Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import base64 | |
| from weasyprint import HTML | |
| import io | |
| # Define the 12-point ML outline with emojis | |
| ml_outline = [ | |
| "🌟 1. Mixture of Experts (MoE)", | |
| "🔥 2. Supervised Fine-Tuning (SFT) using PyTorch", | |
| "🤖 3. Large Language Models (LLM) using Transformers", | |
| "📊 4. Self-Rewarding Learning using NPS 0-10 and Verbatims", | |
| "👍 5. Reinforcement Learning from Human Feedback (RLHF)", | |
| "🔗 6. MergeKit: Merging Models to Same Embedding Space", | |
| "📏 7. DistillKit: Model Size Reduction with Spectrum Analysis", | |
| "🧠 8. Agentic RAG Agents using Document Inputs", | |
| "⏳ 9. Longitudinal Data Summarization from Multiple Docs", | |
| "📑 10. Knowledge Extraction using Markdown Knowledge Graphs", | |
| "🗺️ 11. Knowledge Mapping with Mermaid Diagrams", | |
| "💻 12. ML Code Generation with Streamlit/Gradio/HTML5+JS" | |
| ] | |
| def create_pdf_with_weasyprint(outline_items): | |
| # Create HTML content with two columns | |
| html_content = """ | |
| <html> | |
| <head> | |
| <style> | |
| body { | |
| font-family: Arial, sans-serif; | |
| margin: 20px; | |
| } | |
| .container { | |
| display: flex; | |
| width: 100%; | |
| height: 500px; | |
| } | |
| .column { | |
| width: 50%; | |
| padding: 10px; | |
| } | |
| h2 { | |
| color: #2c3e50; | |
| } | |
| ul { | |
| list-style-type: none; | |
| padding-left: 0; | |
| } | |
| li { | |
| margin: 8px 0; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <div class="column"> | |
| <h2>Cutting-Edge ML Areas (1-6)</h2> | |
| <ul> | |
| """ | |
| # Add items 1-6 | |
| for item in outline_items[:6]: | |
| html_content += f"<li>{item}</li>" | |
| html_content += """ | |
| </ul> | |
| </div> | |
| <div class="column"> | |
| <h2>Cutting-Edge ML Areas (7-12)</h2> | |
| <ul> | |
| """ | |
| # Add items 7-12 | |
| for item in outline_items[6:]: | |
| html_content += f"<li>{item}</li>" | |
| html_content += """ | |
| </ul> | |
| </div> | |
| </div> | |
| </body> | |
| </html> | |
| """ | |
| # Convert HTML to PDF | |
| buffer = io.BytesIO() | |
| HTML(string=html_content).write_pdf(buffer) | |
| pdf_bytes = buffer.getvalue() | |
| buffer.close() | |
| return pdf_bytes | |
| def get_binary_file_downloader_html(bin_file, file_label='File'): | |
| bin_str = base64.b64encode(bin_file).decode() | |
| href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{file_label}">Download {file_label}</a>' | |
| return href | |
| # Streamlit UI | |
| st.title("🚀 Cutting-Edge ML Outline Generator") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.header("📝 Markdown Outline") | |
| outline_text = "\n".join(ml_outline) | |
| st.markdown(outline_text) | |
| md_file = "ml_outline.md" | |
| with open(md_file, "w") as f: | |
| f.write(outline_text) | |
| st.markdown(get_binary_file_downloader_html(md_file.encode(), "ml_outline.md"), unsafe_allow_html=True) | |
| with col2: | |
| st.header("📑 PDF Preview") | |
| if st.button("Generate PDF"): | |
| with st.spinner("Generating PDF..."): | |
| try: | |
| pdf_bytes = create_pdf_with_weasyprint(ml_outline) | |
| # Save to file for download | |
| with open("ml_outline.pdf", "wb") as f: | |
| f.write(pdf_bytes) | |
| st.download_button( | |
| label="Download PDF", | |
| data=pdf_bytes, | |
| file_name="ml_outline.pdf", | |
| mime="application/pdf" | |
| ) | |
| base64_pdf = base64.b64encode(pdf_bytes).decode('utf-8') | |
| pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="400" type="application/pdf"></iframe>' | |
| st.markdown(pdf_display, unsafe_allow_html=True) | |
| except Exception as e: | |
| st.error(f"Error generating PDF: {str(e)}") | |
| st.markdown(""" | |
| <style> | |
| .stButton>button { | |
| background-color: #4CAF50; | |
| color: white; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) |