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import gradio as gr
import cv2
import numpy as np
from collections import Counter
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Download model from Hugging Face repo
MODEL_PATH = hf_hub_download(
repo_id="ibrahim313/Bioengineering_Query_Tool_image_based",
filename="best.pt"
)
# Load the YOLOv10 model
model = YOLO(MODEL_PATH)
def predict(image):
# Convert the image from BGR to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Perform prediction
results = model.predict(source=image_rgb, imgsz=640, conf=0.25)
# Get the annotated image
annotated_img = results[0].plot()
# Extract detection data
detections = results[0].boxes.data if results[0].boxes is not None else []
class_names = [model.names[int(cls)] for cls in detections[:, 5]] if len(detections) > 0 else []
count = Counter(class_names)
# Create a string representation of the detections
detection_str = ', '.join([f"{name}: {count}" for name, count in count.items()]) if class_names else "No detections"
return annotated_img, detection_str
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 Counts")],
title="Blood Cell Count",
description="Upload an image and YOLOv10 will detect blood cells."
)
app.launch()
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