app.py

import os
import io
import re
import streamlit as st

# Must be the first Streamlit command.
st.set_page_config(layout="wide", initial_sidebar_state="collapsed")

from PIL import Image
import fitz  # PyMuPDF

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
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont

# ---------------------------------------------------------------
# Define available NotoEmoji fonts (local files)
# One font is at the root and others are in the 'static' subdirectory.
available_fonts = {
    "NotoEmoji Variable": "NotoEmoji-VariableFont_wght.ttf",
    "NotoEmoji Bold": "NotoEmoji-Bold.ttf",
    "NotoEmoji Light": "NotoEmoji-Light.ttf",
    "NotoEmoji Medium": "NotoEmoji-Medium.ttf",
    "NotoEmoji Regular": "NotoEmoji-Regular.ttf",
    "NotoEmoji SemiBold": "NotoEmoji-SemiBold.ttf"
}

# Sidebar: Let the user choose the desired NotoEmoji font.
selected_font_name = st.sidebar.selectbox(
    "Select NotoEmoji Font",
    options=list(available_fonts.keys())
)
selected_font_path = available_fonts[selected_font_name]

# Register the chosen font with ReportLab.
pdfmetrics.registerFont(TTFont(selected_font_name, selected_font_path))

# ---------------------------------------------------------------
# Default markdown content with emojis.
default_markdown = """# Cutting-Edge ML Outline

## Core ML Techniques
1. 🌟 **Mixture of Experts (MoE)**
   - Conditional computation techniques
   - Sparse gating mechanisms
   - Training specialized sub-models

2. 🔥 **Supervised Fine-Tuning (SFT) using PyTorch**
   - Loss function customization
   - Gradient accumulation strategies
   - Learning rate schedulers

3. 🤖 **Large Language Models (LLM) using Transformers**
   - Attention mechanisms
   - Tokenization strategies
   - Position encodings

## Training Methods
4. 📊 **Self-Rewarding Learning using NPS 0-10 and Verbatims**
   - Custom reward functions
   - Feedback categorization
   - Signal extraction from text

5. 👍 **Reinforcement Learning from Human Feedback (RLHF)**
   - Preference datasets
   - PPO implementation
   - KL divergence constraints

6. 🔗 **MergeKit: Merging Models to Same Embedding Space**
   - TIES merging
   - Task arithmetic
   - SLERP interpolation

## Optimization & Deployment
7. 📏 **DistillKit: Model Size Reduction with Spectrum Analysis**
   - Knowledge distillation
   - Quantization techniques
   - Model pruning strategies

8. 🧠 **Agentic RAG Agents using Document Inputs**
   - Vector database integration
   - Query planning
   - Self-reflection mechanisms

9. ⏳ **Longitudinal Data Summarization from Multiple Docs**
   - Multi-document compression
   - Timeline extraction
   - Entity tracking

## Knowledge Representation
10. 📑 **Knowledge Extraction using Markdown Knowledge Graphs**
    - Entity recognition
    - Relationship mapping
    - Hierarchical structuring

11. 🗺️ **Knowledge Mapping with Mermaid Diagrams**
    - Flowchart generation
    - Sequence diagram creation
    - State diagrams

12. 💻 **ML Code Generation with Streamlit/Gradio/HTML5+JS**
    - Code completion
    - Unit test generation
    - Documentation synthesis
"""

# ---------------------------------------------------------------
# Process markdown into PDF content.
def markdown_to_pdf_content(markdown_text):
    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('# '):
            # Optionally skip the main title.
            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"<b>{section}</b>")
            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

# ---------------------------------------------------------------
# Create PDF using ReportLab.
def create_main_pdf(markdown_text, base_font_size=10, auto_size=False):
    buffer = io.BytesIO()
    doc = SimpleDocTemplate(
        buffer, 
        pagesize=(A4[1], A4[0]),
        leftMargin=36,
        rightMargin=36,
        topMargin=36,
        bottomMargin=36
    )
    
    styles = getSampleStyleSheet()
    story = []
    spacer_height = 10
    left_column, right_column = markdown_to_pdf_content(markdown_text)
    
    # Count total items to possibly adjust font size.
    total_items = 0
    for col in (left_column, right_column):
        for item in col:
            if isinstance(item, list):
                main_item, sub_items