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import gradio as gr
import timm
import torch
import torch.nn as nn
from torchvision import datasets, transforms
from PIL import Image
from torch.utils.mobile_optimizer import optimize_for_mobile
model = timm.create_model('resnet50', pretrained=True)
model.fc = torch.nn.Linear(in_features=model.fc.in_features, out_features=5)
path = "epoch_4_Resnet50-0.5contrast.pth"
model.load_state_dict(torch.load(path))
model.eval()
def transform_image(img_sample):
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize to 224x224
transforms.ToTensor(), # Convert PIL image to tensor
transforms.ColorJitter(contrast=0.5), # Contrast
#transforms.RandomAdjustSharpness(sharpness_factor=0.5),
#transforms.RandomSolarize(threshold=0.75),
#transforms.RandomAutocontrast(p=1),
])
transformed_img = transform(img_sample)
return transformed_img
def predict(Image):
tranformed_img = transform_image(Image)
model.eval()
img = transform_image(Image)
img = img.reshape(1,3,224,224)
#img = torch.from_numpy(tranformed_img)
#outputs = model(img)
#class_out = outputs.argmax(dim=1)
with torch.no_grad():
grade = torch.softmax(model(img.float()), dim=1)[0]
category = ["0 - Normal", "1 - Mild", "2 - Moderate", "3 - Severe", "4 - Proliferative"]
output_dict = {}
for cat, value in zip(category, grade):
output_dict[cat] = value.item()
return output_dict
image = gr.Image(type="pil")#shape=(224, 224), image_mode="RGB")
label = gr.Label(label="Level")
demo = gr.Interface(
fn=predict,
inputs=image,
outputs=label,
#examples=["examples/0.png", "examples/1.png", "examples/2.png", "examples/3.png", "examples/4.png"]
examples=["0.jpeg", "2.jpeg", "4.jpeg"]
)
if __name__ == "__main__":
demo.launch(debug=True)
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