from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Dict, Any
import random  # For dummy AI
# from pyomo.environ import *  # For optimization - commented out to avoid Pydantic issues

app = FastAPI()

class PredictDelayRequest(BaseModel):
    vessel_id: int
    features: List[float]

class OptimizationRequest(BaseModel):
    model_config = {"arbitrary_types_allowed": True}  # Allow Pyomo objects

    vessels: List[Dict[str, Any]]
    ports: List[Dict[str, Any]]
    plants: List[Dict[str, Any]]
    costs: List[Dict[str, Any]]

@app.post("/predict-delay")
def predict_delay(request: PredictDelayRequest):
    # Dummy XGBoost prediction
    predicted_delay = random.uniform(1, 5)  # Replace with actual model
    return {"predicted_delay": predicted_delay}

@app.post("/run-optimization")
def run_optimization(request: OptimizationRequest):
    # Simplified optimization without Pyomo for now
    # This is a dummy implementation - replace with actual Pyomo optimization

    try:
        # Dummy optimization logic
        vessels = request.vessels
        ports = request.ports
        plants = request.plants
        costs = request.costs

        # Generate dummy optimal schedule
        optimal_schedule = []
        total_cost = 0

        for i, vessel in enumerate(vessels):
            # Assign vessel to random ports (max 2 ports per vessel)
            assigned_ports = random.sample(range(len(ports)), min(2, len(ports)))

            for port_idx in assigned_ports:
                port = ports[port_idx]
                # Assign to a random plant
                plant_idx = random.randint(0, len(plants) - 1)
                plant = plants[plant_idx]

                optimal_schedule.append({
                    "vessel_id": vessel.get('id', i),
                    "vessel_name": vessel.get('name', f'Vessel {i}'),
                    "port_id": port.get('id', port_idx),
                    "port_name": port.get('name', f'Port {port_idx}'),
                    "plant_id": plant.get('id', plant_idx),
                    "plant_name": plant.get('name', f'Plant {plant_idx}')
                })

                # Calculate dummy cost
                base_cost = random.randint(5000, 15000)
                total_cost += base_cost

        return {
            "optimal_schedule": optimal_schedule,
            "total_cost": total_cost,
            "status": "success",
            "message": "Optimization completed successfully"
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Optimization failed: {str(e)}")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)