import gradio as gr
from transformers import AutoProcessor, BlipForConditionalGeneration

# from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel
import torch

blip_processor_large = AutoProcessor.from_pretrained("umm-maybe/image-generator-identifier")
blip_model_large = BlipForConditionalGeneration.from_pretrained("umm-maybe/image-generator-identifier")

device = "cuda" if torch.cuda.is_available() else "cpu"

blip_model_large.to(device)

def generate_caption(processor, model, image, tokenizer=None, use_float_16=False):
    inputs = processor(images=image, return_tensors="pt").to(device)

    if use_float_16:
        inputs = inputs.to(torch.float16)
    
    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)

    if tokenizer is not None:
        generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    else:
        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
   
    return generated_caption


def generate_captions(image):

    caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)

    return caption_blip_large


   
examples = [["australia.jpg"], ["biden.png"], ["elon.jpg"], ["horns.jpg"], ["man.jpg"], ["nun.jpg"], ["painting.jpg"], ["pentagon.jpg"], ["pollock.jpg"], ["radcliffe.jpg"], ["split.jpg"], ["waves.jpg"], ["yeti.jpg"]]
outputs = [
           gr.outputs.Textbox(label="Caption including detected generator (if applicable)"),
          ] 

title = "Generator Identification via Image Captioning"
description = "Gradio Demo to illustrate the use of a fine-tuned BLIP image captioning to identify synthetic images.  To use it, simply upload your image and click 'submit', or click one of the examples to load them."

interface = gr.Interface(fn=generate_captions, 
                         inputs=gr.inputs.Image(type="pil"),
                         outputs=outputs,
                         examples=examples, 
                         title=title,
                         description=description,
                         enable_queue=True)
interface.launch()