Spaces:
Sleeping
Sleeping
import streamlit as st | |
import base64 | |
from reportlab.lib.pagesizes import A4 | |
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer | |
from reportlab.lib.styles import getSampleStyleSheet | |
from reportlab.lib import colors | |
import pikepdf | |
import fpdf | |
import fitz # pymupdf | |
import cv2 | |
from PIL import Image | |
import imutils.video | |
import io | |
import os | |
# Define the 12-point ML outline with emojis | |
ml_outline = [ | |
"π 1. Mixture of Experts (MoE)", | |
"π₯ 2. Supervised Fine-Tuning (SFT) using PyTorch", | |
"π€ 3. Large Language Models (LLM) using Transformers", | |
"π 4. Self-Rewarding Learning using NPS 0-10 and Verbatims", | |
"π 5. Reinforcement Learning from Human Feedback (RLHF)", | |
"π 6. MergeKit: Merging Models to Same Embedding Space", | |
"π 7. DistillKit: Model Size Reduction with Spectrum Analysis", | |
"π§ 8. Agentic RAG Agents using Document Inputs", | |
"β³ 9. Longitudinal Data Summarization from Multiple Docs", | |
"π 10. Knowledge Extraction using Markdown Knowledge Graphs", | |
"πΊοΈ 11. Knowledge Mapping with Mermaid Diagrams", | |
"π» 12. ML Code Generation with Streamlit/Gradio/HTML5+JS" | |
] | |
# Demo functions for PDF libraries | |
def demo_pikepdf(): | |
pdf = pikepdf.Pdf.new() | |
pdf.pages.append(pikepdf.Page(pikepdf.Dictionary())) | |
buffer = io.BytesIO() | |
pdf.save(buffer) | |
buffer.seek(0) | |
return buffer.getvalue() | |
def demo_fpdf(): | |
pdf = fpdf.FPDF() | |
pdf.add_page() | |
pdf.set_font("Arial", size=12) | |
pdf.cell(200, 10, txt="FPDF Demo", ln=True) | |
buffer = io.BytesIO() | |
pdf.output(buffer) | |
buffer.seek(0) | |
return buffer.getvalue() | |
def demo_pymupdf(): | |
doc = fitz.open() | |
page = doc.new_page() | |
page.insert_text((100, 100), "PyMuPDF Demo") | |
buffer = io.BytesIO() | |
doc.save(buffer) | |
buffer.seek(0) | |
return buffer.getvalue() | |
# Demo function for image capture (using OpenCV as representative) | |
def demo_image_capture(): | |
try: | |
cap = cv2.VideoCapture(0) | |
ret, frame = cap.read() | |
if ret: | |
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
img = Image.fromarray(rgb_frame) | |
buffer = io.BytesIO() | |
img.save(buffer, format="JPEG") | |
buffer.seek(0) | |
cap.release() | |
return buffer.getvalue() | |
cap.release() | |
except: | |
return None | |
return None | |
# Main PDF creation using ReportLab | |
def create_main_pdf(outline_items): | |
buffer = io.BytesIO() | |
doc = SimpleDocTemplate(buffer, pagesize=(A4[1], A4[0])) # Landscape | |
styles = getSampleStyleSheet() | |
story = [] | |
# Title style | |
title_style = styles['Heading1'] | |
title_style.textColor = colors.darkblue | |
# Normal style | |
normal_style = styles['Normal'] | |
normal_style.fontSize = 10 | |
normal_style.leading = 14 | |
# Page 1: Items 1-6 | |
story.append(Paragraph("Cutting-Edge ML Areas (1-6)", title_style)) | |
story.append(Spacer(1, 12)) | |
for item in outline_items[:6]: | |
story.append(Paragraph(item, normal_style)) | |
story.append(Spacer(1, 6)) | |
# Page break | |
story.append(Spacer(1, 500)) # Force new page | |
# Page 2: Items 7-12 | |
story.append(Paragraph("Cutting-Edge ML Areas (7-12)", title_style)) | |
story.append(Spacer(1, 12)) | |
for item in outline_items[6:]: | |
story.append(Paragraph(item, normal_style)) | |
story.append(Spacer(1, 6)) | |
doc.build(story) | |
buffer.seek(0) | |
return buffer.getvalue() | |
def get_binary_file_downloader_html(bin_data, file_label='File'): | |
bin_str = base64.b64encode(bin_data).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("π Cutting-Edge ML Outline Generator") | |
col1, col2 = st.columns(2) | |
with col1: | |
st.header("π Markdown Outline") | |
outline_text = "\n".join(ml_outline) | |
st.markdown(outline_text) | |
md_file = "ml_outline.md" | |
with open(md_file, "w", encoding='utf-8') as f: | |
f.write(outline_text) | |
st.markdown(get_binary_file_downloader_html(outline_text.encode('utf-8'), "ml_outline.md"), unsafe_allow_html=True) | |
with col2: | |
st.header("π PDF Preview & Demos") | |
# Library Demos | |
st.subheader("Library Demos") | |
if st.button("Run PDF Demos"): | |
with st.spinner("Running demos..."): | |
# pikepdf demo | |
pike_pdf = demo_pikepdf() | |
st.download_button("Download pikepdf Demo", pike_pdf, "pikepdf_demo.pdf") | |
# fpdf demo | |
fpdf_pdf = demo_fpdf() | |
st.download_button("Download fpdf Demo", fpdf_pdf, "fpdf_demo.pdf") | |
# pymupdf demo | |
pymupdf_pdf = demo_pymupdf() | |
st.download_button("Download pymupdf Demo", pymupdf_pdf, "pymupdf_demo.pdf") | |
# Image capture demo | |
img_data = demo_image_capture() | |
if img_data: | |
st.image(img_data, caption="OpenCV Image Capture Demo") | |
else: | |
st.warning("Image capture demo failed - camera not detected") | |
# Main PDF Generation | |
if st.button("Generate Main PDF"): | |
with st.spinner("Generating PDF..."): | |
try: | |
pdf_bytes = create_main_pdf(ml_outline) | |
with open("ml_outline.pdf", "wb") as f: | |
f.write(pdf_bytes) | |
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''' | |
<embed | |
src="data:application/pdf;base64,{base64_pdf}" | |
width="100%" | |
height="400px" | |
type="application/pdf"> | |
''' | |
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) |