app.py

import streamlit as st
import torch
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file



# Model Path/Repo Information
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors" 

# Load model (Executed only once for efficiency)
@st.cache_resource
def load_sdxl_pipeline():
    unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cpu", torch.float32)
    unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cpu"))
    pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float32, variant="fp16").to("cpu")
    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
    return pipe


# Streamlit UI
st.title("Image Generation")
prompt = st.text_input("Enter your image prompt:")

if st.button("Generate Image"):
    if not prompt:
        st.warning("Please enter a prompt.")
    else:
        pipe = load_sdxl_pipeline()  # Load the pipeline from cache
        with torch.no_grad():
            image = pipe(prompt).images[0] 

        st.image(image)





# GOOGLE_API_KEY = ""
# genai.configure(api_key=GOOGLE_API_KEY)
# model = genai.GenerativeModel('gemini-pro')

# def add_to_json(goal):
#     try:
#         with open("test.json", "r") as file:
#             data = json.load(file)
#     except FileNotFoundError:
#         data = {"goals": []}  # Create the file with an empty 'goals' list if it doesn't exist

#     new_item = {"Goal": goal}
#     data["goals"].append(new_item)

#     with open("test.json", "w") as file:
#         json.dump(data, file, indent=4)
        


# def main():
#     if prompt := st.chat_input("Hi, how can I help you?"):
#         goals_prompt = f"""Act as a personal assistant... {prompt} """  
#         completion = model.generate_content(goals_prompt)
#         add_to_json(prompt)

#         with st.chat_message("Assistant"):
#             st.write(completion.text)

        

#     # Display JSON Data
#     if st.button("Show JSON Data"):
#         with open("test.json", "r") as file:
#             data = json.load(file)
#         st.json(data)  # Streamlit's way to display JSON


# if __name__ == "__main__":
#     main()