import streamlit as st
import base64
from reportlab.lib.pagesizes import A4
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib import colors
import io
import re
# Define the ML outline as a markdown string
ml_markdown = """# Cutting-Edge ML Outline
## Core ML Techniques
1. π **Mixture of Experts (MoE)**
- Conditional computation techniques
- Sparse gating mechanisms
- Training specialized sub-models
2. π₯ **Supervised Fine-Tuning (SFT) using PyTorch**
- Loss function customization
- Gradient accumulation strategies
- Learning rate schedulers
3. π€ **Large Language Models (LLM) using Transformers**
- Attention mechanisms
- Tokenization strategies
- Position encodings
## Training Methods
4. π **Self-Rewarding Learning using NPS 0-10 and Verbatims**
- Custom reward functions
- Feedback categorization
- Signal extraction from text
5. π **Reinforcement Learning from Human Feedback (RLHF)**
- Preference datasets
- PPO implementation
- KL divergence constraints
6. π **MergeKit: Merging Models to Same Embedding Space**
- TIES merging
- Task arithmetic
- SLERP interpolation
## Optimization & Deployment
7. π **DistillKit: Model Size Reduction with Spectrum Analysis**
- Knowledge distillation
- Quantization techniques
- Model pruning strategies
8. π§ **Agentic RAG Agents using Document Inputs**
- Vector database integration
- Query planning
- Self-reflection mechanisms
9. β³ **Longitudinal Data Summarization from Multiple Docs**
- Multi-document compression
- Timeline extraction
- Entity tracking
## Knowledge Representation
10. π **Knowledge Extraction using Markdown Knowledge Graphs**
- Entity recognition
- Relationship mapping
- Hierarchical structuring
11. πΊοΈ **Knowledge Mapping with Mermaid Diagrams**
- Flowchart generation
- Sequence diagram creation
- State diagrams
12. π» **ML Code Generation with Streamlit/Gradio/HTML5+JS**
- Code completion
- Unit test generation
- Documentation synthesis
"""
# Process multilevel markdown for PDF output
def markdown_to_pdf_content(markdown_text):
"""Convert markdown text to a format suitable for PDF generation"""
lines = markdown_text.strip().split('\n')
pdf_content = []
in_list_item = False
current_item = None
sub_items = []
for line in lines:
line = line.strip()
if not line:
continue
if line.startswith('# '):
pass
elif line.startswith('## '):
if current_item and sub_items:
pdf_content.append([current_item, sub_items])
sub_items = []
current_item = None
section = line.replace('## ', '').strip()
pdf_content.append(f"{section}")
in_list_item = False
elif re.match(r'^\d+\.', line):
if current_item and sub_items:
pdf_content.append([current_item, sub_items])
sub_items = []
current_item = line.strip()
in_list_item = True
elif line.startswith('- ') and in_list_item:
sub_items.append(line.strip())
else:
if not in_list_item:
pdf_content.append(line.strip())
if current_item and sub_items:
pdf_content.append([current_item, sub_items])
mid_point = len(pdf_content) // 2
left_column = pdf_content[:mid_point]
right_column = pdf_content[mid_point:]
return left_column, right_column
# Main PDF creation using ReportLab
def create_main_pdf(markdown_text):
"""Create a single-page landscape PDF with the outline in two columns"""
buffer = io.BytesIO()
doc = SimpleDocTemplate(
buffer,
pagesize=(A4[1], A4[0]), # Landscape
leftMargin=50,
rightMargin=50,
topMargin=50,
bottomMargin=50
)
styles = getSampleStyleSheet()
story = []
# Create custom styles
title_style = styles['Heading1']
title_style.textColor = colors.darkblue
title_style.alignment = 1 # Center alignment
section_style = ParagraphStyle(
'SectionStyle',
parent=styles['Heading2'],
textColor=colors.darkblue,
spaceAfter=6
)
item_style = ParagraphStyle(
'ItemStyle',
parent=styles['Normal'],
fontSize=11,
leading=14,
fontName='Helvetica-Bold'
)
subitem_style = ParagraphStyle(
'SubItemStyle',
parent=styles['Normal'],
fontSize=10,
leading=12,
leftIndent=20
)
# Add title
story.append(Paragraph("Cutting-Edge ML Outline (ReportLab)", title_style))
story.append(Spacer(1, 20))
# Process markdown content
left_column, right_column = markdown_to_pdf_content(markdown_text)
# Prepare data for table
left_cells = []
for item in left_column:
if isinstance(item, str) and item.startswith(''):
text = item.replace('', '').replace('', '')
left_cells.append(Paragraph(text, section_style))
elif isinstance(item, list):
main_item, sub_items = item
left_cells.append(Paragraph(main_item, item_style))
for sub_item in sub_items:
left_cells.append(Paragraph(sub_item, subitem_style))
else:
left_cells.append(Paragraph(item, item_style))
right_cells = []
for item in right_column:
if isinstance(item, str) and item.startswith(''):
text = item.replace('', '').replace('', '')
right_cells.append(Paragraph(text, section_style))
elif isinstance(item, list):
main_item, sub_items = item
right_cells.append(Paragraph(main_item, item_style))
for sub_item in sub_items:
right_cells.append(Paragraph(sub_item, subitem_style))
else:
right_cells.append(Paragraph(item, item_style))
# Make columns equal length
max_cells = max(len(left_cells), len(right_cells))
left_cells.extend([""] * (max_cells - len(left_cells)))
right_cells.extend([""] * (max_cells - len(right_cells)))
# Create table data
table_data = list(zip(left_cells, right_cells))
# Calculate column widths
col_width = (A4[1] - 120) / 2.0
# Create and style table
table = Table(table_data, colWidths=[col_width, col_width])
table.setStyle(TableStyle([
('VALIGN', (0, 0), (-1, -1), 'TOP'),
('ALIGN', (0, 0), (0, -1), 'LEFT'),
('ALIGN', (1, 0), (1, -1), 'LEFT'),
('BACKGROUND', (0, 0), (-1, -1), colors.white),
('GRID', (0, 0), (-1, -1), 0.5, colors.white),
('LINEAFTER', (0, 0), (0, -1), 1, colors.grey),
]))
story.append(table)
doc.build(story)
buffer.seek(0)
return buffer.getvalue()
# Streamlit UI
st.title("π Cutting-Edge ML Outline Generator")
if st.button("Generate Main PDF"):
with st.spinner("Generating PDF..."):
pdf_bytes = create_main_pdf(ml_markdown)
st.download_button(
label="Download Main PDF",
data=pdf_bytes,
file_name="ml_outline.pdf",
mime="application/pdf"
)
base64_pdf = base64.b64encode(pdf_bytes).decode('utf-8')
pdf_display = f'