import streamlit as st import base64 from ml import MLModel from dl import DLModel st.set_page_config(page_title="Drawing with LLM", page_icon="🎨", layout="wide") @st.cache_resource def load_ml_model(): return MLModel(device="cuda" if st.session_state.get("use_gpu", True) else "cpu") @st.cache_resource def load_dl_model(): return DLModel(device="cuda" if st.session_state.get("use_gpu", True) else "cpu") def render_svg(svg_content): b64 = base64.b64encode(svg_content.encode("utf-8")).decode("utf-8") return f'' st.title("Drawing with LLM 🎨") with st.sidebar: st.header("Settings") model_type = st.selectbox("Model Type", ["ML Model (vtracer)", "DL Model (starvector)"]) use_gpu = st.checkbox("Use GPU", value=True) st.session_state["use_gpu"] = use_gpu if model_type == "ML Model (vtracer)": st.subheader("ML Model Settings") simplify = st.checkbox("Simplify SVG", value=True) color_precision = st.slider("Color Precision", 1, 10, 6) filter_speckle = st.slider("Filter Speckle", 0, 10, 4) path_precision = st.slider("Path Precision", 1, 10, 8) prompt = st.text_area("Enter your description", "A cat sitting on a windowsill at sunset") if st.button("Generate SVG"): with st.spinner("Generating SVG..."): if model_type == "ML Model (vtracer)": model = load_ml_model() svg_content = model.predict( prompt, simplify=simplify, color_precision=color_precision, filter_speckle=filter_speckle, path_precision=path_precision ) else: model = load_dl_model() svg_content = model.predict(prompt) col1, col2 = st.columns(2) with col1: st.subheader("Generated SVG") st.markdown(render_svg(svg_content), unsafe_allow_html=True) with col2: st.subheader("SVG Code") st.code(svg_content, language="xml") # Download button for SVG st.download_button( label="Download SVG", data=svg_content, file_name="generated_svg.svg", mime="image/svg+xml" ) st.markdown("---") st.markdown("This app uses Stable Diffusion to generate images from text and converts them to SVG.")