File size: 1,746 Bytes
aab0186 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import gradio as gr
from PIL import Image
class ConvModel(nn.Module):
def __init__(self):
super().__init__()
self.cnn1 = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.cnn2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.fc = nn.Sequential(
nn.Flatten(),
nn.Linear(32 * 56 * 56, 2)
)
def forward(self, x):
x = self.cnn1(x)
x = self.cnn2(x)
x = self.fc(x)
return x
model = ConvModel()
model.load_state_dict(torch.load("conv_model.pth", map_location="cpu"))
model.eval()
class_names=['NORMAL', 'PNEUMONIA']
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
def predict(img):
img = transform(img).unsqueeze(0)
with torch.inference_mode():
pred_probs = torch.softmax(model(img), dim=1)
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
return pred_labels_and_probs
title = "Zatürre Bulucu"
description = "Gönderilen fotoğrafa göre Sağlıklı mı yoksa Zatürre mi olduğunu tahmin eder."
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=2, label="Predictions")],
title=title,
description=description
)
demo.launch(debug=False, share=True) |