import uuid
import json

import streamlit as st
from PIL import Image
from diffusers import StableDiffusionPipeline
import torch

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)

# pipe = pipe.to("cuda")

st.title("Convert Text To Image :sunglasses:")


def load_metadata():
    metadata = []
    try:
        with open("metadata.json", "r") as f:
            metadata = json.load(f)
    except:
        print("json file doesn't exist")
    return metadata


def update_metadata(data):
    metadata = []
    try:
        with open("metadata.json", "r") as f:
            metadata = json.load(f)
    except:
        print("json file doesn't exist")

    metadata.append(data)

    with open("metadata.json", "w") as json_file:
        json.dump(metadata, json_file)


def render_metadata(data):
    st.write("Previous prompt results (common for all)")
    for d in data:
        st.write("Prompt: ", d["prompt"])
        st.image(Image.open(d["file_name"]))


metadata = load_metadata()

with st.form("tti_form"):
    prompt = st.text_input("Enter Prompt")
    # Every form must have a submit button.
    submitted = st.form_submit_button("Submit")
    if submitted:
        with st.spinner("Processing..."):
            image_name = uuid.uuid4().hex + ".png"
            st.write("prompt", prompt)
            image = pipe(prompt).images[0]
            # test mode - comment above and uncomment following
            # image = Image.open("abcd.png")
            pil_image = Image.fromarray(image)

            st.image(image)
            # save image
            data = {"file_name": image_name, "prompt": prompt}
            pil_image.save(image_name)

            # with open(image_name, 'wb') as f:
            #     f.write(image)

            update_metadata(data)

        st.success("Image generated!")


render_metadata(metadata)