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import torch
from torchvision import models, transforms
from torch import nn, optim
from PIL import Image
import gradio as gr
# Load a pre-trained model (e.g., ResNet50)
model = models.resnet50(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, 2) # For binary classification (Thyroid: Positive/Negative)
# Define image transformation
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]),
])
# Example function to classify images
def classify_thyroid_image(image):
image = Image.open(image).convert("RGB")
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)
diagnosis = "Thyroid Disease Detected" if predicted.item() == 1 else "No Thyroid Disease"
return diagnosis
# Create a Gradio interface for image upload and camera input
gr.Interface(fn=classify_thyroid_image, inputs="image", outputs="text").launch()
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