import sys
# if 'google.colab' in sys.modules:
#     print('Running in Colab.')
#     !pip3 install transformers==4.15.0 timm==0.4.12 fairscale==0.4.4
#     !git clone https://github.com/salesforce/BLIP
#     %cd BLIP
import gradio as gr
import torch
import requests
from torchvision import transforms
from PIL import Image
import requests
import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode


#@title
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def predict(inp):
  inp = transforms.ToTensor()(inp).unsqueeze(0)
  with torch.no_grad():
    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
  return confidences

demo = gr.Interface(fn=predict, 
             inputs=gr.inputs.Image(type="pil"),
             outputs=gr.outputs.Label(num_top_classes=3)
             )
             
def load_demo_image(image_size,device,imageurl):
    img_url = imageurl
    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')   

    w,h = raw_image.size
    display(raw_image.resize((w//5,h//5)))
    
    transform = transforms.Compose([
        transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
        transforms.ToTensor(),
        transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
        ]) 
    image = transform(raw_image).unsqueeze(0).to(device)   
    return image
from models.blip import blip_decoder

def predict(imageurl):
  image_size = 384
  image = load_demo_image(image_size=image_size, device=device,imageurl=imageurl)

  model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
      
  model = blip_decoder(pretrained=model_url, image_size=image_size, vit='base')
  model.eval()
  model = model.to(device)

  with torch.no_grad():
      # beam search
      caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) 
      # nucleus sampling
      # caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) 
  return('caption: '+caption[0])
    
demo = gr.Interface(fn=predict, 
     inputs="text",
     outputs=gr.outputs.Label(num_top_classes=3)
     )   

demo.launch()