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app.py

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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")
+    
+