Upload 3 files

app.py

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+import gradio as gr
+from ultralytics import YOLO
+import os
+import glob
+# Load the trained YOLOv10 model
+model = YOLO('/content/runs/detect/train/weights/best.pt')
+
+# Inference function for Gradio
+def detect_objects(image):
+    # Run prediction
+    results = model.predict(source=image, save=True, conf=0.5)
+
+    # Get the path of the saved image
+    predicted_image_dir = 'runs/detect/predict'
+    predicted_image_path = glob.glob(f"{predicted_image_dir}/*")
+
+    return predicted_image_path[0]
+
+# Create the Gradio interface
+app = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="filepath", label="Upload Image"),
+    outputs=gr.Image(type="filepath", label="Detected Image"),
+    title="YOLOv10 Object Detection App",
+    description="Upload an image to detect blood cells (RBC, WBC, Platelets) using the fine-tuned YOLOv10 model."
+)
+
+# Launch the app
+app.launch(share=True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72c82d84ee8a69ee767873b795dca016186afc721bbfab86b8e6c88d736c135a
+size 5738931

requirements.txt

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+ultralytics
+opencv-python-headless
+gradio
+torch
+glob
+Image
+cv2
+matplotlib
+os
+shutil
+sklearn