from transformers import ViTConfig, ViTForImageClassification
from transformers import ViTFeatureExtractor
from PIL import Image
import requests
import matplotlib.pyplot as plt
import gradio as gr
from transformers import ImageClassificationPipeline, PerceiverForImageClassificationConvProcessing, PerceiverFeatureExtractor
import torch


# option 1: load with randomly initialized weights (train from scratch)

config = ViTConfig(num_hidden_layers=12, hidden_size=768)
model = ViTForImageClassification(config)

#print(config)

feature_extractor = ViTFeatureExtractor()

# or, to load one that corresponds to a checkpoint on the hub:
#feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")


image = "cats.jpg"

 
feature_extractor = PerceiverFeatureExtractor.from_pretrained("deepmind/vision-perceiver-conv")
model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")

image_pipe = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor)

def classify_image(image):
  results = image_pipe(image)
  # convert to format Gradio expects
  output = {}
  for prediction in results:
    predicted_label = prediction['label']
    score = prediction['score']
    output[predicted_label] = score
  return output

image = gr.inputs.Image(type="pil")
label = gr.outputs.Label(num_top_classes=5)
examples = [["cats.jpg"], ["dog.jpg"]]
title = "Interactive demo: Perceiver for image classification"
description = "Demo for classifying images with Perceiver IO. To use it, simply upload an image or use the example images below and click 'submit' to let the model predict the 5 most probable ImageNet classes. Results will show up in a few seconds."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.14795'>Perceiver IO: A General Architecture for Structured Inputs & Outputs</a> | <a href='https://deepmind.com/blog/article/building-architectures-that-can-handle-the-worlds-data/'>Official blog</a></p>"

gr.Interface(fn=classify_image, inputs=image, outputs=label, title=title, description=description, examples="", enable_queue=True).launch(debug=True)