working

.gitattributes

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README.md

@@ -1,13 +1,13 @@
----
-title: CleanSea
-emoji: 📈
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: CleanSea
+emoji: 📈
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 5.29.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -6,18 +6,19 @@ import os
 # Load the YOLO model
 model = YOLO("models/best.pt")
 
-# Define prediction function
+# Prediction function
 def yolo_predict(image):
     results = model(image)[0]
+    annotated_img = results.plot()
     label_conf = {}
     for box in results.boxes:
         cls_id = int(box.cls[0])
         conf = float(box.conf[0])
         label = model.names[cls_id]
         label_conf[label] = max(conf, label_conf.get(label, 0))
-    return label_conf or {"No object detected": 1.0}
+    return Image.fromarray(annotated_img), label_conf or {"No object detected": 1.0}
 
-# Get all example image paths from the examples/ directory
+# Collect example images
 example_dir = "examples"
 example_files = [
     os.path.join(example_dir, f)
@@ -25,15 +26,30 @@ example_files = [
     if f.lower().endswith((".jpg", ".jpeg", ".png"))
 ]
 
+# Gradio app using Blocks layout
+with gr.Blocks() as demo:
+    gr.Markdown("## Ocean Garbage Detector (YOLOv10)")
+    gr.Markdown("Upload an ocean photo to detect garbage such as plastic bottles or bags using a fine-tuned YOLOv10 model.")
 
-# Gradio Interface
-interface = gr.Interface(
-    fn=yolo_predict,
-    inputs=gr.Image(type="pil", label="Upload Ocean Image"),
-    outputs=gr.Label(num_top_classes=5),
-    title="Ocean Garbage Detector (YOLOv10)",
-    description="Upload an ocean photo to detect garbage such as plastic bottles or bags using a fine-tuned YOLOv10 model.",
-    examples=example_files
-)
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload Ocean Image")
+        with gr.Column():
+            bbox_output = gr.Image(label="Detected Objects")
+            label_output = gr.Label(num_top_classes=5)
 
-interface.launch()
+    gr.Examples(examples=example_files, inputs=image_input, cache_examples=False)
+
+    submit_btn = gr.Button("Detect")
+    submit_btn.click(fn=yolo_predict, inputs=image_input, outputs=[bbox_output, label_output])
+
+    gr.Markdown("### 📈 Model Performance Visualizations")
+    with gr.Row():
+        gr.Image("results/results.png", label="Training Metrics")
+        gr.Image("results/confusion_matrix_normalized.png", label="Confusion Matrix")
+    with gr.Row():
+        gr.Image("results/F1_curve.png", label="F1-Confidence Curve")
+        gr.Image("results/P_curve.png", label="Precision-Confidence Curve")
+        gr.Image("results/PR_curve.png", label="Precision-Recall Curve")
+
+# Launch app locally
+demo.launch()

models/best.pt

@@ -1,3 +1,3 @@
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+size 5774131