echo "2. Start the Flask application:"
echo "   python app.py"
chmod +x setup.sh
./setup.sh