app.py

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 gradio.mix import Parallel
from transformers import ImageClassificationPipeline, PerceiverForImageClassificationConvProcessing, PerceiverFeatureExtractor
from transformers import VisionEncoderDecoderModel
from transformers import AutoTokenizer
import torch
from transformers import (
    AutoModelForCausalLM,
    LogitsProcessorList,
    MinLengthLogitsProcessor,
    StoppingCriteriaList,
    MaxLengthCriteria,
)
import json
import os

from spaces_info import description, examples, initial_prompt_value

API_URL = os.getenv("API_URL")
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

##Bloom Inference API
API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
HF_TOKEN = os.environ["HF_TOKEN"]
headers = {"Authorization": f"Bearer {HF_TOKEN}"}

print(API_URL)
print(HF_API_TOKEN)


def query(payload):
    print(payload)
    response = requests.request("POST", API_URL, json=payload, headers={"Authorization": f"Bearer {HF_API_TOKEN}"})
    print(response)
    return json.loads(response.content.decode("utf-8"))

# https://github.com/NielsRogge/Transformers-Tutorials/blob/master/HuggingFace_vision_ecosystem_overview_(June_2022).ipynb
# option 1: load with randomly initialized weights (train from scratch)

#tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
#model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")







def inference(input_sentence, max_length, sample_or_greedy, seed=42):
    if sample_or_greedy == "Sample":
        parameters = {
            "max_new_tokens": max_length,
            "top_p": 0.9,
            "do_sample": True,
            "seed": seed,
            "early_stopping": False,
            "length_penalty": 0.0,
            "eos_token_id": None,
        }
    else:
        parameters = {
            "max_new_tokens": max_length,
            "do_sample": False,
            "seed": seed,
            "early_stopping": False,
            "length_penalty": 0.0,
            "eos_token_id": None,
        }

    payload = {"inputs": input_sentence, "parameters": parameters,"options" : {"use_cache": False} }

    data = query(payload)

    if "error" in data:
        return (None, None, f"<span style='color:red'>ERROR: {data['error']} </span>")

    generation = data[0]["generated_text"].split(input_sentence, 1)[1]
    return (
        before_prompt
        + input_sentence
        + prompt_to_generation
        + generation
        + after_generation,
        data[0]["generated_text"],
        "",
    )





def create_story(text_seed):
  #tokenizer = AutoTokenizer.from_pretrained("gpt2")
  #model = AutoModelForCausalLM.from_pretrained("gpt2")
  
  #eleutherAI gpt-3 based
  tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
  model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")

  # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
  model.config.pad_token_id = model.config.eos_token_id

  #input_prompt = "It might be possible to"
  input_prompt = text_seed
  input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids

  # instantiate logits processors
  logits_processor = LogitsProcessorList(
    [
        MinLengthLogitsProcessor(10, eos_token_id=model.config.eos_token_id),
    ]
  )
  stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=100)])

  outputs = model.greedy_search(
    input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
  )

  result_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
  return result_text






def self_caption(image):
  repo_name = "ydshieh/vit-gpt2-coco-en"
  #test_image = "cats.jpg"
  test_image = image
  #url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  #test_image = Image.open(requests.get(url, stream=True).raw)
  #test_image.save("cats.png")
  
  feature_extractor2 = ViTFeatureExtractor.from_pretrained(repo_name)
  tokenizer = AutoTokenizer.from_pretrained(repo_name)
  model2 = VisionEncoderDecoderModel.from_pretrained(repo_name)
  pixel_values = feature_extractor2(test_image, return_tensors="pt").pixel_values
  print("Pixel Values")
  print(pixel_values)
  # autoregressively generate text (using beam search or other decoding strategy)
  generated_ids = model2.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True)
  
  # decode into text
  preds = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)
  preds = [pred.strip() for pred in preds]
  print("Predictions")
  print(preds)
  print("The preds type is : ",type(preds))
  pred_keys = ["Prediction"]
  pred_value = preds

  pred_dictionary = dict(zip(pred_keys, pred_value))
  print("Pred dictionary")
  print(pred_dictionary)
  #return(pred_dictionary)
  preds = ' '.join(preds)
  #inference(input_sentence, max_length, sample_or_greedy, seed=42)
  story = inference(preds, 32, "Sample", 42) 
  #story = create_story(preds)
  #story = ' '.join(story)
  return story


def classify_image(image):
  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")

  #the following gets called by classify_image() 
  feature_extractor = PerceiverFeatureExtractor.from_pretrained("deepmind/vision-perceiver-conv")
  model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")
  #google/vit-base-patch16-224, deepmind/vision-perceiver-conv
  image_pipe = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor)
 
  
  results = image_pipe(image)
  
  print("RESULTS")
  print(results)
  # convert to format Gradio expects
  output = {}
  for prediction in results:
    predicted_label = prediction['label']
    score = prediction['score']
    output[predicted_label] = score
  print("OUTPUT")
  print(output)
  return output


image = gr.inputs.Image(type="pil")
label = gr.outputs.Label(num_top_classes=5)
#examples = [ ["cats.jpg"], ["batter.jpg"],["drinkers.jpg"] ]
examples = [ ["batter.jpg"] ] 
title = "Generate a Story from an Image"
description = "Demo for classifying images with Perceiver IO. To use it, simply upload an image and click 'submit', a story is autogenerated as well"
article = "<p style='text-align: center'></p>"

img_info1 = gr.Interface(
    fn=classify_image,
    inputs=image,
    outputs=label,
)

img_info2 = gr.Interface(
    fn=self_caption,
    inputs=image,
    #outputs=label,
    outputs = [
    gr.outputs.Textbox(label = 'Story')
],
)

Parallel(img_info1,img_info2, inputs=image, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True)
#Parallel(img_info1,img_info2, inputs=image, outputs=label, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True)