#!/usr/bin/env python3
"""
Unified runner that works on any hardware
Automatically adapts to available resources
"""

import sys
import argparse
from config import get_config

def run_streamlit_app():
    """Run the standard Streamlit UI"""
    import streamlit.web.cli as stcli
    sys.argv = ["streamlit", "run", "ui.py"]
    sys.exit(stcli.main())

def run_agent_mode():
    """Run the autonomous agent"""
    from agentic_optimizer import LocalAgentRunner, StudentProfile
    
    print("Starting Agentic Mode...")
    runner = LocalAgentRunner("neu_graph_analyzed_clean.pkl")
    
    # Demo: Add a test student
    student = StudentProfile(
        student_id="demo",
        completed_courses=["CS1800", "CS2500"],
        current_gpa=3.5,
        interests=["AI", "Machine Learning"],
        career_goals="ML Engineer",
        learning_style="Visual",
        time_commitment=40,
        preferred_difficulty="moderate"
    )
    
    student_id = runner.add_student(student)
    print(f"Tracking student: {student_id}")
    
    # Start agent
    runner.start_agent()

def run_api_server():
    """Run as REST API server"""
    from fastapi import FastAPI, HTTPException
    from pydantic import BaseModel
    import uvicorn
    import pickle
    
    # Load optimizer
    from curriculum_optimizer import HybridOptimizer, StudentProfile
    
    app = FastAPI(title="Curriculum Optimizer API")
    
    # Load model once
    optimizer = HybridOptimizer()
    optimizer.load_models()
    
    with open("neu_graph_analyzed_clean.pkl", 'rb') as f:
        graph = pickle.load(f)
    optimizer.load_data(graph)
    
    class PlanRequest(BaseModel):
        completed_courses: list
        gpa: float = 3.5
        interests: list
        career_goals: str
        learning_style: str = "Visual"
        time_commitment: int = 40
        preferred_difficulty: str = "moderate"
    
    @app.post("/generate_plan")
    async def generate_plan(request: PlanRequest):
        profile = StudentProfile(
            completed_courses=request.completed_courses,
            current_gpa=request.gpa,
            interests=request.interests,
            career_goals=request.career_goals,
            learning_style=request.learning_style,
            time_commitment=request.time_commitment,
            preferred_difficulty=request.preferred_difficulty
        )
        
        plan = optimizer.generate_plan(profile)
        return plan
    
    @app.get("/health")
    async def health():
        return {"status": "healthy", "device": str(optimizer.device)}
    
    print("Starting API server on http://localhost:8000")
    print("API docs at http://localhost:8000/docs")
    uvicorn.run(app, host="0.0.0.0", port=8000)

def test_hardware():
    """Test what hardware is available"""
    import torch
    
    print("=" * 60)
    print("HARDWARE TEST")
    print("=" * 60)
    
    if torch.cuda.is_available():
        print(f"✓ CUDA available")
        print(f"  Device: {torch.cuda.get_device_name(0)}")
        print(f"  Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")
        print(f"  Compute: {torch.cuda.get_device_properties(0).major}.{torch.cuda.get_device_properties(0).minor}")
    else:
        print("✗ No CUDA (CPU only)")
    
    print(f"\nSelected Config: {get_config().__name__}")
    config = get_config()
    print(f"  LLM: {config.LLM_MODEL or 'None (embeddings only)'}")
    print(f"  Embedder: {config.EMBEDDING_MODEL}")
    print(f"  Quantization: {config.QUANTIZATION or 'None'}")
    
    print("\nRecommended mode based on hardware:")
    if torch.cuda.is_available() and torch.cuda.get_device_properties(0).total_memory > 10e9:
        print("  → Use 'streamlit' or 'agent' mode (full features)")
    else:
        print("  → Use 'api' mode (lightweight)")

def main():
    parser = argparse.ArgumentParser(description="Curriculum Optimizer Runner")
    parser.add_argument(
        "mode",
        choices=["streamlit", "agent", "api", "test"],
        help="Run mode: streamlit (UI), agent (autonomous), api (REST server), test (hardware test)"
    )
    parser.add_argument(
        "--config",
        choices=["h200", "colab", "local", "cpu", "minimal"],
        help="Force specific configuration"
    )
    
    args = parser.parse_args()
    
    # Set config if specified
    if args.config:
        import os
        os.environ["CURRICULUM_CONFIG"] = args.config
    
    # Run selected mode
    if args.mode == "streamlit":
        run_streamlit_app()
    elif args.mode == "agent":
        run_agent_mode()
    elif args.mode == "api":
        run_api_server()
    elif args.mode == "test":
        test_hardware()

if __name__ == "__main__":
    if len(sys.argv) == 1:
        # No arguments - run hardware test
        test_hardware()
        print("\nUsage: python run.py [streamlit|agent|api|test]")
    else:
        main()