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app.py

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+import gradio as gr
+from detection import ObjectDetection
+
+examples = [
+    ['test-images/plant1.jpeg', 0.31],
+    ['test-images/plant1.jpeg', 0.51],
+    ['test-images/plant1.jpeg', 0.39],
+]
+
+def get_predictions(img, threshold, box_color, text_color):
+    v8_results = yolov8_detector.v8_score_frame(img)
+    v8_frame = yolov8_detector.plot_bboxes(v8_results, img, float(threshold), box_color, text_color)
+    return v8_frame
+
+with gr.Blocks(title="Leaf Disease Detection", theme=gr.themes.Monochrome()) as interface:
+    gr.Markdown("# Leaf Disease Detection")
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(shape=(416,416), label="Input Image")
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    box_color = gr.ColorPicker(label="Box Color", value="#0000ff")
+                with gr.Column():
+                    text_color = gr.ColorPicker(label="Prediction Color", value="#ff0000")
+
+            confidence = gr.Slider(maximum=1, step=0.01, value=0.4, label="Confidence Threshold", interactive=True)
+            btn = gr.Button("Detect")
+    
+    with gr.Row():        
+        with gr.Box():
+            v8_prediction = gr.Image(shape=(416,416), label="YOLOv8")
+
+        btn.click(
+            get_predictions,
+            [image, confidence, box_color, text_color],
+            [v8_prediction]
+        )
+    
+    with gr.Row():
+        gr.Examples(examples=examples, inputs=[image, confidence])
+
+
+yolov8_detector = ObjectDetection('yolov8')
+
+interface.launch()

detection.py

@@ -0,0 +1,52 @@
+import cv2
+import IPython
+from PIL import ImageColor
+from ultralytics import YOLO
+
+class ObjectDetection:
+    def __init__(self, model_name='yolov8'):
+        self.model_name = model_name
+        self.model = self.load_model()
+        self.classes = self.model.names
+        self.device = 'cpu' 
+
+    def load_model(self):
+        model = YOLO(f"weights/{self.model_name}_best.pt")
+        return model
+    
+    def v8_score_frame(self, frame):
+        results = self.model(frame)
+
+        labels = results.names[results.pred[..., -1].argmax(-1)]  # Get class labels
+        confidences = results.pred[..., -2].max(-1)  # Get confidences
+        coords = results.pred[..., :-2]  # Get coordinates
+
+        return labels, confidences, coords
+
+    def get_coords(self, frame, row):
+        return int(row[0]), int(row[1]), int(row[2]), int(row[3])
+    
+    def class_to_label(self, x):
+        return self.classes[int(x)]
+    
+    def get_color(self, code):
+        rgb = ImageColor.getcolor(code, "RGB")
+        return rgb 
+    
+    def plot_bboxes(self, results, frame, threshold=0.5, box_color='red', text_color='white'):
+        labels, conf, coord = results
+
+        frame = frame.copy()
+        box_color = self.get_color(box_color)
+        text_color = self.get_color(text_color)
+
+        for i in range(len(labels)):
+            if conf[i] >= threshold:
+                x1, y1, x2, y2 = self.get_coords(frame, coord[i])
+                class_name = self.class_to_label(labels[i])
+
+                cv2.rectangle(frame, (x1, y1), (x2, y2), box_color, 2)
+                cv2.putText(frame, f"{class_name} - {conf[i]*100:.2f}%", (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.5, text_color)
+        
+        return frame
+