import os
import torch
import re
import gradio as gr
from pathlib import Path
from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel


# Pattern to ignore all the text after 2 or more full stops
regex_pattern = "[.]{2,}"


def post_process(text):
    try:
        text = text.strip()
        text = re.split(regex_pattern, text)[0]
    except Exception as e:
        print(e)
        pass
    return text


def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])


def predict(image, max_length=64, num_beams=4):
    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
    pixel_values = pixel_values.to(device)

    with torch.no_grad():
        output_ids = model.generate(
            pixel_values,
            max_length=max_length,
            num_beams=num_beams,
            return_dict_in_generate=True,
        ).sequences

    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    pred = post_process(preds[0])

    return pred


model_name_or_path = "deepklarity/poster2plot"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load model.

model = VisionEncoderDecoderModel.from_pretrained(model_name_or_path)
model.to(device)
print("Loaded model")

feature_extractor = AutoFeatureExtractor.from_pretrained(model.encoder.name_or_path)
print("Loaded feature_extractor")

tokenizer = AutoTokenizer.from_pretrained(model.decoder.name_or_path, use_fast=True)
if model.decoder.name_or_path == "gpt2":
    tokenizer.pad_token = tokenizer.eos_token

print("Loaded tokenizer")

title = "Poster2Plot: Upload a Movie/T.V show poster to generate a plot"
description = ""

input = gr.inputs.Image(type="pil")

example_images = sorted(
    [f.as_posix() for f in Path("examples").glob("*.jpg")]
)
print(f"Loaded {len(example_images)} example images")

demo = gr.Blocks()
filenames = next(os.walk('examples'), (None, None, []))[2]
examples = [[f"examples/{filename}"] for filename in filenames]
print(examples)

with demo:
    with gr.Column():
        with gr.Row():
            with gr.Column():
                input_image = gr.Image()
                with gr.Row():
                    clear_button = gr.Button(value="Clear", variant='secondary')
                    submit_button = gr.Button(value="Submit", variant='primary')
            with gr.Column():
                plot = gr.Textbox()
        with gr.Row():
            example_images = gr.Dataset(components=[input_image], samples=examples)

    submit_button.click(fn=predict, inputs=[input_image], outputs=[plot])
    example_images.click(fn=set_example_image, inputs=[example_images], outputs=example_images.components)

demo.launch()