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import torch
import gradio as gr
from torch import nn
from torch.nn import functional as F
import torchvision
from PIL import Image
from torchvision import transforms
transformer = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
model_best=torch.jit.load('model_scriptedd.pt',map_location=torch.device('cpu'))
classes=['Good luck','Love','Ok','Thumb up','Victory']
def predict(inp):
inp=transformer(inp).unsqueeze(0)
with torch.no_grad():
prediction =F.softmax(model_best(inp)[0], dim=0)
confidences = {classes[i]: float(prediction[i]) for i in range(5)}
return confidences
gr.Interface(predict,inputs=gr.inputs.Image(label="Input Image"),outputs='label').launch(debug='True')
#gr.Interface(predict,inputs=[gr.inputs.Image(label="Input Image", source="webcam"),gr.inputs.Image(label="Input Image")],outputs='label').launch(debug='True') |