import torch
import gradio as gr
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer

model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}

def predict_step(image_paths):
  images = []
  for image_path in image_paths:
    i_image = Image.open(image_path)
    if i_image.mode != "RGB":
      i_image = i_image.convert(mode="RGB")
    images.append(i_image)
  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
  pixel_values = pixel_values.to(device)
  output_ids = model.generate(pixel_values, **gen_kwargs)
  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
  preds = [pred.strip() for pred in preds]
  return preds

#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/35-Favorite-Games.jpg', '35-Favorite-Games.jpg')

#result = predict_step(['35-Favorite-Games.jpg'])

def predict(image,max_length=64, num_beams=4):
  image = image.convert('RGB')
  image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
  clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
  caption_ids = model.generate(image, max_length = max_length)[0]
  caption_text = clean_text(tokenizer.decode(caption_ids))
  return caption_text 

description= "NLP Image Understanding"
title = "NLP Image Understanding"
article = "nlpconnect vit-gpt2-image-captioning"

input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
output = gr.outputs.Textbox(type="auto",label="Captions")

#examples = [['35-Favorite-Games.jpg']]
examples = [f"{i}.jpg" for i in range(1,20)]

interface = gr.Interface(
        fn=predict,
        inputs = input,
        outputs=output,
        examples = examples,
        title=title,
        description=description,
        article = article,
    )
interface.launch()