Create app.py

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image, ImageOps, ImageEnhance
+import numpy as np
+import tempfile
+
+# Load your model
+model = YOLO("model/best.pt")
+
+def preprocess(image):
+    """Safe preprocessing for PIL or numpy input."""
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    image = ImageOps.exif_transpose(image).convert("RGB")
+
+    # Optional resize for performance
+    w, h = image.size
+    max_dim = max(w, h)
+    if max_dim > 1024:
+        scale = 1024 / max_dim
+        image = image.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
+
+    # Light contrast enhancement
+    image = ImageEnhance.Contrast(image).enhance(1.05)
+
+    return image
+
+
+def detect(image, conf=0.5, iou=0.5):
+    """Run YOLO detection on a single model Space."""
+    image = preprocess(image)
+
+    results = model.predict(image, conf=conf, iou=iou)
+    boxes = results[0].boxes
+
+    # Convert YOLO output to numpy RGB
+    output = results[0].plot()[:, :, ::-1]  # BGR → RGB
+
+    if len(boxes) > 0:
+        diagnosis = "⚠️ Redness detected."
+    else:
+        diagnosis = "🟢 No redness detected."
+
+    return [output, diagnosis]
+
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=detect,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(0, 1, value=0.5, step=0.05, label="Confidence Threshold"),
+        gr.Slider(0, 1, value=0.5, step=0.05, label="NMS IoU Threshold"),
+    ],
+    outputs=[
+        gr.Image(label="Redness Detection Result"),
+        gr.Textbox(label="Diagnosis")
+    ],
+    title="Redness Detection"
+)
+
+interface.launch()