import gradio as gr
import torch
from ultralytics import YOLO
from PIL import Image
import numpy as np

# Load your model
model = YOLO('yolov8m_defence.pt')  # Replace with your model path

# Define class names (based on your 18 categories)
class_names = {
    0: "Cargo Aircraft", 1: "Commercial Aircraft", 2: "Drone", 3: "Fighter Jet",
    4: "Fighter Plane", 5: "Helicopter", 6: "Light Aircraft", 7: "Missile",
    8: "Truck", 9: "Car", 10: "Tank", 11: "Bus", 12: "Van",
    13: "Cargo Ship", 14: "Yacht", 15: "Cruise Ship", 16: "Warship", 17: "Sailboat"
}

def predict(image):
    """Run inference on uploaded image"""
    if image is None:
        return None, "Please upload an image"
    
    # Run inference
    results = model(image)
    
    # Get the plotted image with bounding boxes
    annotated_image = results[0].plot()
    
    # Convert BGR to RGB for display
    annotated_image = annotated_image[..., ::-1]
    
    # Generate detection summary
    detections = results[0].boxes
    if len(detections) == 0:
        summary = "No objects detected"
    else:
        summary = f"Detected {len(detections)} objects:\n\n"
        for i, box in enumerate(detections):
            class_id = int(box.cls[0])
            confidence = float(box.conf[0])
            class_name = class_names.get(class_id, f"Class {class_id}")
            summary += f"• {class_name}: {confidence:.2%}\n"
    
    return annotated_image, summary

# Create Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=[
        gr.Image(type="numpy", label="Detection Results"),
        gr.Textbox(label="Detection Summary", lines=10)
    ],
    title="YOLOv8m Defence Object Detection",
    description="""
    Upload an image to detect military and civilian vehicles, aircraft, and ships.
    
    **Detectable Objects:** Aircraft (cargo, commercial, fighter, helicopter, etc.), 
    Vehicles (car, truck, tank, bus, van), Ships (cargo, yacht, cruise, warship, sailboat), 
    and specialized items (drone, missile).
    
    *Developed for DSTA Brainhack 2025 - TIL-AI Category*
    """,
    examples=[
        ["examples/test1.jpg"],
        ["examples/test2.jpg"],
        ["examples/test3.jpg"],
        ["examples/test4.jpg"],
        ["examples/test5.jpg"],
    ],
    theme=gr.themes.Soft(),
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch()