import os
env_var = os.environ.get('env')
import torch
import time 
import numpy as np
import gradio as gr
from PIL import Image
import torchvision 
from torchvision import transforms

device = 'cpu'
model = torch.load('model.pkl').to(device).eval()
transform = transforms.Resize(size=500)
labels = ['Cat', 'Dog']

def predict(image):
    start = time.time()
    with torch.no_grad():
        image = Image.fromarray(np.uint8(image)).convert('RGB')
        image = transform(image)
        image = np.array(image)
        image = torch.from_numpy(image).permute(2,0,1).float()
        image = image.unsqueeze(0)
        prediction = model(image.to(device))
        pred_idx = np.argmax(prediction.to(device))
        pred_label = "Cat" if pred_idx == 0 else "Dog"
        label = [l for l in labels if l!=pred_label]
        confidences = {pred_label: float(prediction[0][pred_idx])/100, label[len(label)-1]: 1-(float(prediction[0][pred_idx]))/100 }
        infer = time.time()-start
    return confidences, infer

gr.Interface(fn=predict,
             inputs=gr.Image(), 
             outputs=[gr.Label(num_top_classes=3), gr.Textbox('infer',label='Inference Time')],
             examples='1.jpg cat.jpg chinese-dog-breeds.jpg gettyimages.jpg kitty.jpeg'.split(' ')).launch()