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import gradio as gr
import cv2
import numpy as np
import pandas as pd
from collections import Counter
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# # Download YOLOv10 model from Hugging Face
# MODEL_PATH = hf_hub_download(
# repo_id="ibrahim313/Bioengineering_Query_Tool_image_based",
# filename="best.pt"
# )
# Load the model
model = YOLO("best.pt")
def process_image(image):
"""Detect cells in the image, extract attributes, and return results."""
# Convert image to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Perform detection
results = model.predict(source=image_rgb, imgsz=640, conf=0.25)
# Get annotated image
annotated_img = results[0].plot()
# Extract detection data
detections = results[0].boxes.data if results[0].boxes is not None else []
if len(detections) > 0:
class_names = [model.names[int(cls)] for cls in detections[:, 5]]
count = Counter(class_names)
detection_str = ', '.join([f"{name}: {count[name]}" for name in count])
# Extract cell attributes (position, size, etc.)
df = pd.DataFrame(detections.numpy(), columns=["x_min", "y_min", "x_max", "y_max", "confidence", "class"])
df["class_name"] = df["class"].apply(lambda x: model.names[int(x)])
df["width"] = df["x_max"] - df["x_min"]
df["height"] = df["y_max"] - df["y_min"]
df["area"] = df["width"] * df["height"]
summary = df.groupby("class_name")["area"].describe().reset_index()
else:
detection_str = "No detections"
summary = pd.DataFrame(columns=["class_name", "count", "mean", "std", "min", "25%", "50%", "75%", "max"])
return annotated_img, detection_str, summary
# Create Gradio interface
app = gr.Interface(
fn=process_image,
inputs=gr.Image(type="numpy", label="Upload an Image"),
outputs=[
gr.Image(type="numpy", label="Annotated Image"),
gr.Textbox(label="Detection Counts"),
gr.Dataframe(label="Cell Statistics")
],
title="Bioengineering Image Analysis Tool",
description="Upload an image to detect and analyze bioengineering cells using YOLOv10."
)
app.launch() |