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import streamlit as st |
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import base64 |
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import pdfkit |
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import io |
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import os |
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ml_outline = [ |
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"🌟 1. Mixture of Experts (MoE)", |
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"🔥 2. Supervised Fine-Tuning (SFT) using PyTorch", |
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"🤖 3. Large Language Models (LLM) using Transformers", |
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"📊 4. Self-Rewarding Learning using NPS 0-10 and Verbatims", |
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"👍 5. Reinforcement Learning from Human Feedback (RLHF)", |
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"🔗 6. MergeKit: Merging Models to Same Embedding Space", |
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"📏 7. DistillKit: Model Size Reduction with Spectrum Analysis", |
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"🧠 8. Agentic RAG Agents using Document Inputs", |
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"⏳ 9. Longitudinal Data Summarization from Multiple Docs", |
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"📑 10. Knowledge Extraction using Markdown Knowledge Graphs", |
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"🗺️ 11. Knowledge Mapping with Mermaid Diagrams", |
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"💻 12. ML Code Generation with Streamlit/Gradio/HTML5+JS" |
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] |
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def create_pdf_with_pdfkit(outline_items): |
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html_content = """ |
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<html> |
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<head> |
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<meta charset="UTF-8"> |
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<style> |
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body { |
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font-family: Arial, sans-serif; |
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font-size: 12pt; |
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margin: 20px; |
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} |
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.container { |
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display: flex; |
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width: 100%; |
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} |
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.column { |
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width: 50%; |
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padding: 20px; |
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} |
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h2 { |
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color: #2c3e50; |
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font-size: 16pt; |
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} |
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ul { |
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list-style-type: none; |
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padding-left: 0; |
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} |
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li { |
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margin: 10px 0; |
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} |
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</style> |
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</head> |
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<body> |
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<div class="container"> |
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<div class="column"> |
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<h2>Cutting-Edge ML Areas (1-6)</h2> |
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<ul> |
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""" |
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for item in outline_items[:6]: |
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html_content += f"<li>{item}</li>" |
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html_content += """ |
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</ul> |
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</div> |
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<div class="column"> |
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<h2>Cutting-Edge ML Areas (7-12)</h2> |
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<ul> |
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""" |
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for item in outline_items[6:]: |
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html_content += f"<li>{item}</li>" |
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html_content += """ |
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</ul> |
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</div> |
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</div> |
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</body> |
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</html> |
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""" |
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options = { |
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'page-size': 'A4', |
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'orientation': 'Landscape', |
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'encoding': 'UTF-8', |
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'margin-top': '0.5in', |
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'margin-right': '0.5in', |
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'margin-bottom': '0.5in', |
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'margin-left': '0.5in', |
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} |
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pdf_bytes = pdfkit.from_string(html_content, False, options=options) |
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return pdf_bytes |
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def get_binary_file_downloader_html(bin_data, file_label='File'): |
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bin_str = base64.b64encode(bin_data).decode() |
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href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{file_label}">Download {file_label}</a>' |
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return href |
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st.title("🚀 Cutting-Edge ML Outline Generator") |
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col1, col2 = st.columns(2) |
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with col1: |
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st.header("📝 Markdown Outline") |
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outline_text = "\n".join(ml_outline) |
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st.markdown(outline_text) |
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md_file = "ml_outline.md" |
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with open(md_file, "w", encoding='utf-8') as f: |
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f.write(outline_text) |
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st.markdown(get_binary_file_downloader_html(outline_text.encode('utf-8'), "ml_outline.md"), unsafe_allow_html=True) |
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with col2: |
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st.header("📑 PDF Preview") |
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if st.button("Generate PDF"): |
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with st.spinner("Generating PDF..."): |
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try: |
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pdf_bytes = create_pdf_with_pdfkit(ml_outline) |
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with open("ml_outline.pdf", "wb") as f: |
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f.write(pdf_bytes) |
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st.download_button( |
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label="Download PDF", |
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data=pdf_bytes, |
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file_name="ml_outline.pdf", |
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mime="application/pdf" |
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) |
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base64_pdf = base64.b64encode(pdf_bytes).decode('utf-8') |
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pdf_display = f''' |
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<embed |
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src="data:application/pdf;base64,{base64_pdf}" |
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width="100%" |
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height="400px" |
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type="application/pdf"> |
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''' |
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st.markdown(pdf_display, unsafe_allow_html=True) |
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except Exception as e: |
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st.error(f"Error generating PDF: {str(e)}") |
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st.markdown(""" |
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<style> |
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.stButton>button { |
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background-color: #4CAF50; |
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color: white; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
