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4c89a16 b27384b d96a71d 4c89a16 c1a1ba5 b27384b c1a1ba5 d96a71d 4c89a16 d96a71d c1a1ba5 d96a71d 4c89a16 d96a71d 4c89a16 d96a71d 4c89a16 c1a1ba5 d96a71d 4c89a16 b27384b d96a71d 4c89a16 c1a1ba5 b27384b d96a71d | 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 | import torch
from torch import nn
from torchvision import models, transforms
from PIL import Image
import gradio as gr
import numpy as np
# Define model (pretrained ResNet50)
model = models.resnet50(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, 3) # 3 output classes: Normal, Hypothyroidism, Hyperthyroidism
# Define image transformations (resizing and normalization)
transform = transforms.Compose([
transforms.Resize((224, 224)), # Resize image for input
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Example classification function
def classify_thyroid_condition(image):
image = Image.fromarray(image.astype('uint8'), 'RGB') # Convert numpy array to Pillow Image
image = transform(image).unsqueeze(0) # Add batch dimension
model.eval() # Set the model to evaluation mode
with torch.no_grad():
output = model(image)
_, predicted = torch.max(output, 1)
# Map prediction to class labels
if predicted.item() == 0:
diagnosis = "Normal"
elif predicted.item() == 1:
diagnosis = "Hypothyroidism"
else:
diagnosis = "Hyperthyroidism"
return diagnosis
# Create Gradio interface for image input
gr.Interface(fn=classify_thyroid_condition, inputs="image", outputs="text").launch()
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