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#!/usr/bin/env python3
"""
HuggingFace Agents Course Unit 4 Final Assignment
Multi-Agent System using LangGraph for GAIA Benchmark
Goal: Achieve 30%+ score on Unit 4 API (GAIA benchmark subset)
Architecture: Multi-agent LangGraph system with Qwen 2.5 models
"""
import os
import gradio as gr
from typing import Dict, Any
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
class GAIAAgentSystem:
"""Main orchestrator for the GAIA benchmark multi-agent system"""
def __init__(self):
self.setup_environment()
self.initialize_agents()
def setup_environment(self):
"""Initialize environment and validate required settings"""
self.hf_token = os.getenv("HUGGINGFACE_TOKEN")
if not self.hf_token:
print("WARNING: HUGGINGFACE_TOKEN not set. Some features may be limited.")
# Use optimized Qwen model tier configuration
self.router_model = "Qwen/Qwen2.5-7B-Instruct" # Fast routing
self.main_model = "Qwen/Qwen2.5-32B-Instruct" # Main reasoning
self.complex_model = "Qwen/Qwen2.5-72B-Instruct" # Complex tasks
def initialize_agents(self):
"""Initialize the multi-agent system components"""
print("🚀 Initializing GAIA Agent System...")
print(f"📱 Router Model: {self.router_model}")
print(f"🧠 Main Model: {self.main_model}")
print(f"🔬 Complex Model: {self.complex_model}")
# TODO: Initialize LangGraph workflow
# TODO: Setup agent nodes and edges
# TODO: Configure tools and capabilities
def process_question(self, question: str, files: list = None) -> Dict[str, Any]:
"""Process a GAIA benchmark question through the multi-agent system"""
if not question.strip():
return {
"answer": "Please provide a question to process.",
"confidence": 0.0,
"reasoning": "No input provided",
"agent_path": []
}
# TODO: Route question through LangGraph workflow
# TODO: Coordinate between multiple agents
# TODO: Process any uploaded files
# TODO: Return structured response
# Placeholder response for Phase 1
return {
"answer": f"Processing question: {question[:100]}...",
"confidence": 0.5,
"reasoning": "Phase 1 placeholder - agent system initializing",
"agent_path": ["router", "main_agent"]
}
def create_gradio_interface():
"""Create the Gradio web interface for HuggingFace Space deployment"""
agent_system = GAIAAgentSystem()
def process_with_files(question: str, files):
"""Handle question processing with optional file uploads"""
file_list = files if files else []
result = agent_system.process_question(question, file_list)
# Format output for display
output = f"""
**Answer:** {result['answer']}
**Confidence:** {result['confidence']:.1%}
**Reasoning:** {result['reasoning']}
**Agent Path:** {' → '.join(result['agent_path'])}
"""
return output
# Create Gradio interface
interface = gr.Interface(
fn=process_with_files,
inputs=[
gr.Textbox(
label="GAIA Question",
placeholder="Enter your question here...",
lines=3
),
gr.Files(
label="Upload Files (Optional)",
file_count="multiple",
file_types=["image", "audio", ".txt", ".csv", ".xlsx", ".py"]
)
],
outputs=gr.Markdown(label="Agent Response"),
title="🤖 GAIA Benchmark Agent System",
description="""
Multi-agent system for the GAIA benchmark using LangGraph framework.
**Capabilities:**
- Multi-step reasoning and planning
- Web search and research
- File processing (images, audio, documents)
- Mathematical computation
- Code execution and analysis
**Target:** 30%+ accuracy on GAIA benchmark questions
""",
examples=[
["What is the population of France?", None],
["Calculate the square root of 144", None],
["Analyze the uploaded image and describe what you see", None]
],
theme=gr.themes.Soft()
)
return interface
def main():
"""Main entry point"""
print("🎯 HuggingFace Agents Course Unit 4 - Final Assignment")
print("📊 Target: 30%+ score on GAIA benchmark")
print("🔧 Framework: LangGraph multi-agent system")
print("💰 Budget: Free tier models (~$0.10/month)")
# Create and launch interface
interface = create_gradio_interface()
# Launch with appropriate settings for HuggingFace Space
interface.launch(
share=False,
server_name="0.0.0.0",
server_port=7860,
show_error=True
)
if __name__ == "__main__":
main() |