app.py

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()