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import modal
import os
# Create Modal app
app = modal.App("alita-chat-app")
# Define the image with all required dependencies
image = (
modal.Image.debian_slim(python_version="3.11")
.pip_install([
"gradio>=4.0.0",
"llama-index-core",
"llama-index-llms-anthropic",
"python-dotenv",
"openai",
"llama-index",
"anthropic",
"requests",
"dataclasses",
"beautifulsoup4",
"duckduckgo-search",
"llama-index-tools-duckduckgo"
])
# Main script
.add_local_file("manager_agent2.py", "/app/manager_agent2.py")
# Models
.add_local_file("models/__init__.py", "/app/models/__init__.py")
.add_local_file("models/mcp_tool_spec.py", "/app/models/mcp_tool_spec.py")
.add_local_file("models/mcp_execution_result.py", "/app/models/mcp_execution_result.py")
.add_local_file("models/task_prompt.py", "/app/models/task_prompt.py")
# Components
.add_local_file("components/__init__.py", "/app/components/__init__.py")
.add_local_file("components/mcp_brainstormer.py", "/app/components/mcp_brainstormer.py")
.add_local_file("components/web_agent.py", "/app/components/web_agent.py")
.add_local_file("components/script_generator.py", "/app/components/script_generator.py")
.add_local_file("components/code_runner.py", "/app/components/code_runner.py")
.add_local_file("components/mcp_registry.py", "/app/components/mcp_registry.py")
)
# Global variables to store initialized components
llm = None
manager_agent = None
@app.function(
image=image,
secrets=[modal.Secret.from_name("anthropic")],
max_containers=10,
timeout=300,
min_containers=1,
cpu=2,
memory=2048
)
def initialize_components():
"""Initialize LLM and Manager Agent"""
global llm, manager_agent
import sys
sys.path.append("/app")
try:
# Import required modules
from llama_index.core import Settings
from llama_index.llms.anthropic import Anthropic
from models import TaskPrompt
from manager_agent import ManagerAgent
# Get API key from environment
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise ValueError("ANTHROPIC_API_KEY not found in environment variables")
# Initialize LLM
llm = Anthropic(model="claude-3-5-sonnet-20241022", api_key=api_key)
Settings.llm = llm
print("Successfully initialized LlamaIndex with Anthropic model")
# Initialize the ManagerAgent
manager_agent = ManagerAgent(llm)
print("✅ ManagerAgent initialized successfully")
return True
except Exception as e:
print(f"Error initializing components: {e}")
import traceback
traceback.print_exc()
return False
@app.function(
image=image,
secrets=[modal.Secret.from_name("anthropic-api-key")],
max_containers=10,
timeout=60,
min_containers=1,
cpu=2,
memory=2048
)
def process_message(message: str):
"""Process a single message through the ManagerAgent"""
import sys
sys.path.append("/app")
try:
from models import TaskPrompt
from manager_agent import ManagerAgent
from llama_index.core import Settings
from llama_index.llms.anthropic import Anthropic
# Initialize components if needed
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
return "❌ ANTHROPIC_API_KEY not found in environment variables"
llm = Anthropic(model="claude-3-5-sonnet-20241022", api_key=api_key)
Settings.llm = llm
manager_agent = ManagerAgent(llm)
# Process the message
task_prompt = TaskPrompt(text=message)
response = manager_agent.run_task(task_prompt)
return response
except Exception as e:
import traceback
error_msg = f"❌ Error processing message: {str(e)}\n{traceback.format_exc()}"
print(error_msg)
return error_msg
# FIXED: Simple web server approach
@app.function(
image=image,
secrets=[modal.Secret.from_name("anthropic-api-key")],
max_containers=10,
timeout=300,
min_containers=1,
cpu=2,
memory=2048
)
@modal.web_server(port=7860, startup_timeout=180)
def gradio_app():
"""Simple Gradio app without complex initialization"""
import gradio as gr
import asyncio
async def chat_function(message, history):
"""Simple chat function that calls the Modal function"""
try:
# Call the Modal function to process the message
response = process_message.remote(message)
# Stream the response word by word for better UX
words = response.split()
partial_response = ""
for i, word in enumerate(words):
partial_response += word + " "
if i % 3 == 0 or i == len(words) - 1:
yield partial_response.strip()
await asyncio.sleep(0.01)
except Exception as e:
yield f"❌ Error: {str(e)}"
# Create simple Gradio interface
interface = gr.ChatInterface(
fn=chat_function,
type="messages",
title="ALITA",
description="ALITA: the self learning AI",
examples=[
"🔍 search for information about AI",
"🛠️ Analyse this csv file",
"⚡ Generate a script to automate a repetitive task",
"🌐 Find open source resources for machine learning",
],
theme="soft"
)
# Launch the interface with Modal-compatible settings
interface.launch(
server_name="0.0.0.0", # Must bind to all interfaces for Modal
server_port=7840, # Must match the port in @modal.web_server
share=False, # Don't create public links
quiet=True, # Reduce logging noise
show_error=True,
prevent_thread_lock=True # Important: prevents blocking Modal's event loop
)
# For local development and testing
if __name__ == "__main__":
app.deploy("alita-chat-app") |