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

def summary(model, input_size):
    from torchsummary import summary
    summary(model, input_size=input_size)

def load_model(file_path):
    # Load the Roboflow model
    rf = Roboflow(api_key="K1TXQnJq7EE7yoCf1g3C")
    project = rf.workspace("fyp-l87nq").project("bone-fracture-detection-rkuqr")
    model = project.version(3).model

    # Load the model weights into a PyTorch model
    pytorch_model = YOLO('data.yaml')
    pytorch_model.load_state_dict(torch.load(file_path, map_location=torch.device('cpu')))
    pytorch_model.eval()

    return pytorch_model

file_path = 'yolov8s.pt'
model = load_model(file_path)

def predict_fracture(image):
    # Preprocess the image for the Roboflow model
    img = Image.fromarray(image)
    img_tensor = to_tensor(img).unsqueeze(0)  # Convert image to tensor and add batch dimension

    # Perform inference with the Roboflow model
    with torch.no_grad():
        output = model(img_tensor)

    # Postprocess the inference output
    results = output[0]
    img_with_boxes = image.copy()
    for box in results:
        label = int(box[5])
        score = float(box[4])
        if label == 0:  # Assuming 0 corresponds to the bone fracture class
            color = "red" if score > 0.5 else "orange"  # Adjust the threshold as needed
            xmin, ymin, xmax, ymax = box[:4].int().tolist()
            img_with_boxes.rectangle([xmin, ymin, xmax, ymax], outline=color, width=2)
            img_with_boxes.text((xmin, ymin), f"Fracture: {score:.2f}", font_size=12, color=color)

    return Image.fromarray((np.uint8(img_with_boxes)))

# Define the to_tensor function
def to_tensor(image):
    image = np.array(image) / 255.0
    return torch.from_numpy(image.transpose((2, 0, 1))).float()
    
# Gradio Interface
iface = gr.Interface(
     fn=lambda *args, **kwargs: predict_fracture(args[0], load_model),
    inputs=gr.Image(),
    outputs=gr.Image(),
    live=True,
    title="Bone Fracture Detection",
    description="Upload an X-ray image to detect bone fractures using Roboflow's YOLOv8 model.",
)

iface.launch()