Upload 3 files

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from collections import Counter
+from ultralytics import YOLO
+
+# Load YOLOv10 model
+model_path = "best.pt" 
+model = YOLO(model_path)
+
+# Define the predict function
+def predict(image):
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    result = model.predict(source=image_rgb, imgsz=640, conf=0.25)
+
+    annotated_img = result[0].plot()
+
+    detections = result[0].boxes.data
+    class_names = [model.names[int(cls)] for cls in detections[:, 5]]
+    count = Counter(class_names)
+
+    detection_str = ', '.join([f"{name}: {count}" for name, count in count.items()])
+    annotated_img = annotated_img[:, :, ::-1]
+
+    return annotated_img, detection_str
+
+# Create Gradio interface
+app = gr.Interface(
+    predict,
+    inputs=gr.Image(type="numpy", label="Upload an image"),
+    outputs=[gr.Image(type="numpy", label="Annotated Image"), gr.Textbox(label="Detection Count")],
+    title="Blood Cell Count using YOLO V10",
+    description="Upload an image,then YOLO V10 model will detect and annotate blood cells."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch(share=True, server_port=8080, debug=True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f98dfcf46648763e3c8f45ca85e04e45cf70d010fc567712499737c611c58f88
+size 5753203

requirements.txt

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+gradio
+opencv-python
+roboflow
+torch
+albumentations
+ultralytics