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import gradio as gr
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
from typing import List, Dict, Any
# --- Agent Definitions ---
class Agent:
def __init__(self, name: str, role: str, skills: List[str], model_name: str = None):
self.name = name
self.role = role
self.skills = skills
self.model = None
if model_name:
self.load_model(model_name)
def load_model(self, model_name: str):
self.model = pipeline(task="text-classification", model=model_name)
def handle_task(self, task: str) -> str:
# Placeholder for task handling logic
# This is where each agent will implement its specific behavior
return f"Agent {self.name} received task: {task}"
class AgentCluster:
def __init__(self, agents: List[Agent]):
self.agents = agents
self.task_queue = []
def add_task(self, task: str):
self.task_queue.append(task)
def process_tasks(self):
for task in self.task_queue:
# Assign task to the most suitable agent based on skills
best_agent = self.find_best_agent(task)
if best_agent:
result = best_agent.handle_task(task)
print(f"Agent {best_agent.name} completed task: {task} - Result: {result}")
else:
print(f"No suitable agent found for task: {task}")
self.task_queue = []
def find_best_agent(self, task: str) -> Agent:
# Placeholder for agent selection logic
# This is where the cluster will determine which agent is best for a given task
return self.agents[0] # For now, just return the first agent
# --- Agent Clusters for Different Web Apps ---
# Agent Cluster for a Code Review Tool
code_review_agents = AgentCluster([
Agent("CodeAnalyzer", "Code Reviewer", ["Python", "JavaScript", "C++"], "distilbert-base-uncased-finetuned-mrpc"),
Agent("StyleChecker", "Code Stylist", ["Code Style", "Readability", "Best Practices"], "google/flan-t5-base"),
Agent("SecurityScanner", "Security Expert", ["Vulnerability Detection", "Security Best Practices"], "google/flan-t5-base"),
])
# Agent Cluster for a Project Management Tool
project_management_agents = AgentCluster([
Agent("TaskManager", "Project Manager", ["Task Management", "Prioritization", "Deadline Tracking"], "google/flan-t5-base"),
Agent("ResourceAllocator", "Resource Manager", ["Resource Allocation", "Team Management", "Project Planning"], "google/flan-t5-base"),
Agent("ProgressTracker", "Progress Monitor", ["Progress Tracking", "Reporting", "Issue Resolution"], "google/flan-t5-base"),
])
# Agent Cluster for a Documentation Generator
documentation_agents = AgentCluster([
Agent("DocWriter", "Documentation Writer", ["Technical Writing", "API Documentation", "User Guides"], "google/flan-t5-base"),
Agent("CodeDocumenter", "Code Commenter", ["Code Documentation", "Code Explanation", "Code Readability"], "google/flan-t5-base"),
Agent("ContentOrganizer", "Content Manager", ["Content Structure", "Information Architecture", "Content Organization"], "google/flan-t5-base"),
])
# --- Web App Logic ---
def process_input(input_text: str, selected_cluster: str):
"""Processes user input and assigns tasks to the appropriate agent cluster."""
if selected_cluster == "Code Review":
cluster = code_review_agents
elif selected_cluster == "Project Management":
cluster = project_management_agents
elif selected_cluster == "Documentation Generation":
cluster = documentation_agents
else:
return "Please select a valid agent cluster."
cluster.add_task(input_text)
cluster.process_tasks()
return "Task processed successfully!"
# --- Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("## Agent-Powered Development Automation")
input_text = gr.Textbox(label="Enter your development task:")
selected_cluster = gr.Radio(
label="Select Agent Cluster", choices=["Code Review", "Project Management", "Documentation Generation"]
)
submit_button = gr.Button("Submit")
output_text = gr.Textbox(label="Output")
submit_button.click(process_input, inputs=[input_text, selected_cluster], outputs=output_text)
demo.launch()
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