app.py

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

# --- Load YOLOv12 model ---
model_path = "best.pt"
if os.path.exists(model_path):
    model = YOLO(model_path)
else:
    model = None

# --- Inference function ---
def detect_ear(image: Image.Image, conf: float = 0.25):
    if model is not None:
        try:
            # Run YOLOv12 inference
            results = model(np.array(image), conf=conf, verbose=False)
            annotated = results[0].plot()
            return Image.fromarray(annotated)
        except Exception as e:
            print(f"Model inference failed: {e}")
    
    # --- Dummy fallback ---
    import PIL.ImageDraw as ImageDraw
    im = image.copy()
    draw = ImageDraw.Draw(im)
    w, h = im.size
    box = (int(w*0.25), int(h*0.25), int(w*0.75), int(h*0.75))
    draw.rectangle(box, outline="#00ffff", width=5)
    draw.text((box[0], box[1]-25), "Detected: Possible Condition (Dummy)", fill="#00ffff")
    return im

# --- Gradio Interface ---
iface = gr.Interface(
    fn=detect_ear,
    inputs=[
        gr.Image(type="pil", label="Upload Otoscopic Image"),
        gr.Slider(0.0, 1.0, value=0.25, step=0.01, label="Confidence Threshold")
    ],
    outputs=gr.Image(type="pil", label="Detection Result"),
    title="Automated Ear Disease Detection",
    description="Detect ear conditions using YOLOv12",
)

# --- Launch App ---
iface.launch()