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import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr
# Load your trained model
with torch.no_grad():
model = torch.load('classifier.pt')
# Define the preprocessing function for the input image
def preprocess(image):
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]),
])
image = Image.fromarray(image.astype('uint8'), 'RGB')
image = transform(image)
return image.unsqueeze(0)
# Define the predict function
def predict(image):
# Preprocess the image
input_tensor = preprocess(image)
# Make a prediction
with torch.no_grad():
output = model(input_tensor)
# Perform post-processing if needed (e.g., softmax for probabilities)
# Replace this with your actual post-processing logic
probabilities = torch.softmax(output.logits, dim=1).squeeze().tolist()
# Map the class indices to class labels
class_labels = ["Cat", "Dog", "Horse", "Monkey"]
# Create a dictionary with class labels and probabilities
predictions = {label: prob for label, prob in zip(class_labels, probabilities)}
return predictions
# Create the Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=4),
live=True
)
# Launch the Gradio app
iface.launch(quiet=True)
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