import os
import gradio as gr

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

summarizer = pipeline("summarization")
model_id = "runwayml/stable-diffusion-v1-5"

SAVED_CHECKPOINT = 'mikegarts/distilgpt2-lotr'
MIN_WORDS = 120

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

def get_image_pipe():
    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=READ_TOKEN)
    pipe.to('cuda')
    return pipe

def get_model():
    model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT)
    tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT)
    return model, tokenizer


def generate(prompt):
    model, tokenizer = get_model()

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

    outputs = model.generate(
        input_ids=input_ids, 
        max_length=100, 
        temperature=0.7, 
        num_return_sequences=3, 
        do_sample=True
    )

    return tokenizer.decode(outputs[0], skip_special_tokens=True).rsplit('.', 1)[0] + '.'

def make_image(prompt):
    pipe = get_image_pipe()
    image = pipe(prompt).images[0]  

def predict(prompt):
    story = generate(prompt=prompt)
    summary = summarizer(story, min_length=5, max_length=20)[0]['summary_text']
    image = make_image(summary)
    return story, summarizer(story, min_length=5, max_length=20), image


title = "Lord of the rings app"
description = """A Lord of the rings insired app that combines text and image generation"""

gr.Interface(
    fn=predict,
    inputs="textbox",
    outputs=["text", "text", "image"],
    title=title,
    description=description,
    examples=[["My new adventure would be"], ["Then I a hobbit appeared"], ["Frodo told me"]]
).launch(share=True)