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import torch
import torch.nn as nn
from PIL import Image
from torchvision import transforms
import torchvision
import gradio as gr
weights = torchvision.models.GoogLeNet_Weights.DEFAULT
transfer_model_transformer = weights.transforms()
transfer_model = torchvision.models.googlenet(weights=weights)
transfer_model.classifier = nn.Sequential(
nn.Dropout(p=0.2), nn.Linear(in_features=1024, out_features=512), nn.ReLU(),
nn.Linear(in_features=512, out_features=256), nn.ReLU(),
nn.Linear(in_features=256, out_features=128), nn.ReLU(),
nn.Linear(in_features=128, out_features=64), nn.ReLU(),
nn.Linear(in_features=64, out_features=32), nn.ReLU(),
nn.Linear(in_features=32, out_features=16), nn.ReLU(),
nn.Linear(in_features=16, out_features=8), nn.ReLU(),
nn.Linear(in_features=8, out_features=4), nn.ReLU(),
nn.Linear(in_features=4, out_features=2)
)
# Modeli CPU üzerinde yükle
transfer_model.load_state_dict(torch.load("best_model_transfer.pth", map_location=torch.device('cpu')))
class_names = ['Tere', 'Roka']
def predict(img):
"""Transforms and performs a prediction on img and returns prediction and time taken."""
# Transform the target image and add a batch dimension
img = transfer_model_transformer(img).unsqueeze(0)
# Put model into evaluation mode and turn on inference mode
transfer_model.eval()
transfer_model.to("cpu")
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(transfer_model(img), dim=1)
# Create a prediction label and prediction probability dictionary for each prediction class
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
return pred_labels_and_probs
# Create title, description and article strings
title = "CRESS ARUGULA DISTINCTIVE"
description = "An artificial intelligence application that recognizes whether the photo uploaded to the system is cress or arugula."
# Create the Gradio demo
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=len(class_names), label="Predictions")],
title=title,
description=description
)
# Launch the demo!
demo.launch(debug=False, share=True)
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