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#!/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
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