feat: ✨ Thêm ứng dụng Gradio để nhận diện tướng AOV

- Tạo giao diện Gradio cho phép người dùng tải ảnh lên và nhận diện tướng AOV sử dụng YOLOv8.
- Tích hợp model YOLOv8 để thực hiện việc nhận diện và hiển thị kết quả trên ảnh.
- Thêm file requirements.txt để liệt kê các thư viện cần thiết cho ứng dụng.

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import os
+from PIL import Image
+
+# Load model
+model_path = "models/aov_herodetector_v1.pt"
+try:
+    model = YOLO(model_path)
+    print(f"Model loaded successfully from {model_path}")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+def detect_heroes(image):
+    """
+    Detect AOV heroes in the uploaded image
+    """
+    if model is None:
+        return None, "Model not loaded properly"
+    
+    try:
+        # Convert PIL Image to numpy array if needed
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        
+        # Run inference
+        results = model(image)
+        
+        # Get the first result
+        result = results[0]
+        
+        # Plot results on image
+        annotated_image = result.plot()
+        
+        # Convert BGR to RGB (OpenCV uses BGR, but gradio expects RGB)
+        annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+        
+        # Get detection info
+        detection_info = ""
+        if len(result.boxes) > 0:
+            detection_info = f"Detected {len(result.boxes)} heroes:\n"
+            for i, box in enumerate(result.boxes):
+                conf = box.conf[0].item()
+                cls = int(box.cls[0].item())
+                class_name = model.names[cls] if cls < len(model.names) else f"Class_{cls}"
+                detection_info += f"- {class_name}: {conf:.2f}\n"
+        else:
+            detection_info = "No heroes detected"
+        
+        return annotated_image, detection_info
+        
+    except Exception as e:
+        return None, f"Error during detection: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="AOV Hero Detector", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎮 Arena of Valor Hero Detector")
+    gr.Markdown("Upload an image to detect AOV heroes using YOLOv8 model")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Upload Image",
+                type="pil",
+                height=400
+            )
+            detect_btn = gr.Button("🔍 Detect Heroes", variant="primary")
+            
+        with gr.Column():
+            output_image = gr.Image(
+                label="Detection Result",
+                height=400
+            )
+            detection_text = gr.Textbox(
+                label="Detection Info",
+                lines=5,
+                max_lines=10
+            )
+    
+    # Examples (optional - you can add sample images)
+    gr.Examples(
+        examples=[],  # Add paths to example images if you have them
+        inputs=input_image,
+        label="Example Images"
+    )
+    
+    # Event handlers
+    detect_btn.click(
+        fn=detect_heroes,
+        inputs=input_image,
+        outputs=[output_image, detection_text]
+    )
+    
+    # Auto-detect when image is uploaded
+    input_image.change(
+        fn=detect_heroes,
+        inputs=input_image,
+        outputs=[output_image, detection_text]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

models/aov_herodetector_v1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a80e681d5aade95cd5b7d30e150cfc81e6737ab363ab5863dd41358b84cb0c1
+size 52025867

requirements.txt

@@ -0,0 +1,7 @@
+gradio
+ultralytics
+opencv-python
+pillow
+numpy
+torch
+torchvision