import os
import gradio as gr

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline
from diffusers import StableDiffusionPipeline

READ_TOKEN = os.environ.get('HF_ACCESS_TOKEN', None)

model_id = "runwayml/stable-diffusion-v1-5"
# model_id = "CompVis/stable-diffusion-v1-4"

has_cuda = torch.cuda.is_available()

if has_cuda:
    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=READ_TOKEN)
    device = "cuda"
else:
    pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", use_auth_token=READ_TOKEN)
    device = "cpu"
    
pipe.to(device)
def safety_checker(images, clip_input):
    return images, False
pipe.safety_checker = safety_checker

SAVED_CHECKPOINT = 'mikegarts/distilgpt2-lotr'
model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT)
tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT)

summarizer = pipeline("summarization")

def break_until_dot(txt):
    return txt.rsplit('.', 1)[0] + '.'

def generate(prompt):
    input_context = prompt
    input_ids = tokenizer.encode(input_context, return_tensors="pt").to(model.device)

    outputs = model.generate(
        input_ids=input_ids, 
        max_length=180, 
        temperature=0.7, 
        num_return_sequences=3, 
        do_sample=True
    )
    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return break_until_dot(decoded)

def generate_image(prompt, inference_steps):
    prompt = prompt + ', masterpiece charcoal pencil art lord of the rings illustration'
    img = pipe(prompt, height=512, width=512, num_inference_steps=inference_steps)
    return img.images[0]

def generate_story(prompt):
    story = generate(prompt=prompt)
    summary = summarizer(story, min_length=5, max_length=15)[0]['summary_text']
    summary = break_until_dot(summary)
    return story, summary, gr.update(visible=True)

with gr.Blocks() as demo:
    title = gr.Markdown('## Lord of the rings app')
    description = gr.Markdown('### A Lord of the rings insired app that combines text and image generation')
    prompt = gr.Textbox(label="Your prompt", value="And then the hobbit said")
    story = gr.Textbox(label="Your story")
    summary = gr.Textbox(label="Summary")
    
    bt_make_text = gr.Button("Generate text")
    bt_make_image = gr.Button("Generate and image (takes about 10-15 minutes on CPU)", visible=False)
    
    image = gr.Image(label='Illustration for your story')
    inference_steps = gr.Slider(5, 30, value=15, step=1, label="Num inference steps (more steps makes a better image but takes more time)")
    
    bt_make_text.click(fn=generate_story, inputs=prompt, outputs=[story, summary, bt_make_image])
    bt_make_image.click(fn=generate_image, inputs=[summary, inference_steps], outputs=image)

if READ_TOKEN:
    demo.launch()
else:
    demo.launch(share=True, debug=True)