initial space

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test_images/ filter=lfs diff=lfs merge=lfs -text
+test_images/example2.jpg filter=lfs diff=lfs merge=lfs -text
+test_images/example3.jpg filter=lfs diff=lfs merge=lfs -text
+test_images/example1.jpg filter=lfs diff=lfs merge=lfs -text
+install filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,8 +1,8 @@
 ---
-title: Yolo Layout Detector Space
-emoji: 🏆
+title: Layout Detector Space
+emoji: ⚡
 colorFrom: blue
-colorTo: purple
+colorTo: green
 sdk: gradio
 sdk_version: 3.44.4
 app_file: app.py

app.py

@@ -0,0 +1,38 @@
+
+import gradio as gr
+import yolov5
+from PIL import Image
+
+app_title = "Aksara Jawa Layout Detection"
+models_id = 'hermanshid/yolo-layout-detector-space'
+
+model = yolov5.load(models_id)
+
+examples = [['test_images/example1.jpg', 0.6, ]]
+
+
+def predict(image, threshold=0.25):
+    global models_id
+    global model
+
+    input_size = 640
+
+    model.conf = threshold
+    results = model(image, size=input_size)
+    numpy_image = results.render()[0]
+    output_image = Image.fromarray(numpy_image)
+    return output_image
+
+
+gr.Interface(
+    title=app_title,
+    description="Created by 'hermanshid'",
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Slider(maximum=1, step=0.01, value=0.25),
+    ],
+    outputs=gr.Image(type="pil"),
+    examples=examples,
+    cache_examples=True if examples else False,
+).launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,4 @@
+yolov5==7.0.5
+gradio==3.15.0
+torch
+huggingface-hub