app.py

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

# ------------------------
# Load the YOLOv11 classification model
# ------------------------
MODEL_PATH = "best.pt"
model = YOLO(MODEL_PATH)

# Mapping from model's short labels to full orientation names
ORIENTATION_MAP = {
    "ax": "axial",
    "co": "coaxial",
    "sa": "sagittal"
}

# ------------------------
# Prediction function
# ------------------------
def predict_orientation(image: Image.Image):
    # Perform inference using the model
    results = model.predict(source=image, device="cpu", imgsz=224, verbose=False)
    
    # Extract probabilities
    probs = results[0].probs.data.cpu()
    pred = torch.argmax(probs)
    
    # Get the original class label from the model
    original_class = model.names[pred.item()]  # "ax", "co", or "sa"
    
    # Map to full orientation name
    orientation = ORIENTATION_MAP.get(original_class, original_class)
    
    # Get confidence score
    confidence = round(probs[pred].item(), 2)
    
    return f"Orientation: {orientation} | Confidence: {confidence}"

# ------------------------
# Gradio Interface
# ------------------------
iface = gr.Interface(
    fn=predict_orientation,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="MRI Orientation Predictor",
    description="Upload your image and the model outputs prediction and confidence."
)

iface.launch()