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import gradio as gr
from ultralytics import YOLO
from PIL import Image
import timm
from cods.classif.data import ClassificationDataset
from cods.classif.models import ClassificationModel
from cods.classif.cp import ClassificationConformalizer
def classif(img):
model_name = "resnet34"
pretrained_resnet_34 = timm.create_model(model_name, pretrained=True)
classifier = ClassificationModel(model=pretrained_resnet_34, model_name=model_name)
val_dataset = ClassificationDataset(...) # path to imagenet validation set
val_preds = classifier.build_predictions(
val_dataset,
dataset_name="imagenet",
split_name="cal",
batch_size=512,
shuffle=False,
)
cc = ClassificationConformalizer(method="lac", preprocess="softmax")
cc.lbd = 0.9
conf_cls = cc.conformalize(val_preds)
return str(conf_cls)
# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
def main_function(lbd, img):
results = model(img) # predict on an image
r = results[0]
im_bgr = r.plot() # BGR-order numpy array
im_rgb = Image.fromarray(im_bgr[..., ::-1]) # RGB-order PIL image
new_img = im_rgb
# res = results[0].save(filename="output.jpg") # save the image
# # load image
# new_img = Image.open("output.jpg")
return new_img
iface = gr.Interface(
fn=classif, # main_function,
inputs=gr.Image(type="pil"), # ["slider", gr.Image(type="pil")],
outputs=gr.Textbox(), # Image(type="pil"),
examples=[
"bus.jpg", # [0, "bus.jpg"],
],
)
iface.launch()
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