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| """ Code inspired by https://huggingface.co/spaces/flax-community/dalle-mini | |
| """ | |
| import base64 | |
| import os | |
| import time | |
| from io import BytesIO | |
| from multiprocessing import Process | |
| import streamlit as st | |
| from PIL import Image | |
| import requests | |
| import logging | |
| def start_server(): | |
| os.system("uvicorn server:app --port 8080 --host 0.0.0.0 --workers 1") | |
| def load_models(): | |
| if not is_port_in_use(8080): | |
| with st.spinner(text="Loading models, please wait..."): | |
| proc = Process(target=start_server, args=(), daemon=True) | |
| proc.start() | |
| while not is_port_in_use(8080): | |
| time.sleep(1) | |
| st.success("Model server started.") | |
| else: | |
| st.success("Model server already running...") | |
| st.session_state["models_loaded"] = True | |
| def is_port_in_use(port): | |
| import socket | |
| with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: | |
| return s.connect_ex(("0.0.0.0", port)) == 0 | |
| def generate(prompt): | |
| correct_request = f"http://0.0.0.0:8080/correct?prompt={prompt}" | |
| response = requests.get(correct_request) | |
| images = response.json()["images"] | |
| images = [Image.open(BytesIO(base64.b64decode(img))) for img in images] | |
| return images | |
| if "models_loaded" not in st.session_state: | |
| st.session_state["models_loaded"] = False | |
| st.header("Logo generator") | |
| #st.subheader("Generate images from text") | |
| st.write("Generate logos from text") | |
| if not st.session_state["models_loaded"]: | |
| load_models() | |
| prompt = st.text_input("Your text prompt. Tip: start with 'a logo of...':") | |
| DEBUG = False | |
| # UI code taken from https://huggingface.co/spaces/flax-community/dalle-mini/blob/main/app/streamlit/app.py | |
| if prompt != "": | |
| container = st.empty() | |
| container.markdown( | |
| f""" | |
| <style> p {{ margin:0 }} div {{ margin:0 }} </style> | |
| <div data-stale="false" class="element-container css-1e5imcs e1tzin5v1"> | |
| <div class="stAlert"> | |
| <div role="alert" data-baseweb="notification" class="st-ae st-af st-ag st-ah st-ai st-aj st-ak st-g3 st-am st-b8 st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-b9 st-b1 st-b2 st-b3 st-b4 st-b5 st-b6"> | |
| <div class="st-b7"> | |
| <div class="css-whx05o e13vu3m50"> | |
| <div data-testid="stMarkdownContainer" class="css-1ekf893 e16nr0p30"> | |
| <img src="https://raw.githubusercontent.com/borisdayma/dalle-mini/main/app/streamlit/img/loading.gif" width="30"/> | |
| Generating predictions for: <b>{prompt}</b> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| print(f"Getting selections: {prompt}") | |
| selected = generate(prompt) | |
| margin = 0.1 # for better position of zoom in arrow | |
| n_columns = 3 | |
| cols = st.columns([1] + [margin, 1] * (n_columns - 1)) | |
| for i, img in enumerate(selected): | |
| cols[(i % n_columns) * 2].image(img) | |
| container.markdown(f"**{prompt}**") | |
| st.button("Run again", key="again_button") | |