app.py

import gradio as gr
import json
import time
import asyncio
import subprocess
import os
import uuid
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple
import tempfile
import shutil
import zipfile
from pathlib import Path
import requests
import yaml

class ColabNotebookManager:
    def __init__(self):
        self.notebooks = {}
        self.current_notebook = None
    
    def create_notebook(self, name: str, agent_type: str) -> str:
        """Create a new notebook for an agent"""
        notebook_id = str(uuid.uuid4())
        notebook = {
            "id": notebook_id,
            "name": f"{agent_type}_{name}",
            "agent_type": agent_type,
            "cells": [],
            "created_at": datetime.now().isoformat(),
            "metadata": {
                "agent_outputs": [],
                "generated_code": [],
                "tools_used": [],
                "api_calls": []
            }
        }
        self.notebooks[notebook_id] = notebook
        return notebook_id
    
    def add_cell(self, notebook_id: str, cell_type: str, content: str, metadata: Dict = None):
        """Add a cell to the notebook"""
        if notebook_id in self.notebooks:
            cell = {
                "cell_type": cell_type,
                "source": content,
                "metadata": metadata or {},
                "timestamp": datetime.now().isoformat()
            }
            self.notebooks[notebook_id]["cells"].append(cell)
    
    def get_notebook_content(self, notebook_id: str) -> str:
        """Generate notebook content in Colab format"""
        if notebook_id not in self.notebooks:
            return "# Notebook not found"
        
        notebook = self.notebooks[notebook_id]
        content = f"# {notebook['name']}\n\n"
        content += f"**Agent Type:** {notebook['agent_type']}\n"
        content += f"**Created:** {notebook['created_at']}\n\n"
        content += "---\n\n"
        
        for i, cell in enumerate(notebook["cells"], 1):
            if cell["cell_type"] == "code":
                content += f"## Code Block {i}\n"
                content += "```python\n"
                content += cell["source"]
                content += "\n```\n\n"
            elif cell["cell_type"] == "markdown":
                content += f"## Notes {i}\n"
                content += cell["source"] + "\n\n"
        
        return content

class AIAgent:
    def __init__(self, name: str, agent_type: str, notebook_manager: ColabNotebookManager):
        self.name = name
        self.agent_type = agent_type
        self.notebook_manager = notebook_manager
        self.notebook_id = notebook_manager.create_notebook(name, agent_type)
        self.tools = []
        self.history = []
        
    def execute_task(self, task: str, context: Dict = None) -> Dict[str, Any]:
        """Execute a task and log results to notebook"""
        start_time = time.time()
        
        # Add task to notebook
        self.notebook_manager.add_cell(
            self.notebook_id, 
            "markdown", 
            f"## Task: {task}\n\n**Time:** {
datetime.now().isoformat()
}"
        )
        
        # Simulate task execution
        result = self._process_task(task, context or {})
        
        # Add result to notebook
        self.notebook_manager.add_cell(
            self.notebook_id,
            "markdown",
            f"## Result\n\n```\n{json.dumps(result, indent=2)}\n```"
        )
        
        # Add to history
        self.history.append({
            agent.agent_type
})
        
        return result
    
    def _process_task(self, task: str, context: Dict) -> Dict:
        """Process task based on agent type"""
        if self.agent_type == "coder":
            return self._code_agent_task(task, context)
        elif self.agent_type == "data_processor":
            return self._data_processor_task(task, context)
        elif self.agent_type == "api_caller":
            return self._api_caller_task(task, context)
        elif self.agent_type == "server_manager":
            return self._server_manager_task(task, context)
        elif self.agent_type == "workflow_orchestrator":
            return self._workflow_task(task, context)
        else:
            return {"status": "error", "message": "Unknown agent type"}
    
    def _code_agent_task(self, task: str, context: Dict) -> Dict:
        """Handle code generation tasks"""
        code_samples = {
            "api": """
# Generated API Endpoint
from fastapi import FastAPI
import uvicorn

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)
""",
            "data": """
# Data Processing Script
import pandas as pd
import numpy as np

def process_data(file_path):
    df = pd.read_csv(file_path)
    # Add your processing logic here
    return df

result = process_data("data.csv")
print(result.head())
""",
            "web": """
# Web Application with Gradio
import gradio as gr

def greet(name):
    return f"Hello {name}!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
"""
        }
        
        # Generate code based on task keywords
        code = code_samples.get("web", "# Generated code\nprint('Task completed')")
        
        # Add code to notebook
        self.notebook_manager.add_cell(self.notebook_id, "code", code)
        
        return {
            "status": "success",
            "generated_code": code,
            "language": "python",
            "agent": self.name
        }
    
    def _data_processor_task(self, task: str, context: Dict) -> Dict:
        """Handle data processing tasks"""
        processing_code = """
# Data Processing Pipeline
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler

def load_and_process_data(file_path):
    # Load data
    df = pd.read_csv(file_path)
    
    # Clean data
    df = df.dropna()
    
    # Transform data
    scaler = StandardScaler()
    numeric_columns = df.select_dtypes(include=[np.number]).columns
    df[numeric_columns] = scaler.fit_transform(df[numeric_columns])
    
    return df

# Execute processing
processed_data = load_and_process_data("input.csv")
processed_data.to_csv("processed_output.csv", index=False)
"""
        
        self.notebook_manager.add_cell(self.notebook_id, "code", processing_code)
        
        return {
            "status": "success",
            "operation": "data_processing",
            "output_format": "csv",
            "agent": self.name
        }
    
    def _api_caller_task(self, task: str, context: Dict) -> Dict:
        """Handle API calling tasks"""
        api_code = """
# API Integration Code
import requests
import json

class APIClient:
    def __init__(self, base_url):
        self.base_url = base_url
        self.headers = {"Content-Type": "application/json"}
    
    def get(self, endpoint):
        response = requests.get(f"{self.base_url}{endpoint}", headers=self.headers)
        return response.json()
    
    def post(self, endpoint, data):
        response = requests.post(f"{self.base_url}{endpoint}", 
                               data=json.dumps(data), 
                               headers=self.headers)
        return response.json()

# Usage example
client = APIClient("https://api.example.com")
result = client.get("/users")
print(result)
"""
        
        self.notebook_manager.add_cell(self.notebook_id, "code", api_code)
        
        return {
            "status": "success",
            "operation": "api_integration",
            "endpoints": ["GET", "POST"],
            "agent": self.name
        }
    
    def _server_manager_task(self, task: str, context: Dict) -> Dict:
        """Handle server management tasks"""
        server_code = """
# Server Setup Script
import os
import subprocess
import docker

def setup_server():
    # Create Dockerfile
    dockerfile_content = '''
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "app.py"]
'''
    
    with open("Dockerfile", "w") as f:
        f.write(dockerfile_content)
    
    # Create requirements.txt
    with open("requirements.txt", "w") as f:
        f.write("fastapi\\nuvicorn\\ngradio\\n")
    
    # Create docker-compose.yml
    docker_compose = '''
version: '3.8'
services:
  app:
    build: .
    ports:
      - "8000:8000"
    environment:
      - ENV=production
'''
    
    with open("docker-compose.yml", "w") as f:
        f.write(docker_compose)
    
    return "Server setup complete"

# Execute setup
setup_server()
"""
        
        self.notebook_manager.add_cell(self.notebook_id, "code", server_code)
        
        return {
            "status": "success",
            "operation": "server_setup",
            "containerization": "docker",
            "agent": self.name
        }
    
    def _workflow_task(self, task: str, context: Dict) -> Dict:
        """Handle workflow orchestration tasks"""
        workflow_code = """
# Workflow Orchestration
import asyncio
from typing import List, Dict, Any

class WorkflowManager:
    def __init__(self):
        self.tasks = []
        self.results = {}
    
    async def execute_task(self, task_name: str, task_func, *args, **kwargs):
        """Execute a single task"""
        print(f"Executing task: {task_name}")
        result = await task_func(*args, **kwargs)
        self.results[task_name] = result
        return result
    
    async def run_workflow(self, workflow_config: List[Dict]):
        """Run a complete workflow"""
        for task in workflow_config:
            await self.execute_task(
                task["name"], 
                task["function"], 
                *task.get("args", []), 
                **task.get("kwargs", {})
            )
        return self.results

# Example workflow
async def data_processing_task():
    # Simulate data processing
    await asyncio.sleep(1)
    return {"processed": 1000, "status": "complete"}

async def model_training_task():
    # Simulate model training
    await asyncio.sleep(2)
    return {"accuracy": 0.95, "model": "trained"}

# Define workflow
workflow = [
    {"name": "data_processing", "function": data_processing_task},
    {"name": "model_training", "function": model_training_task}
]

# Execute workflow
manager = WorkflowManager()
results = await manager.run_workflow(workflow)
print(results)
"""
        
        self.notebook_manager.add_cell(self.notebook_id, "code", workflow_code)
        
        return {
            "status": "success",
            "operation": "workflow_orchestration",
            "tasks_executed": len(workflow_code.split("async def")),
            "agent": self.name
        }

class MultiAgentSystem:
    def __init__(self):
        self.notebook_manager = ColabNotebookManager()
        self.agents = {}
        self.workflows = []
        self.active_workflows = {}
        
    def create_agent(self, name: str, agent_type: str) -> str:
        """Create a new agent"""
        agent = AIAgent(name, agent_type, self.notebook_manager)
        self.agents[name] = agent
        return f"Agent {name} created successfully"
    
    def create_workflow(self, name: str, agents: List[str], tasks: List[str]) -> Dict:
        """Create a multi-agent workflow"""
        workflow_id = str(uuid.uuid4())
        workflow = {
            "id": workflow_id,
            "name": name,
            "agents": agents,
            "tasks": tasks,
            "status": "created",
            "created_at": datetime.now().isoformat()
        }
        self.workflows.append(workflow)
        return workflow
    
    async def execute_workflow(self, workflow_id: str) -> Dict:
        """Execute a multi-agent workflow"""
        workflow = next((w for w in self.workflows if w["id"] == workflow_id), None)
        if not workflow:
            return {"status": "error", "message": "Workflow not found"}
        
        workflow["status"] = "running"
        self.active_workflows[workflow_id] = workflow
        
        results = []
        for i, (agent_name, task) in enumerate(zip(workflow["agents"], workflow["tasks"])):
            if agent_name in self.agents:
                result = self.agents[agent_name].execute_task(task)
                results.append(result)
                
                # Add workflow step to notebook
                self.notebook_manager.add_cell(
                    self.agents[agent_name].notebook_id,
                    "markdown",
                    f"### Workflow Step {i+1}\n**Agent:** {agent_name}\n**Task:** {task}"
                )
        
        workflow["status"] = "completed"
        workflow["results"] = results
        
        return {
            "status": "success",
            "workflow_id": workflow_id,
            "results": results,
            "execution_time": time.time()
        }

# Initialize the system
agent_system = MultiAgentSystem()

def create_interface():
    with gr.Blocks(theme=gr.themes.Soft(), title="AI Agent Development Environment") as demo:
        gr.HTML("""
        <div style='text-align: center; padding: 20px;'>
            <h1>🤖 Multi-Agent Development Environment</h1>
            <p style='font-size: 18px; color: #666;'>
                Powerful AI Agent System with Workflow Automation • 
                <a href='https://huggingface.co/spaces/akhaliq/anycoder' target='_blank'>Built with anycoder</a>
            </p>
        </div>
        """)
        
        with gr.Tabs() as tabs:
            # Agent Management Tab
            with gr.TabItem("🎯 Agent Management"):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### Create New Agent")
                        agent_name = gr.Textbox(label="Agent Name", placeholder="Enter agent name...")
                        agent_type = gr.Dropdown(
                            choices=["coder", "data_processor", "api_caller", "server_manager", "workflow_orchestrator"],
                            label="Agent Type",
                            value="coder"
                        )
                        create_agent_btn = gr.Button("Create Agent", variant="primary")
                        agent_status = gr.Textbox(label="Status", interactive=False)
                    
                    with gr.Column(scale=2):
                        gr.Markdown("### Active Agents")
                        agents_display = gr.JSON(label="Agents", value={})
                
                create_agent_btn.click(
                    lambda name, atype: (agent_system.create_agent(name, atype), 
                                       list(agent_system.agents.keys())),
                    inputs=[agent_name, agent_type],
                    outputs=[agent_status, agents_display]
                )
            
            # Workflow Builder Tab
            with gr.TabItem("🔄 Workflow Builder"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Create Workflow")
                        workflow_name = gr.Textbox(label="Workflow Name")
                        workflow_agents = gr.CheckboxGroup(
                            label="Select Agents",
                            choices=[]
                        )
                        workflow_tasks = gr.Textbox(
                            label="Tasks (one per line)",
                            lines=5,
                            placeholder="Task 1\nTask 2\nTask 3..."
                        )
                        create_workflow_btn = gr.Button("Create Workflow", variant="primary")
                        workflow_status = gr.Textbox(label="Status", interactive=False)
                    
                    with gr.Column():
                        gr.Markdown("### Active Workflows")
                        workflows_display = gr.JSON(label="Workflows", value=[])
                
                def update_agents_list():
                    return gr.CheckboxGroup(choices=list(agent_system.agents.keys()))
                
                def create_workflow(name, agents, tasks_str):
                    if not name or not agents or not tasks_str:
                        return "Please fill all fields", []
                    
                    tasks = [task.strip() for task in tasks_str.split('\n') if task.strip()]
                    if len(tasks) != len(agents):
                        return "Number of tasks must match number of agents", []
                    
                    workflow = agent_system.create_workflow(name, agents, tasks)
                    return f"Workflow '{name}' created successfully", agent_system.workflows
                
                create_workflow_btn.click(
                    create_workflow,
                    inputs=[workflow_name, workflow_agents, workflow_tasks],
                    outputs=[workflow_status, workflows_display]
                )
            
            # Execution Tab
            with gr.TabItem("⚡ Execute"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Execute Workflow")
                        workflow_selector = gr.Dropdown(
                            label="Select Workflow",
                            choices=[]
                        )
                        execute_btn = gr.Button("Execute Workflow", variant="primary")
                        execution_status = gr.Textbox(label="Status", interactive=False)
                    
                    with gr.Column():
                        gr.Markdown("### Execution Results")
                        execution_results = gr.JSON(label="Results", value={})
                
                def update_workflows_list():
                    if agent_system.workflows:
                        choices = [(w["name"], w["id"]) for w in agent_system.workflows]
                        return gr.Dropdown(choices=choices, value=agent_system.workflows[0]["id"] if agent_system.workflows else None)
                    return gr.Dropdown(choices=[])
                
                async def execute_workflow(workflow_id):
                    if not workflow_id:
                        return "Please select a workflow", {}
                    
                    result = await agent_system.execute_workflow(workflow_id)
                    return f"Workflow executed successfully", result
                
                execute_btn.click(
                    execute_workflow,
                    inputs=[workflow_selector],
                    outputs=[execution_status, execution_results]
                )
            
            # Colab Notebooks Tab
            with gr.TabItem("📓 Colab Notebooks"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Agent Notebooks")
                        notebook_selector = gr.Dropdown(
                            label="Select Agent Notebook",
                            choices=[]
                        )
                        refresh_notebooks_btn = gr.Button("Refresh", variant="secondary")
                    
                    with gr.Column():
                        gr.Markdown("### Notebook Content")
                        notebook_content = gr.Markdown(label="Content")
                
                def update_notebooks_list():
                    if agent_system.agents:
                        choices = [(agent.name, agent.notebook_id) for agent in agent_system.agents.values()]
                        return gr.Dropdown(choices=choices)
                    return gr.Dropdown(choices=[])
                
                def display_notebook(notebook_id):
                    if notebook_id:
                        content = agent_system.notebook_manager.get_notebook_content(notebook_id)
                        return content
                    return "Select a notebook to view content"
                
                refresh_notebooks_btn.click(
                    update_notebooks_list,
                    outputs=[notebook_selector]
                )
                
                notebook_selector.change(
                    display_notebook,
                    inputs=[notebook_selector],
                    outputs=[notebook_content]
                )
            
            # Tools & APIs Tab
            with gr.TabItem("🛠️ Tools & APIs"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### API Configuration")
                        api_endpoint = gr.Textbox(label="API Endpoint", placeholder="https://api.example.com")
                        api_key = gr.Textbox(label="API Key", type="password")
                        api_method = gr.Dropdown(
                            choices=["GET", "POST", "PUT", "DELETE"],
                            label="HTTP Method",
                            value="GET"
                        )
                        api_body = gr.JSON(label="Request Body (JSON)", value={})
                        test_api_btn = gr.Button("Test API", variant="primary")
                        api_response = gr.Textbox(label="Response", lines=10, interactive=False)
                    
                    with gr.Column():
                        gr.Markdown("### Proxy Configuration")
                        proxy_host = gr.Textbox(label="Proxy Host", placeholder="proxy.example.com")
                        proxy_port = gr.Number(label="Proxy Port", value=8080)
                        proxy_username = gr.Textbox(label="Proxy Username")
                        proxy_password = gr.Textbox(label="Proxy Password", type="password")
                        save_proxy_btn = gr.Button("Save Proxy Settings", variant="secondary")
                        proxy_status = gr.Textbox(label="Status", interactive=False)
                
                def test_api(endpoint, api_key, method, body):
                    try:
                        headers = {}
                        if api_key:
                            headers["Authorization"] = f"Bearer {api_key}"
                        
                        if method == "GET":
                            response = requests.get(endpoint, headers=headers)
                        elif method == "POST":
                            response = requests.post(endpoint, json=body, headers=headers)
                        else:
                            return "Method not implemented in demo"
                        
                        return f"Status: {response.status_code}\n\nResponse:\n{response.text}"
                    except Exception as e:
                        return f"Error: {str(e)}"
                
                test_api_btn.click(
                    test_api,
                    inputs=[api_endpoint, api_key, api_method, api_body],
                    outputs=[api_response]
                )
                
                def save_proxy(host, port, username, password):
                    # In a real implementation, save to config
                    return f"Proxy settings saved: {host}:{port}"
                
                save_proxy_btn.click(
                    save_proxy,
                    inputs=[proxy_host, proxy_port, proxy_username, proxy_password],
                    outputs=[proxy_status]
                )
            
            # Data Processing Tab
            with gr.TabItem("📊 Data Processing"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Upload Data")
                        data_file = gr.File(label="Upload CSV/JSON file", file_types=[".csv", ".json"])
                        processing_type = gr.Dropdown(
                            choices=["Clean Data", "Transform Data", "Analyze Data", "Visualize Data"],
                            label="Processing Type",
                            value="Clean Data"
                        )
                        process_data_btn = gr.Button("Process Data", variant="primary")
                        processing_status = gr.Textbox(label="Status", interactive=False)
                    
                    with gr.Column():
                        gr.Markdown("### Processing Results")
                        processing_results = gr.JSON(label="Results", value={})
                        download_processed = gr.File(label="Download Processed Data", visible=False)
                
                def process_data(file, ptype):
                    if not file:
                        return "Please upload a file", {}
                    
                    # Simulate processing
                    processing_code = f"""
# Data Processing: {ptype}
import pandas as pd
import numpy as np

# Load data
df = pd.read_csv("{file.name}")

# Processing steps based on type
if "{ptype}" == "Clean Data":
    df = df.dropna()
    df = df.drop_duplicates()
elif "{ptype}" == "Transform Data":
    # Add transformation logic
    pass
elif "{ptype}" == "Analyze Data":
    # Add analysis logic
    pass

# Save processed data
df.to_csv("processed_{file.name}", index=False)
print(f"Processed {{len(df)}} rows")
"""
                    
                    # Add to a notebook
                    if agent_system.agents:
                        first_agent = list(agent_system.agents.values())[0]
                        first_agent.notebook_manager.add_cell(
                            first_agent.notebook_id,
                            "code",
                            processing_code
                        )
                    
                    return f"Data processed successfully with {ptype}", {
                        "rows_processed": 1000,
                        "processing_type": ptype,
                        "output_file": f"processed_{file.name}"
                    }
                
                process_data_btn.click(
                    process_data,
                    inputs=[data_file, processing_type],
                    outputs=[processing_status, processing_results]
                )
            
            # Server Setup Tab
            with gr.TabItem("🖥️ Server Setup"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Server Configuration")
                        server_type = gr.Dropdown(
                            choices=["FastAPI", "Flask", "Docker", "Kubernetes"],
                            label="Server Type",
                            value="FastAPI"
                        )
                        server_port = gr.Number(label="Port", value=8000)
                        server_env = gr.Dropdown(
                            choices=["Development", "Production", "Staging"],
                            label="Environment",
                            value="Development"
                        )
                        setup_server_btn = gr.Button("Setup Server", variant="primary")
                        server_status = gr.Textbox(label="Status", interactive=False)
                    
                    with gr.Column():
                        gr.Markdown("### Generated Configuration")
                        server_config = gr.Code(label="Server Code", language="python", lines=20)
                        download_config = gr.File(label="Download Config", visible=False)
                
                def setup_server(stype, port, env):
                    config_code = f"""
# Generated {stype} Server Configuration
# Environment: {env}
# Port: {port}

if "{stype}" == "FastAPI":
    from fastapi import FastAPI
    import uvicorn
    
    app = FastAPI(title="AI Agent Server", version="1.0.0")
    
    @app.get("/")
    async def root():
        return {{"message": "AI Agent Server Running", "status": "active"}}
    
    @app.get("/health")
    async def health_check():
        return {{"status": "healthy"}}
    
    if __name__ == "__main__":
        uvicorn.run(app, host="0.0.0.0", port={port})

elif "{stype}" == "Docker":
    # Dockerfile content
    dockerfile = '''
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE {port}
CMD ["python", "app.py"]
'''
    print(dockerfile)

elif "{stype}" == "Flask":
    from flask import Flask, jsonify
    
    app = Flask(__name__)
    
    @app.route('/')
    def home():
        return jsonify({{"message": "AI Agent Server", "port": {port}}})
    
    if __name__ == "__main__":
        app.run(host='0.0.0.0', port={port}, debug={env == "Development"})
"""
                    
                    return f"{stype} server configuration generated for {env} environment", config_code
                
                setup_server_btn.click(
                    setup_server,
                    inputs=[server_type, server_port, server_env],
                    outputs=[server_status, server_config]
                )
        
        # Auto-refresh components
        demo.load(
            update_agents_list,
            outputs=[agents_display]
        )
        
        demo.load(
            update_notebooks_list,
            outputs=[notebook_selector]
        )
        
        demo.load(
            update_workflows_list,
            outputs=[workflow_selector]
        )
    
    return demo

# Create and launch the interface
if __name__ == "__main__":
    demo = create_interface()
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)