src/main.py

#!/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()