Update app.py
Browse files
app.py
CHANGED
@@ -1,1379 +1,115 @@
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#
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import
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import os
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import re
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import base64
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import glob
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import
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import random
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import shutil
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import time
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import zipfile
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import json
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import asyncio
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import aiofiles
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from datetime import datetime
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from collections import Counter
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from dataclasses import dataclass, field
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from io import BytesIO
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from typing import Optional, List, Dict, Any
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import pandas as pd
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import
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from
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from reportlab.pdfgen import canvas
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from reportlab.lib.utils import ImageReader
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from
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as PlatypusImage, PageBreak, Preformatted
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from reportlab.lib.styles import getSampleStyleSheet
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from reportlab.lib.units import inch
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from reportlab.lib.enums import TA_CENTER, TA_LEFT # For text alignment
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import fitz # PyMuPDF
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# --- Hugging Face Imports ---
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from huggingface_hub import InferenceClient, HfApi, list_models
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from huggingface_hub.utils import RepositoryNotFoundError, GatedRepoError # Import specific exceptions
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# ---
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st.set_page_config(
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page_title="Vision & Layout Titans
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page_icon="🤖",
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layout="wide"
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initial_sidebar_state="expanded",
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menu_items={
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'Get Help': 'https://huggingface.co/docs',
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'Report a Bug': None, # Replace with your bug report link if desired
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'About': "Combined App: PDF Layout Generator + Hugging Face Powered AI Tools 🌌"
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}
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)
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"
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]
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]
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st.
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st.
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st.
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st.
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st.
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#
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st.
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st.
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st.session_state.setdefault('gen_max_tokens', 512)
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st.session_state.setdefault('gen_temperature', 0.7)
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st.session_state.setdefault('gen_top_p', 0.95)
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st.session_state.setdefault('gen_frequency_penalty', 0.0) # Corresponds to repetition_penalty=1.0
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st.session_state.setdefault('gen_seed', -1) # -1 for random
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# Removed asset_gallery_container - render directly in sidebar
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# if 'asset_gallery_container' not in st.session_state:
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# st.session_state['asset_gallery_container'] = st.sidebar.empty()
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# --- Dataclasses (Refined for Local Models) -------------
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@dataclass
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class LocalModelConfig:
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name: str # User-defined local name
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hf_id: str # Hugging Face model ID used for download
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model_type: str # 'causal', 'vision', 'diffusion', 'ocr', etc.
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size_category: str = "unknown" # e.g., 'small', 'medium', 'large'
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domain: Optional[str] = None
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local_path: str = field(init=False) # Path where it's saved
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def __post_init__(self):
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# Define local path based on type and name
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type_folder = f"{self.model_type}_models"
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safe_name = re.sub(r'[^\w\-]+', '_', self.name) # Sanitize name for path
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self.local_path = os.path.join(type_folder, safe_name)
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def get_full_path(self):
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return os.path.abspath(self.local_path)
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# (Keep DiffusionConfig if still using diffusers library separately)
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@dataclass
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class DiffusionConfig: # Kept for clarity in diffusion tab if needed
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name: str
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base_model: str
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size: str
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domain: Optional[str] = None
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@property
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def model_path(self):
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# Ensure diffusion models are saved in their own distinct top-level folder
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return f"diffusion_models/{re.sub(r'[^w-]+', '_', self.name)}"
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# --- Helper Functions (Combined and refined) -------------
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def generate_filename(sequence, ext="png"):
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timestamp = time.strftime('%Y%m%d_%H%M%S')
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safe_sequence = re.sub(r'[^\w\-]+', '_', str(sequence))
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return f"{safe_sequence}_{timestamp}.{ext}"
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def pdf_url_to_filename(url):
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name = re.sub(r'^https?://', '', url)
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name = re.sub(r'[<>:"/\\|?*]', '_', name)
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return name[:100] + ".pdf" # Limit length
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def get_download_link(file_path, mime_type="application/octet-stream", label="Download"):
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if not os.path.exists(file_path): return f"{label} (File not found)"
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try:
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with open(file_path, "rb") as f: file_bytes = f.read()
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b64 = base64.b64encode(file_bytes).decode()
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return f'<a href="data:{mime_type};base64,{b64}" download="{os.path.basename(file_path)}">{label}</a>'
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except Exception as e:
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logger.error(f"Error creating download link for {file_path}: {e}")
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return f"{label} (Error)"
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def zip_directory(directory_path, zip_path):
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with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
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for root, _, files in os.walk(directory_path):
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for file in files:
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file_path = os.path.join(root, file)
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zipf.write(file_path, os.path.relpath(file_path, os.path.dirname(directory_path)))
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def get_local_model_paths(model_type="causal"):
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"""Gets paths of locally saved models of a specific type."""
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pattern = f"{model_type}_models/*"
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dirs = [d for d in glob.glob(pattern) if os.path.isdir(d)]
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return dirs
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def get_gallery_files(file_types=("png", "pdf", "jpg", "jpeg", "md", "txt")):
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"""Gets all files with specified extensions in the current directory."""
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all_files = set()
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for ext in file_types:
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# Ensure the glob pattern correctly targets files in the script's directory
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all_files.update(glob.glob(f"./*.{ext.lower()}")) # Use ./* for current dir
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all_files.update(glob.glob(f"./*.{ext.upper()}"))
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# Convert to list and remove potential './' prefix for cleaner display
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return sorted([os.path.normpath(f) for f in all_files])
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def get_pdf_files():
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# Use get_gallery_files to find PDFs
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return get_gallery_files(['pdf'])
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def download_pdf(url, output_path):
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try:
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headers = {'User-Agent': 'Mozilla/5.0'}
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response = requests.get(url, stream=True, timeout=20, headers=headers)
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response.raise_for_status()
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with open(output_path, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192): f.write(chunk)
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logger.info(f"Successfully downloaded {url} to {output_path}")
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return True
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except requests.exceptions.RequestException as e:
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logger.error(f"Failed to download {url}: {e}")
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if os.path.exists(output_path):
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try:
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os.remove(output_path)
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logger.info(f"Removed partially downloaded file: {output_path}")
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except OSError as remove_error:
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logger.error(f"Error removing partial file {output_path}: {remove_error}")
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except Exception as general_remove_error:
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logger.error(f"General error removing partial file {output_path}: {general_remove_error}")
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return False
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except Exception as e:
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logger.error(f"An unexpected error occurred during download of {url}: {e}")
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if os.path.exists(output_path):
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try:
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os.remove(output_path)
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logger.info(f"Removed file after unexpected error: {output_path}")
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except OSError as remove_error:
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logger.error(f"Error removing file after unexpected error {output_path}: {remove_error}")
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except Exception as general_remove_error:
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logger.error(f"General error removing file after unexpected error {output_path}: {general_remove_error}")
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return False
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async def process_pdf_snapshot(pdf_path, mode="single", resolution_factor=2.0):
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start_time = time.time()
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# Use a placeholder within the main app area for status during async operations
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status_placeholder = st.empty()
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status_placeholder.text(f"Processing PDF Snapshot ({mode}, Res: {resolution_factor}x)... (0s)")
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output_files = []
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try:
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doc = fitz.open(pdf_path)
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matrix = fitz.Matrix(resolution_factor, resolution_factor)
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num_pages_to_process = 0
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if mode == "single": num_pages_to_process = min(1, len(doc))
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elif mode == "twopage": num_pages_to_process = min(2, len(doc))
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elif mode == "allpages": num_pages_to_process = len(doc)
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for i in range(num_pages_to_process):
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page_start_time = time.time()
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page = doc.load_page(i) # Use load_page for efficiency
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pix = page.get_pixmap(matrix=matrix)
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base_name = os.path.splitext(os.path.basename(pdf_path))[0]
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output_file = generate_filename(f"{base_name}_pg{i+1}_{mode}", "png")
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# Ensure output path is valid before saving
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output_dir = os.path.dirname(output_file) or "."
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if not os.path.exists(output_dir): os.makedirs(output_dir)
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await asyncio.to_thread(pix.save, output_file)
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output_files.append(output_file)
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elapsed_page = int(time.time() - page_start_time)
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status_placeholder.text(f"Processing PDF Snapshot ({mode}, Res: {resolution_factor}x)... Page {i+1}/{num_pages_to_process} done ({elapsed_page}s)")
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await asyncio.sleep(0.01)
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doc.close()
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elapsed = int(time.time() - start_time)
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status_placeholder.success(f"PDF Snapshot ({mode}, {len(output_files)} files) completed in {elapsed}s!")
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return output_files
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except Exception as e:
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logger.error(f"Failed to process PDF snapshot for {pdf_path}: {e}", exc_info=True) # Add traceback
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status_placeholder.error(f"Failed to process PDF {os.path.basename(pdf_path)}: {e}")
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# Clean up any files created before the error
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for f in output_files:
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if os.path.exists(f):
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try: os.remove(f)
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except: pass
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return []
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# --- HF Inference Client Management ---
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def get_hf_client() -> Optional[InferenceClient]:
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"""Gets or initializes the Hugging Face Inference Client based on session state."""
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provider = st.session_state.hf_provider
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custom_key = st.session_state.hf_custom_key.strip()
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token_to_use = custom_key if custom_key else HF_TOKEN
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if not token_to_use and provider != "hf-inference":
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# Don't show error here, let caller handle it if client is needed
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# st.error(f"Provider '{provider}' requires a Hugging Face API token...")
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return None
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if provider == "hf-inference" and not token_to_use:
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logger.warning("Using hf-inference provider without a token. Rate limits may apply.")
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token_to_use = None # Explicitly set to None for public inference API
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# Check if client needs re-initialization
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current_client = st.session_state.get('hf_inference_client')
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needs_reinit = True
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if current_client:
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# Compare provider and token status more carefully
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current_token = getattr(current_client, '_token', None) # Access internal token if exists
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current_provider = getattr(current_client, 'provider', None) # Access provider if exists
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token_matches = (token_to_use == current_token)
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provider_matches = (provider == current_provider)
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if token_matches and provider_matches:
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needs_reinit = False
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if needs_reinit:
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try:
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logger.info(f"Initializing InferenceClient for provider: {provider}. Token source: {'Custom Key' if custom_key else ('HF_TOKEN' if HF_TOKEN else 'None')}")
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st.session_state.hf_inference_client = InferenceClient(model=None, token=token_to_use, provider=provider) # Init without model initially
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# Store provider on client instance if possible (check InferenceClient structure or assume it's handled internally)
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setattr(st.session_state.hf_inference_client, 'provider', provider) # Explicitly store provider for re-init check
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setattr(st.session_state.hf_inference_client, '_token', token_to_use) # Explicitly store token for re-init check
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logger.info("InferenceClient initialized successfully.")
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except Exception as e:
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st.error(f"Failed to initialize Hugging Face client for provider {provider}: {e}")
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logger.error(f"InferenceClient initialization failed: {e}")
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st.session_state.hf_inference_client = None
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return st.session_state.hf_inference_client
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# --- HF/Local Model Processing Functions ---
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def process_text_hf(text: str, prompt: str, use_api: bool) -> str:
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"""Processes text using either HF Inference API or a loaded local model."""
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status_placeholder = st.empty()
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start_time = time.time()
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result_text = ""
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params = {
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"max_new_tokens": st.session_state.gen_max_tokens,
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"temperature": st.session_state.gen_temperature,
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"top_p": st.session_state.gen_top_p,
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"repetition_penalty": st.session_state.gen_frequency_penalty, # Keep user value, adjust name below if needed
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}
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seed = st.session_state.gen_seed
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if seed != -1: params["seed"] = seed
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system_prompt = "You are a helpful assistant. Process the following text based on the user's request."
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full_prompt = f"{prompt}\n\n---\n\n{text}"
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messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": full_prompt}]
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if use_api:
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status_placeholder.info("Processing text using Hugging Face API...")
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client = get_hf_client()
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if not client: return "Error: Hugging Face client not configured/available."
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model_id = st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model
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if not model_id: return "Error: No Hugging Face API model specified."
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status_placeholder.info(f"Using API Model: {model_id}")
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try:
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# Ensure repetition_penalty is passed correctly if supported
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api_params = {
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"max_tokens": params['max_new_tokens'],
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"temperature": params['temperature'],
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"top_p": params['top_p'],
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"repetition_penalty": params.get('repetition_penalty') # Check if API uses this name
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}
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if 'seed' in params: api_params['seed'] = params['seed']
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response = client.chat_completion(model=model_id, messages=messages, **api_params)
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result_text = response.choices[0].message.content or ""
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logger.info(f"HF API text processing successful for model {model_id}.")
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except Exception as e:
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logger.error(f"HF API text processing failed for model {model_id}: {e}", exc_info=True)
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result_text = f"Error during Hugging Face API inference: {str(e)}"
|
401 |
-
else:
|
402 |
-
status_placeholder.info("Processing text using local model...")
|
403 |
-
if not _transformers_available: return "Error: Transformers library not available."
|
404 |
-
model_path = st.session_state.get('selected_local_model_path')
|
405 |
-
if not model_path or model_path not in st.session_state.get('local_models', {}): return "Error: No suitable local model selected/loaded."
|
406 |
-
local_model_data = st.session_state['local_models'][model_path]
|
407 |
-
if local_model_data.get('type') != 'causal': return f"Error: Loaded model '{os.path.basename(model_path)}' is not a Causal LM."
|
408 |
-
status_placeholder.info(f"Using Local Model: {os.path.basename(model_path)}")
|
409 |
-
model = local_model_data.get('model')
|
410 |
-
tokenizer = local_model_data.get('tokenizer')
|
411 |
-
if not model or not tokenizer: return f"Error: Model/tokenizer not found for {os.path.basename(model_path)}."
|
412 |
-
try:
|
413 |
-
try: prompt_for_model = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
414 |
-
except: logger.warning(f"Chat template failed for {model_path}. Using basic format."); prompt_for_model = f"System: {system_prompt}\nUser: {full_prompt}\nAssistant:"
|
415 |
-
inputs = tokenizer(prompt_for_model, return_tensors="pt", padding=True, truncation=True, max_length=2048).to(model.device) # Increased context slightly
|
416 |
-
generate_params = {
|
417 |
-
"max_new_tokens": params['max_new_tokens'],
|
418 |
-
"temperature": params['temperature'],
|
419 |
-
"top_p": params['top_p'],
|
420 |
-
"repetition_penalty": params.get('repetition_penalty', 1.0),
|
421 |
-
"do_sample": True if params['temperature'] > 0.01 else False, # Sample if temp > 0.01
|
422 |
-
"pad_token_id": tokenizer.eos_token_id
|
423 |
-
}
|
424 |
-
with torch.no_grad(): outputs = model.generate(**inputs, **generate_params)
|
425 |
-
input_length = inputs['input_ids'].shape[1]; generated_ids = outputs[0][input_length:]
|
426 |
-
result_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
|
427 |
-
logger.info(f"Local text processing successful for model {model_path}.")
|
428 |
-
except Exception as e:
|
429 |
-
logger.error(f"Local text processing failed for model {model_path}: {e}", exc_info=True)
|
430 |
-
result_text = f"Error during local model inference: {str(e)}"
|
431 |
-
|
432 |
-
elapsed = int(time.time() - start_time)
|
433 |
-
status_placeholder.success(f"Text processing completed in {elapsed}s.")
|
434 |
-
return result_text
|
435 |
-
|
436 |
-
def process_image_hf(image: Image.Image, prompt: str, use_api: bool) -> str:
|
437 |
-
"""Processes an image using either HF Inference API or a local model."""
|
438 |
-
status_placeholder = st.empty()
|
439 |
-
start_time = time.time()
|
440 |
-
result_text = "[Image processing requires specific Vision model implementation]"
|
441 |
-
|
442 |
-
if use_api:
|
443 |
-
status_placeholder.info("Processing image using Hugging Face API (Image-to-Text)...")
|
444 |
-
client = get_hf_client()
|
445 |
-
if not client: return "Error: HF client not configured."
|
446 |
-
buffered = BytesIO(); image.save(buffered, format="PNG"); img_bytes = buffered.getvalue()
|
447 |
-
try:
|
448 |
-
captioning_model_id = "Salesforce/blip-image-captioning-large" # Default captioner
|
449 |
-
vqa_model_id = "llava-hf/llava-1.5-7b-hf" # Default VQA - MAY REQUIRE DIFFERENT CLIENT CALL
|
450 |
-
# Decide whether to use captioning or VQA based on prompt? Simple approach: captioning.
|
451 |
-
status_placeholder.info(f"Using API Image-to-Text Model: {captioning_model_id}")
|
452 |
-
response_list = client.image_to_text(data=img_bytes, model=captioning_model_id)
|
453 |
-
if response_list and 'generated_text' in response_list[0]:
|
454 |
-
result_text = f"API Caption: {response_list[0]['generated_text']}\n(Prompt '{prompt}' likely ignored by this API endpoint)"
|
455 |
-
logger.info(f"HF API image captioning successful for model {captioning_model_id}.")
|
456 |
-
else: result_text = "Error: Unexpected response format from image-to-text API."; logger.warning(f"Unexpected API response: {response_list}")
|
457 |
-
except Exception as e: logger.error(f"HF API image processing failed: {e}"); result_text = f"Error during HF API image inference: {str(e)}"
|
458 |
-
else:
|
459 |
-
status_placeholder.info("Processing image using local model...")
|
460 |
-
if not _transformers_available: return "Error: Transformers library needed."
|
461 |
-
model_path = st.session_state.get('selected_local_model_path')
|
462 |
-
if not model_path or model_path not in st.session_state.get('local_models', {}): return "Error: No suitable local model selected/loaded."
|
463 |
-
local_model_data = st.session_state['local_models'][model_path]
|
464 |
-
model_type = local_model_data.get('type')
|
465 |
-
if model_type not in ['vision', 'ocr']: return f"Error: Loaded model '{os.path.basename(model_path)}' is not a Vision/OCR type."
|
466 |
-
status_placeholder.warning(f"Local {model_type} Model ({os.path.basename(model_path)}): Processing logic depends on specific model. Placeholder active.")
|
467 |
-
# --- ADD SPECIFIC LOCAL VISION/OCR MODEL LOGIC HERE ---
|
468 |
-
# This section needs code tailored to the loaded model's processor/generate methods
|
469 |
-
# Example placeholder:
|
470 |
-
processor = local_model_data.get('processor')
|
471 |
-
model = local_model_data.get('model')
|
472 |
-
if processor and model:
|
473 |
-
result_text = f"[Local {model_type} model processing needs implementation for {os.path.basename(model_path)}. Prompt: '{prompt}']"
|
474 |
-
else:
|
475 |
-
result_text = f"Error: Missing model or processor for local {model_type} model {os.path.basename(model_path)}."
|
476 |
-
# --- END OF PLACEHOLDER ---
|
477 |
-
|
478 |
-
elapsed = int(time.time() - start_time)
|
479 |
-
status_placeholder.success(f"Image processing attempt completed in {elapsed}s.")
|
480 |
-
return result_text
|
481 |
-
|
482 |
-
async def process_hf_ocr(image: Image.Image, output_file: str, use_api: bool) -> str:
|
483 |
-
""" Performs OCR using the process_image_hf function framework. """
|
484 |
-
# Simple prompt for OCR task
|
485 |
-
ocr_prompt = "Extract text content from this image."
|
486 |
-
result = process_image_hf(image, ocr_prompt, use_api=use_api) # Pass use_api flag
|
487 |
-
|
488 |
-
# Save the result if it looks like text (basic check)
|
489 |
-
if result and not result.startswith("Error") and not result.startswith("["):
|
490 |
-
try:
|
491 |
-
async with aiofiles.open(output_file, "w", encoding='utf-8') as f:
|
492 |
-
await f.write(result)
|
493 |
-
logger.info(f"HF OCR result saved to {output_file}")
|
494 |
-
except IOError as e:
|
495 |
-
logger.error(f"Failed to save HF OCR output to {output_file}: {e}")
|
496 |
-
result += f"\n[Error saving file: {e}]" # Append error to result if save fails
|
497 |
-
|
498 |
-
# --- CORRECTED BLOCK ---
|
499 |
-
elif os.path.exists(output_file):
|
500 |
-
# Remove file if processing failed or was just a placeholder message
|
501 |
-
try:
|
502 |
-
os.remove(output_file)
|
503 |
-
except OSError:
|
504 |
-
# Log error or just ignore if removal fails
|
505 |
-
logger.warning(f"Could not remove potentially empty/failed OCR file: {output_file}")
|
506 |
-
pass # Ignore removal error
|
507 |
-
except Exception as e_rem: # Catch any other error during removal
|
508 |
-
logger.warning(f"Error removing OCR file {output_file}: {e_rem}")
|
509 |
-
pass
|
510 |
-
# --- END CORRECTION ---
|
511 |
-
|
512 |
-
return result
|
513 |
-
|
514 |
-
# --- Character Functions (Keep from previous) -----------
|
515 |
-
def randomize_character_content():
|
516 |
-
intro_templates = ["{char} is a valiant knight...", "{char} is a mischievous thief...", "{char} is a wise scholar...", "{char} is a fiery warrior...", "{char} is a gentle healer..."]
|
517 |
-
greeting_templates = ["'I am from the knight's guild...'", "'I heard you needed help—name’s {char}...", "'Oh, hello! I’m {char}, didn’t see you there...'", "'I’m {char}, and I’m here to fight...'", "'I’m {char}, here to heal...'"]
|
518 |
-
name = f"Character_{random.randint(1000, 9999)}"; gender = random.choice(["Male", "Female"]); intro = random.choice(intro_templates).format(char=name); greeting = random.choice(greeting_templates).format(char=name)
|
519 |
-
return name, gender, intro, greeting
|
520 |
-
|
521 |
-
def save_character(character_data):
|
522 |
-
characters = st.session_state.get('characters', []);
|
523 |
-
if any(c['name'] == character_data['name'] for c in characters): st.error(f"Character name '{character_data['name']}' already exists."); return False
|
524 |
-
characters.append(character_data); st.session_state['characters'] = characters
|
525 |
-
try:
|
526 |
-
with open("characters.json", "w", encoding='utf-8') as f: json.dump(characters, f, indent=2); logger.info(f"Saved character: {character_data['name']}"); return True
|
527 |
-
except IOError as e: logger.error(f"Failed to save characters.json: {e}"); st.error(f"Failed to save character file: {e}"); return False
|
528 |
-
|
529 |
-
def load_characters():
|
530 |
-
if not os.path.exists("characters.json"): st.session_state['characters'] = []; return
|
531 |
-
try:
|
532 |
-
with open("characters.json", "r", encoding='utf-8') as f: characters = json.load(f)
|
533 |
-
if isinstance(characters, list): st.session_state['characters'] = characters; logger.info(f"Loaded {len(characters)} characters.")
|
534 |
-
else: st.session_state['characters'] = []; logger.warning("characters.json is not a list, resetting."); os.remove("characters.json")
|
535 |
-
except (json.JSONDecodeError, IOError) as e:
|
536 |
-
logger.error(f"Failed to load or decode characters.json: {e}"); st.error(f"Error loading character file: {e}. Starting fresh."); st.session_state['characters'] = []
|
537 |
-
try: corrupt_filename = f"characters_corrupt_{int(time.time())}.json"; shutil.copy("characters.json", corrupt_filename); logger.info(f"Backed up corrupted character file to {corrupt_filename}"); os.remove("characters.json")
|
538 |
-
except Exception as backup_e: logger.error(f"Could not backup corrupted character file: {backup_e}")
|
539 |
-
|
540 |
-
# --- Utility: Clean stems (Keep from previous) ----------
|
541 |
-
def clean_stem(fn: str) -> str:
|
542 |
-
name = os.path.splitext(os.path.basename(fn))[0]; name = name.replace('-', ' ').replace('_', ' ')
|
543 |
-
return name.strip().title()
|
544 |
-
|
545 |
-
# --- PDF Creation Functions ---
|
546 |
-
# Original image-only PDF function (might be removed or kept as an option)
|
547 |
-
def make_image_sized_pdf(sources):
|
548 |
-
# ... (kept same as previous version for now) ...
|
549 |
-
if not sources: st.warning("No image sources provided for PDF generation."); return None
|
550 |
-
buf = io.BytesIO(); c = canvas.Canvas(buf, pagesize=letter)
|
551 |
-
try:
|
552 |
-
for idx, src in enumerate(sources, start=1):
|
553 |
-
status_placeholder = st.empty(); status_placeholder.info(f"Adding page {idx}/{len(sources)}: {os.path.basename(str(src))}...")
|
554 |
-
try:
|
555 |
-
filename = f'page_{idx}'
|
556 |
-
if isinstance(src, str):
|
557 |
-
if not os.path.exists(src): logger.warning(f"Image file not found: {src}. Skipping."); status_placeholder.warning(f"Skipping missing file: {os.path.basename(src)}"); continue
|
558 |
-
img_obj = Image.open(src); filename = os.path.basename(src)
|
559 |
-
elif hasattr(src, 'name'): # Handle uploaded file object
|
560 |
-
src.seek(0); img_obj = Image.open(src); filename = getattr(src, 'name', f'uploaded_image_{idx}'); src.seek(0)
|
561 |
-
else: continue # Skip unknown source type
|
562 |
-
with img_obj:
|
563 |
-
iw, ih = img_obj.size
|
564 |
-
if iw <= 0 or ih <= 0: logger.warning(f"Invalid image dimensions ({iw}x{ih}) for {filename}. Skipping."); status_placeholder.warning(f"Skipping invalid image: {filename}"); continue
|
565 |
-
cap_h = 30; pw, ph = iw, ih + cap_h; c.setPageSize((pw, ph)); img_reader = ImageReader(img_obj)
|
566 |
-
c.drawImage(img_reader, 0, cap_h, width=iw, height=ih, preserveAspectRatio=True, anchor='c', mask='auto')
|
567 |
-
caption = clean_stem(filename); c.setFont('Helvetica', 12); c.setFillColorRGB(0, 0, 0); c.drawCentredString(pw / 2, cap_h / 2 + 3, caption)
|
568 |
-
c.setFont('Helvetica', 8); c.setFillColorRGB(0.5, 0.5, 0.5); c.drawRightString(pw - 10, 8, f"Page {idx}")
|
569 |
-
c.showPage(); status_placeholder.success(f"Added page {idx}/{len(sources)}: {filename}")
|
570 |
-
except (IOError, OSError, UnidentifiedImageError) as img_err: logger.error(f"Error processing image {src}: {img_err}"); status_placeholder.error(f"Error adding page {idx}: {img_err}")
|
571 |
-
except Exception as e: logger.error(f"Unexpected error adding page {idx} ({src}): {e}"); status_placeholder.error(f"Unexpected error on page {idx}: {e}")
|
572 |
-
c.save(); buf.seek(0)
|
573 |
-
if buf.getbuffer().nbytes < 100: st.error("PDF generation resulted in an empty file."); return None
|
574 |
-
return buf.getvalue()
|
575 |
-
except Exception as e: logger.error(f"Fatal error during PDF generation: {e}"); st.error(f"PDF Generation Failed: {e}"); return None
|
576 |
-
|
577 |
-
# --- NEW Combined PDF Generation Function ---
|
578 |
-
def make_combined_pdf(ordered_sources_info: List[Dict]) -> Optional[bytes]:
|
579 |
-
if not ordered_sources_info:
|
580 |
-
st.warning("No items selected for combined PDF generation.")
|
581 |
-
return None
|
582 |
-
|
583 |
-
buf = io.BytesIO()
|
584 |
-
c = canvas.Canvas(buf, pagesize=letter)
|
585 |
-
styles = getSampleStyleSheet()
|
586 |
-
total_pages_generated = 0
|
587 |
-
|
588 |
-
# Add page number function
|
589 |
-
def draw_page_number(canvas, page_num, page_width, page_height):
|
590 |
-
canvas.saveState()
|
591 |
-
canvas.setFont('Helvetica', 8)
|
592 |
-
canvas.setFillColorRGB(0.5, 0.5, 0.5)
|
593 |
-
canvas.drawRightString(page_width - inch/2, inch/2, f"Page {page_num}")
|
594 |
-
canvas.restoreState()
|
595 |
-
|
596 |
-
for idx, item_info in enumerate(ordered_sources_info):
|
597 |
-
filepath = item_info.get('filepath')
|
598 |
-
file_type = item_info.get('type')
|
599 |
-
filename = item_info.get('filename', f"item_{idx+1}")
|
600 |
-
item_caption = clean_stem(filename)
|
601 |
-
|
602 |
-
if not filepath: logger.warning(f"Skipping item {idx+1} due to missing filepath."); continue
|
603 |
-
is_file_object = not isinstance(filepath, str)
|
604 |
-
status_placeholder = st.empty()
|
605 |
-
status_placeholder.info(f"Processing item {idx+1}/{len(ordered_sources_info)}: {filename} ({file_type})...")
|
606 |
-
|
607 |
-
try:
|
608 |
-
# --- IMAGE Processing ---
|
609 |
-
if file_type == 'Image':
|
610 |
-
if is_file_object: filepath.seek(0)
|
611 |
-
try:
|
612 |
-
img_obj = Image.open(filepath)
|
613 |
-
with img_obj:
|
614 |
-
iw, ih = img_obj.size
|
615 |
-
if iw <= 0 or ih <= 0: raise ValueError("Invalid image dimensions")
|
616 |
-
cap_h = 30; pw, ph = iw, ih + cap_h
|
617 |
-
c.setPageSize((pw, ph)); img_reader = ImageReader(img_obj)
|
618 |
-
c.drawImage(img_reader, 0, cap_h, width=iw, height=ih, preserveAspectRatio=True, anchor='c', mask='auto')
|
619 |
-
c.setFont('Helvetica', 12); c.setFillColorRGB(0, 0, 0); c.drawCentredString(pw / 2, cap_h / 2 + 3, item_caption)
|
620 |
-
total_pages_generated += 1; draw_page_number(c, total_pages_generated, pw, ph)
|
621 |
-
c.showPage()
|
622 |
-
finally:
|
623 |
-
if is_file_object: filepath.seek(0)
|
624 |
-
|
625 |
-
# --- PDF Processing ---
|
626 |
-
elif file_type == 'PDF':
|
627 |
-
src_doc = None
|
628 |
-
try:
|
629 |
-
if is_file_object: filepath.seek(0); pdf_bytes = filepath.read(); src_doc = fitz.open("pdf", pdf_bytes)
|
630 |
-
else: src_doc = fitz.open(filepath)
|
631 |
-
if len(src_doc) == 0: st.warning(f"Skipping empty PDF: {filename}"); continue
|
632 |
-
for i, page in enumerate(src_doc):
|
633 |
-
page_rect = page.rect; pw, ph = page_rect.width, page_rect.height
|
634 |
-
if pw <= 0 or ph <= 0: continue
|
635 |
-
c.setPageSize((pw, ph))
|
636 |
-
pix = page.get_pixmap(dpi=150) # Render as image
|
637 |
-
if pix.width > 0 and pix.height > 0:
|
638 |
-
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples); img_reader = ImageReader(img)
|
639 |
-
c.drawImage(img_reader, 0, 0, width=pw, height=ph)
|
640 |
-
else: c.setFont('Helvetica', 10); c.setFillColorRGB(1,0,0); c.drawCentredString(pw/2, ph/2, f"Failed to render page {i+1} preview")
|
641 |
-
overlay_text = f"{item_caption} (p{i+1})"; c.setFont('Helvetica', 8); c.setFillColorRGB(0, 0, 0, alpha=0.6); c.drawString(10, 10, overlay_text)
|
642 |
-
total_pages_generated += 1; draw_page_number(c, total_pages_generated, pw, ph)
|
643 |
-
c.showPage()
|
644 |
-
finally:
|
645 |
-
if src_doc: src_doc.close()
|
646 |
-
if is_file_object: filepath.seek(0)
|
647 |
-
|
648 |
-
# --- TEXT/MARKDOWN Processing ---
|
649 |
-
elif file_type == 'Text':
|
650 |
-
if is_file_object:
|
651 |
-
filepath.seek(0)
|
652 |
-
try: text_content = filepath.read().decode('utf-8')
|
653 |
-
except: text_content = filepath.read().decode('latin-1', errors='replace')
|
654 |
-
else:
|
655 |
-
with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: text_content = f.read()
|
656 |
-
|
657 |
-
temp_buf = io.BytesIO()
|
658 |
-
temp_doc = SimpleDocTemplate(temp_buf, pagesize=letter, leftMargin=inch, rightMargin=inch, topMargin=inch, bottomMargin=inch)
|
659 |
-
story = [Paragraph(f"Content from: {item_caption}", styles['h2']), Spacer(1, 0.2*inch)]
|
660 |
-
# Use Preformatted for simple text dump
|
661 |
-
story.append(Preformatted(text_content, styles['Code']))
|
662 |
-
temp_doc.build(story)
|
663 |
-
temp_buf.seek(0)
|
664 |
-
|
665 |
-
text_pdf = fitz.open("pdf", temp_buf.read())
|
666 |
-
for i, page in enumerate(text_pdf):
|
667 |
-
page_rect = page.rect; pw, ph = page_rect.width, page_rect.height
|
668 |
-
c.setPageSize((pw, ph)); pix = page.get_pixmap(dpi=150)
|
669 |
-
if pix.width > 0 and pix.height > 0:
|
670 |
-
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples); img_reader = ImageReader(img)
|
671 |
-
c.drawImage(img_reader, 0, 0, width=pw, height=ph)
|
672 |
-
else: c.setFont('Helvetica', 10); c.setFillColorRGB(1,0,0); c.drawCentredString(pw/2, ph/2, f"Failed to render text page {i+1}")
|
673 |
-
total_pages_generated += 1; draw_page_number(c, total_pages_generated, pw, ph)
|
674 |
-
c.showPage()
|
675 |
-
text_pdf.close()
|
676 |
-
|
677 |
-
else: # Unknown type
|
678 |
-
logger.warning(f"Unsupported file type for PDF combination: {filename} ({file_type})")
|
679 |
-
c.setPageSize(letter); c.setFont('Helvetica-Bold', 14); c.setFillColorRGB(0.7, 0.7, 0); c.drawCentredString(letter[0] / 2, letter[1] / 2 + 20, f"Unsupported File: {filename}")
|
680 |
-
c.setFont('Helvetica', 10); c.drawCentredString(letter[0] / 2, letter[1] / 2 - 20, f"Type: {file_type}. Cannot include.")
|
681 |
-
total_pages_generated += 1; draw_page_number(c, total_pages_generated, letter[0], letter[1])
|
682 |
-
c.showPage()
|
683 |
-
|
684 |
-
except Exception as item_err:
|
685 |
-
logger.error(f"Error processing item {filename} for PDF: {item_err}", exc_info=True)
|
686 |
-
try: # Add error page
|
687 |
-
c.setPageSize(letter); c.setFont('Helvetica-Bold', 14); c.setFillColorRGB(1, 0, 0); c.drawCentredString(letter[0] / 2, letter[1] / 2 + 20, f"Error processing: {filename}")
|
688 |
-
c.setFont('Helvetica', 10); c.drawCentredString(letter[0] / 2, letter[1] / 2 - 20, f"{str(item_err)[:100]}"); total_pages_generated += 1; draw_page_number(c, total_pages_generated, letter[0], letter[1]); c.showPage()
|
689 |
-
except: logger.error(f"Failed to add error page for {filename}")
|
690 |
-
finally:
|
691 |
-
status_placeholder.empty()
|
692 |
-
|
693 |
-
if total_pages_generated == 0: st.error("No pages were successfully added."); return None
|
694 |
-
try:
|
695 |
-
c.save(); buf.seek(0)
|
696 |
-
if buf.getbuffer().nbytes < 100: st.error("Combined PDF generation resulted empty."); return None
|
697 |
-
return buf.getvalue()
|
698 |
-
except Exception as e: logger.error(f"Fatal error during final PDF save: {e}"); st.error(f"PDF Save Failed: {e}"); return None
|
699 |
-
|
700 |
-
|
701 |
-
# --- Sidebar Gallery Update Function (MODIFIED for Sort, PDF Preview Fix, Delete Fix) ---
|
702 |
-
def get_sort_key(filename):
|
703 |
-
ext = os.path.splitext(filename)[1].lower()
|
704 |
-
if ext in ['.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff']: priority = 1
|
705 |
-
elif ext in ['.md', '.txt']: priority = 2
|
706 |
-
elif ext == '.pdf': priority = 3
|
707 |
-
else: priority = 4
|
708 |
-
return (priority, filename.lower())
|
709 |
-
|
710 |
-
def update_gallery():
|
711 |
-
st.sidebar.markdown("### Asset Gallery 📸📖")
|
712 |
-
all_files_unsorted = get_gallery_files()
|
713 |
-
all_files = sorted(all_files_unsorted, key=get_sort_key) # Apply sorting
|
714 |
-
|
715 |
-
if not all_files: st.sidebar.info("No assets found."); return
|
716 |
-
st.sidebar.caption(f"Found {len(all_files)} assets:")
|
717 |
-
|
718 |
-
for idx, file in enumerate(all_files):
|
719 |
-
st.session_state['unique_counter'] += 1
|
720 |
-
unique_id = st.session_state['unique_counter']
|
721 |
-
item_key_base = f"gallery_item_{os.path.basename(file)}_{unique_id}"
|
722 |
-
basename = os.path.basename(file)
|
723 |
-
st.sidebar.markdown(f"**{basename}**")
|
724 |
-
|
725 |
-
try:
|
726 |
-
file_ext = os.path.splitext(file)[1].lower()
|
727 |
-
preview_failed = False
|
728 |
-
# Previews with better error handling
|
729 |
-
if file_ext in ['.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff']:
|
730 |
-
try:
|
731 |
-
with st.sidebar.expander("Preview", expanded=False): st.image(Image.open(file), use_container_width=True)
|
732 |
-
except Exception as img_err: st.sidebar.warning(f"Img preview failed: {img_err}"); preview_failed = True
|
733 |
-
elif file_ext == '.pdf':
|
734 |
-
try:
|
735 |
-
with st.sidebar.expander("Preview (Page 1)", expanded=False):
|
736 |
-
doc = fitz.open(file)
|
737 |
-
if len(doc) > 0:
|
738 |
-
pix = doc[0].get_pixmap(matrix=fitz.Matrix(0.5, 0.5))
|
739 |
-
if pix.width > 0 and pix.height > 0: img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples); st.image(img, use_container_width=True)
|
740 |
-
else: st.warning("Failed to render PDF page."); preview_failed = True
|
741 |
-
else: st.warning("Empty PDF")
|
742 |
-
doc.close()
|
743 |
-
except Exception as pdf_err: st.sidebar.warning(f"PDF preview failed: {pdf_err}"); logger.warning(f"PDF preview error {file}: {pdf_err}"); preview_failed = True
|
744 |
-
elif file_ext in ['.md', '.txt']:
|
745 |
-
try:
|
746 |
-
with st.sidebar.expander("Preview (Start)", expanded=False):
|
747 |
-
with open(file, 'r', encoding='utf-8', errors='ignore') as f: content_preview = f.read(200)
|
748 |
-
st.code(content_preview + "...", language='markdown' if file_ext == '.md' else 'text')
|
749 |
-
except Exception as txt_err: st.sidebar.warning(f"Text preview failed: {txt_err}"); preview_failed = True
|
750 |
-
|
751 |
-
# Actions
|
752 |
-
action_cols = st.sidebar.columns(3)
|
753 |
-
with action_cols[0]:
|
754 |
-
checkbox_key = f"cb_{item_key_base}"
|
755 |
-
st.session_state.setdefault('asset_checkboxes', {})
|
756 |
-
st.session_state['asset_checkboxes'][file] = st.checkbox("Select", value=st.session_state['asset_checkboxes'].get(file, False), key=checkbox_key)
|
757 |
-
with action_cols[1]:
|
758 |
-
mime_map = {'.png': 'image/png', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', '.pdf': 'application/pdf', '.txt': 'text/plain', '.md': 'text/markdown'}
|
759 |
-
mime_type = mime_map.get(file_ext, "application/octet-stream"); dl_key = f"dl_{item_key_base}"
|
760 |
-
try:
|
761 |
-
with open(file, "rb") as fp: st.download_button(label="📥", data=fp, file_name=basename, mime=mime_type, key=dl_key, help="Download")
|
762 |
-
except Exception as dl_e: st.error(f"DL Err: {dl_e}")
|
763 |
-
with action_cols[2]:
|
764 |
-
delete_key = f"del_{item_key_base}"
|
765 |
-
if st.button("🗑️", key=delete_key, help=f"Delete {basename}"):
|
766 |
-
delete_success = False
|
767 |
-
try:
|
768 |
-
os.remove(file)
|
769 |
-
st.session_state['asset_checkboxes'].pop(file, None)
|
770 |
-
if file in st.session_state.get('layout_snapshots', []): st.session_state['layout_snapshots'].remove(file) # Remove if also in old list
|
771 |
-
logger.info(f"Deleted asset: {file}")
|
772 |
-
st.toast(f"Deleted {basename}!", icon="✅")
|
773 |
-
delete_success = True
|
774 |
-
except OSError as e: logger.error(f"Error deleting file {file}: {e}"); st.error(f"Could not delete {basename}: {e}")
|
775 |
-
except Exception as e: logger.error(f"Unexpected error deleting file {file}: {e}"); st.error(f"Could not delete {basename}: {e}")
|
776 |
-
# Rerun to refresh the gallery list after attempting delete
|
777 |
-
st.rerun()
|
778 |
-
|
779 |
-
except FileNotFoundError: st.sidebar.error(f"File vanished: {basename}"); st.session_state['asset_checkboxes'].pop(file, None)
|
780 |
-
except Exception as e: st.sidebar.error(f"Display Error: {basename}"); logger.error(f"Error displaying asset {file}: {e}")
|
781 |
-
st.sidebar.markdown("---")
|
782 |
-
|
783 |
-
# --- UI Elements -----------------------------------------
|
784 |
-
# Sidebar Structure
|
785 |
-
st.sidebar.subheader("🤖 Hugging Face Settings")
|
786 |
-
# ... (HF API, Local Model, Params Expanders - code unchanged) ...
|
787 |
-
with st.sidebar.expander("API Inference Settings", expanded=False):
|
788 |
-
st.session_state.hf_custom_key = st.text_input("Custom HF Token (BYOK)", value=st.session_state.get('hf_custom_key', ""), type="password", key="hf_custom_key_input", help="Enter your Hugging Face API token. Overrides HF_TOKEN env var.")
|
789 |
-
token_status = "Custom Key Set" if st.session_state.hf_custom_key else ("Default HF_TOKEN Set" if HF_TOKEN else "No Token Set"); st.caption(f"Token Status: {token_status}")
|
790 |
-
providers_list = ["hf-inference", "cerebras", "together", "sambanova", "novita", "cohere", "fireworks-ai", "hyperbolic", "nebius"]
|
791 |
-
st.session_state.hf_provider = st.selectbox("Inference Provider", options=providers_list, index=providers_list.index(st.session_state.get('hf_provider', DEFAULT_PROVIDER)), key="hf_provider_select", help="Select the backend provider. Some require specific API keys.")
|
792 |
-
if not st.session_state.hf_custom_key and not HF_TOKEN and st.session_state.hf_provider != "hf-inference": st.warning(f"Provider '{st.session_state.hf_provider}' may require a token.")
|
793 |
-
st.session_state.hf_custom_api_model = st.text_input("Custom API Model ID", value=st.session_state.get('hf_custom_api_model', ""), key="hf_custom_model_input", placeholder="e.g., google/gemma-2-9b-it", help="Overrides the featured model selection below if provided.")
|
794 |
-
effective_api_model = st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model
|
795 |
-
st.session_state.hf_selected_api_model = st.selectbox("Featured API Model", options=FEATURED_MODELS_LIST, index=FEATURED_MODELS_LIST.index(st.session_state.get('hf_selected_api_model', FEATURED_MODELS_LIST[0])), key="hf_featured_model_select", help="Select a common model. Ignored if Custom API Model ID is set.")
|
796 |
-
st.caption(f"Effective API Model: {effective_api_model}")
|
797 |
-
with st.sidebar.expander("Local Model Selection", expanded=True):
|
798 |
-
if not _transformers_available: st.warning("Transformers library not found.")
|
799 |
-
else:
|
800 |
-
local_model_options = ["None"] + list(st.session_state.get('local_models', {}).keys())
|
801 |
-
current_selection = st.session_state.get('selected_local_model_path'); current_selection = current_selection if current_selection in local_model_options else "None"
|
802 |
-
selected_path = st.selectbox("Active Local Model", options=local_model_options, index=local_model_options.index(current_selection), format_func=lambda x: os.path.basename(x) if x != "None" else "None", key="local_model_selector", help="Select a loaded local model.")
|
803 |
-
st.session_state.selected_local_model_path = selected_path if selected_path != "None" else None
|
804 |
-
if st.session_state.selected_local_model_path:
|
805 |
-
model_info = st.session_state.local_models[st.session_state.selected_local_model_path]
|
806 |
-
st.caption(f"Type: {model_info.get('type', '?')} | Device: {model_info.get('model').device if model_info.get('model') else 'N/A'}")
|
807 |
-
else: st.caption("No local model selected.")
|
808 |
-
with st.sidebar.expander("Generation Parameters", expanded=False):
|
809 |
-
st.session_state.gen_max_tokens = st.slider("Max New Tokens", 1, 4096, st.session_state.get('gen_max_tokens', 512), step=1, key="param_max_tokens")
|
810 |
-
st.session_state.gen_temperature = st.slider("Temperature", 0.01, 2.0, st.session_state.get('gen_temperature', 0.7), step=0.01, key="param_temp")
|
811 |
-
st.session_state.gen_top_p = st.slider("Top-P", 0.01, 1.0, st.session_state.get('gen_top_p', 0.95), step=0.01, key="param_top_p")
|
812 |
-
st.session_state.gen_frequency_penalty = st.slider("Repetition Penalty", 1.0, 2.0, st.session_state.get('gen_frequency_penalty', 0.0)+1.0, step=0.05, key="param_repetition", help="1.0 means no penalty.")
|
813 |
-
st.session_state.gen_seed = st.slider("Seed", -1, 65535, st.session_state.get('gen_seed', -1), step=1, key="param_seed", help="-1 for random.")
|
814 |
-
|
815 |
-
st.sidebar.markdown("---")
|
816 |
-
# Gallery is rendered later by calling update_gallery()
|
817 |
-
|
818 |
-
# --- App Title & Main Area ---
|
819 |
-
st.title("Vision & Layout Titans (HF) 🚀🖼️📄")
|
820 |
-
st.markdown("Combined App: PDF Layout Generator + Hugging Face Powered AI Tools")
|
821 |
-
|
822 |
-
# Warning for missing libraries in main area if sidebar not ready
|
823 |
-
if not _transformers_available:
|
824 |
-
st.warning("AI/ML libraries (torch, transformers) not found. Local model features disabled.")
|
825 |
-
elif not _diffusers_available:
|
826 |
-
st.warning("Diffusers library not found. Diffusion model features disabled.")
|
827 |
-
|
828 |
-
|
829 |
-
# --- Main Application Tabs ---
|
830 |
-
tabs_to_create = [
|
831 |
-
"Combined PDF Generator 📄", # Renamed Tab 0
|
832 |
-
"Camera Snap 📷",
|
833 |
-
"Download PDFs 📥",
|
834 |
-
"Build Titan (Local Models) 🌱",
|
835 |
-
"PDF Page Process (HF) 📄", # Clarified name
|
836 |
-
"Image Process (HF) 🖼️",
|
837 |
-
"Text Process (HF) 📝",
|
838 |
-
"Test OCR (HF) 🔍",
|
839 |
-
"Test Image Gen (Diffusers) 🎨",
|
840 |
-
"Character Editor 🧑🎨",
|
841 |
-
"Character Gallery 🖼️",
|
842 |
-
]
|
843 |
-
tabs = st.tabs(tabs_to_create)
|
844 |
-
|
845 |
-
# --- Tab Implementations ---
|
846 |
-
|
847 |
-
# --- Tab 1: Combined PDF Generator (OVERHAULED) ---
|
848 |
-
with tabs[0]:
|
849 |
-
st.header("Combined PDF Generator 📄➕🖼️➕...")
|
850 |
-
st.markdown("Select assets (Images, PDFs, Text/MD) from the sidebar gallery, reorder them, and generate a combined PDF.")
|
851 |
-
|
852 |
-
# --- Get Selected Files ---
|
853 |
-
selected_files_paths = [
|
854 |
-
f for f, selected in st.session_state.get('asset_checkboxes', {}).items()
|
855 |
-
if selected and os.path.exists(f) # Ensure file still exists
|
856 |
-
]
|
857 |
-
|
858 |
-
if not selected_files_paths:
|
859 |
-
st.info("👈 Select one or more assets from the sidebar gallery using the checkboxes.")
|
860 |
-
else:
|
861 |
-
st.info(f"{len(selected_files_paths)} assets selected from gallery.")
|
862 |
-
|
863 |
-
# --- Populate DataFrame for Reordering ---
|
864 |
-
combined_records = []
|
865 |
-
for idx, filepath in enumerate(selected_files_paths):
|
866 |
-
filename = os.path.basename(filepath)
|
867 |
-
ext = os.path.splitext(filename)[1].lower()
|
868 |
-
file_type = "Unknown"
|
869 |
-
if ext in ['.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff']: file_type = "Image"
|
870 |
-
elif ext == '.pdf': file_type = "PDF"
|
871 |
-
elif ext in ['.md', '.txt']: file_type = "Text"
|
872 |
-
|
873 |
-
combined_records.append({
|
874 |
-
"filename": filename,
|
875 |
-
"filepath": filepath, # Keep the path
|
876 |
-
"type": file_type,
|
877 |
-
"order": idx, # Initial order based on selection
|
878 |
-
})
|
879 |
-
|
880 |
-
combined_df_initial = pd.DataFrame(combined_records)
|
881 |
-
|
882 |
-
st.markdown("#### Reorder Selected Assets for PDF")
|
883 |
-
st.caption("Edit the 'Order' column or drag rows to set the sequence for the combined PDF.")
|
884 |
-
|
885 |
-
edited_combined_df = st.data_editor(
|
886 |
-
combined_df_initial,
|
887 |
-
column_config={
|
888 |
-
"filename": st.column_config.TextColumn("Filename", disabled=True),
|
889 |
-
"filepath": None, # Hide filepath column
|
890 |
-
"type": st.column_config.TextColumn("Type", disabled=True),
|
891 |
-
"order": st.column_config.NumberColumn(
|
892 |
-
"Order",
|
893 |
-
min_value=0,
|
894 |
-
# max_value=len(combined_df_initial)-1, # Max can cause issues if rows added/removed by user selection change
|
895 |
-
step=1,
|
896 |
-
required=True,
|
897 |
-
),
|
898 |
-
},
|
899 |
-
hide_index=True,
|
900 |
-
use_container_width=True,
|
901 |
-
num_rows="dynamic", # Allow drag-and-drop reordering
|
902 |
-
key="combined_pdf_editor"
|
903 |
-
)
|
904 |
-
|
905 |
-
# Sort by the edited 'order' column
|
906 |
-
ordered_combined_df = edited_combined_df.sort_values('order').reset_index(drop=True)
|
907 |
-
|
908 |
-
# Prepare list of dicts for the PDF generation function
|
909 |
-
ordered_sources_info_for_pdf = ordered_combined_df[['filepath', 'type', 'filename']].to_dict('records')
|
910 |
-
|
911 |
-
# --- Generate & Download ---
|
912 |
-
st.subheader("Generate Combined PDF")
|
913 |
-
if st.button("🖋️ Generate Combined PDF", key="generate_combined_pdf_btn"):
|
914 |
-
if not ordered_sources_info_for_pdf:
|
915 |
-
st.warning("No items available after reordering.")
|
916 |
-
else:
|
917 |
-
with st.spinner("Generating combined PDF... This might take a while."):
|
918 |
-
combined_pdf_bytes = make_combined_pdf(ordered_sources_info_for_pdf)
|
919 |
-
|
920 |
-
if combined_pdf_bytes:
|
921 |
-
# Create filename
|
922 |
-
now = datetime.now(pytz.timezone("US/Central"))
|
923 |
-
prefix = now.strftime("%Y%m%d-%H%M%p")
|
924 |
-
first_item_name = clean_stem(ordered_sources_info_for_pdf[0].get('filename','combined'))
|
925 |
-
combined_pdf_fname = f"{prefix}_Combined_{first_item_name}.pdf"
|
926 |
-
combined_pdf_fname = re.sub(r'[^\w\-\.\_]', '_', combined_pdf_fname) # Sanitize
|
927 |
-
|
928 |
-
st.success(f"✅ Combined PDF ready: **{combined_pdf_fname}**")
|
929 |
-
st.download_button(
|
930 |
-
"⬇️ Download Combined PDF",
|
931 |
-
data=combined_pdf_bytes,
|
932 |
-
file_name=combined_pdf_fname,
|
933 |
-
mime="application/pdf",
|
934 |
-
key="download_combined_pdf_btn"
|
935 |
-
)
|
936 |
-
# Add preview (optional, might be slow for large combined PDFs)
|
937 |
-
# ... (preview logic similar to other tabs if desired) ...
|
938 |
-
else:
|
939 |
-
st.error("Combined PDF generation failed. Check logs or input files.")
|
940 |
-
|
941 |
-
|
942 |
-
# --- Tab 2: Camera Snap ---
|
943 |
-
with tabs[1]:
|
944 |
-
st.header("Camera Snap 📷")
|
945 |
-
st.subheader("Single Capture (Adds to General Gallery)")
|
946 |
-
cols = st.columns(2)
|
947 |
-
with cols[0]:
|
948 |
-
cam0_img = st.camera_input("Take a picture - Cam 0", key="main_cam0")
|
949 |
-
if cam0_img:
|
950 |
-
filename = generate_filename("cam0_snap");
|
951 |
-
if st.session_state.get('cam0_file') and os.path.exists(st.session_state['cam0_file']):
|
952 |
-
try:
|
953 |
-
os.remove(st.session_state['cam0_file'])
|
954 |
-
except OSError:
|
955 |
-
pass
|
956 |
-
try:
|
957 |
-
with open(filename, "wb") as f: f.write(cam0_img.getvalue())
|
958 |
-
st.session_state['cam0_file'] = filename; st.session_state['history'].append(f"Snapshot from Cam 0: {filename}"); st.image(Image.open(filename), caption="Camera 0 Snap", use_container_width=True); logger.info(f"Saved snapshot from Camera 0: {filename}"); st.success(f"Saved {filename}")
|
959 |
-
update_gallery(); # Refresh sidebar without rerun
|
960 |
-
except Exception as e: st.error(f"Failed to save Cam 0 snap: {e}"); logger.error(f"Failed to save Cam 0 snap {filename}: {e}")
|
961 |
-
with cols[1]:
|
962 |
-
cam1_img = st.camera_input("Take a picture - Cam 1", key="main_cam1")
|
963 |
-
if cam1_img:
|
964 |
-
filename = generate_filename("cam1_snap")
|
965 |
-
if st.session_state.get('cam1_file') and os.path.exists(st.session_state['cam1_file']):
|
966 |
-
try:
|
967 |
-
os.remove(st.session_state['cam1_file'])
|
968 |
-
except OSError:
|
969 |
-
pass
|
970 |
-
try:
|
971 |
-
with open(filename, "wb") as f: f.write(cam1_img.getvalue())
|
972 |
-
st.session_state['cam1_file'] = filename; st.session_state['history'].append(f"Snapshot from Cam 1: {filename}"); st.image(Image.open(filename), caption="Camera 1 Snap", use_container_width=True); logger.info(f"Saved snapshot from Camera 1: {filename}"); st.success(f"Saved {filename}")
|
973 |
-
update_gallery(); # Refresh sidebar without rerun
|
974 |
-
except Exception as e: st.error(f"Failed to save Cam 1 snap: {e}"); logger.error(f"Failed to save Cam 1 snap {filename}: {e}")
|
975 |
-
|
976 |
-
|
977 |
-
# --- Tab 3: Download PDFs ---
|
978 |
-
with tabs[2]:
|
979 |
-
st.header("Download PDFs 📥")
|
980 |
-
st.markdown("Download PDFs from URLs and optionally create image snapshots.")
|
981 |
-
if st.button("Load Example arXiv URLs 📚", key="load_examples"):
|
982 |
-
example_urls = ["https://arxiv.org/pdf/2308.03892", "https://arxiv.org/pdf/1706.03762", "https://arxiv.org/pdf/2402.17764", "https://www.clickdimensions.com/links/ACCERL/"]
|
983 |
-
st.session_state['pdf_urls_input'] = "\n".join(example_urls)
|
984 |
-
url_input = st.text_area("Enter PDF URLs (one per line)", value=st.session_state.get('pdf_urls_input', ""), height=150, key="pdf_urls_textarea")
|
985 |
-
if st.button("Robo-Download PDFs 🤖", key="download_pdfs_button"):
|
986 |
-
urls = [url.strip() for url in url_input.strip().split("\n") if url.strip()]
|
987 |
-
if not urls: st.warning("Please enter at least one URL.")
|
988 |
-
else:
|
989 |
-
progress_bar = st.progress(0); status_text = st.empty(); total_urls = len(urls); download_count = 0; existing_pdfs = get_pdf_files()
|
990 |
-
for idx, url in enumerate(urls):
|
991 |
-
output_path = pdf_url_to_filename(url); status_text.text(f"Processing {idx + 1}/{total_urls}: {os.path.basename(output_path)}..."); progress_bar.progress((idx + 1) / total_urls)
|
992 |
-
if os.path.exists(output_path): # Check existence properly
|
993 |
-
st.info(f"Already exists: {os.path.basename(output_path)}")
|
994 |
-
st.session_state['downloaded_pdfs'][url] = output_path
|
995 |
-
# Ensure checkbox state is preserved or reset if needed
|
996 |
-
st.session_state['asset_checkboxes'][output_path] = st.session_state['asset_checkboxes'].get(output_path, False)
|
997 |
-
else:
|
998 |
-
if download_pdf(url, output_path):
|
999 |
-
st.session_state['downloaded_pdfs'][url] = output_path; logger.info(f"Downloaded PDF from {url} to {output_path}"); st.session_state['history'].append(f"Downloaded PDF: {output_path}"); st.session_state['asset_checkboxes'][output_path] = False; download_count += 1; existing_pdfs.append(output_path)
|
1000 |
-
else: st.error(f"Failed to download: {url}")
|
1001 |
-
status_text.success(f"Download process complete! Successfully downloaded {download_count} new PDFs.")
|
1002 |
-
if download_count > 0: update_gallery(); # Refresh sidebar without rerun
|
1003 |
-
|
1004 |
-
st.subheader("Create Snapshots from Gallery PDFs")
|
1005 |
-
snapshot_mode = st.selectbox("Snapshot Mode", ["First Page (High-Res)", "First Two Pages (High-Res)", "All Pages (High-Res)", "First Page (Low-Res Preview)"], key="pdf_snapshot_mode")
|
1006 |
-
resolution_map = {"First Page (High-Res)": 2.0, "First Two Pages (High-Res)": 2.0, "All Pages (High-Res)": 2.0, "First Page (Low-Res Preview)": 1.0}
|
1007 |
-
mode_key_map = {"First Page (High-Res)": "single", "First Two Pages (High-Res)": "twopage", "All Pages (High-Res)": "allpages", "First Page (Low-Res Preview)": "single"}
|
1008 |
-
resolution = resolution_map[snapshot_mode]; mode_key = mode_key_map[snapshot_mode]
|
1009 |
-
if st.button("Snapshot Selected PDFs 📸", key="snapshot_selected_pdfs"):
|
1010 |
-
selected_pdfs = [path for path in get_gallery_files(['pdf']) if st.session_state['asset_checkboxes'].get(path, False)]
|
1011 |
-
if not selected_pdfs: st.warning("No PDFs selected in the sidebar gallery!")
|
1012 |
-
else:
|
1013 |
-
st.info(f"Starting snapshot process for {len(selected_pdfs)} selected PDF(s)..."); snapshot_count = 0; total_snapshots_generated = 0
|
1014 |
-
for pdf_path in selected_pdfs:
|
1015 |
-
if not os.path.exists(pdf_path): st.warning(f"File not found: {pdf_path}. Skipping."); continue
|
1016 |
-
new_snapshots = asyncio.run(process_pdf_snapshot(pdf_path, mode_key, resolution))
|
1017 |
-
if new_snapshots:
|
1018 |
-
snapshot_count += 1; total_snapshots_generated += len(new_snapshots)
|
1019 |
-
st.write(f"Snapshots for {os.path.basename(pdf_path)}:"); cols = st.columns(3)
|
1020 |
-
for i, snap_path in enumerate(new_snapshots):
|
1021 |
-
with cols[i % 3]:
|
1022 |
-
try: st.image(Image.open(snap_path), caption=os.path.basename(snap_path), use_container_width=True)
|
1023 |
-
except Exception as snap_img_err: st.warning(f"Cannot display snap {os.path.basename(snap_path)}: {snap_img_err}")
|
1024 |
-
st.session_state['asset_checkboxes'][snap_path] = False # Add to gallery
|
1025 |
-
if total_snapshots_generated > 0: st.success(f"Generated {total_snapshots_generated} snapshots from {snapshot_count} PDFs."); update_gallery(); # Refresh sidebar without rerun
|
1026 |
-
else: st.warning("No snapshots were generated. Check logs or PDF files.")
|
1027 |
-
|
1028 |
-
|
1029 |
-
# --- Tab 4: Build Titan (Local Models) ---
|
1030 |
-
with tabs[3]:
|
1031 |
-
st.header("Build Titan (Local Models) 🌱")
|
1032 |
-
st.markdown("Download and save models from Hugging Face Hub for local use.")
|
1033 |
-
if not _transformers_available:
|
1034 |
-
st.error("Transformers library not available. Cannot download or load local models.")
|
1035 |
-
else:
|
1036 |
-
build_model_type = st.selectbox("Select Model Type", ["Causal LM", "Vision/Multimodal", "OCR", "Diffusion"], key="build_type_local")
|
1037 |
-
st.subheader(f"Download {build_model_type} Model")
|
1038 |
-
hf_model_id = st.text_input("Hugging Face Model ID", placeholder=f"e.g., {'google/gemma-2-9b-it' if build_model_type == 'Causal LM' else 'llava-hf/llava-1.5-7b-hf' if build_model_type == 'Vision/Multimodal' else 'microsoft/trocr-base-handwritten' if build_model_type == 'OCR' else 'stabilityai/stable-diffusion-xl-base-1.0'}", key="build_hf_model_id")
|
1039 |
-
local_model_name = st.text_input("Local Name for this Model", value=f"{build_model_type.split('/')[0].lower()}_{os.path.basename(hf_model_id).replace('.','') if hf_model_id else 'model'}", key="build_local_name", help="A unique name to identify this model locally.")
|
1040 |
-
st.info("Private or gated models require a valid Hugging Face token (set via HF_TOKEN env var or the Custom Key in sidebar API settings).")
|
1041 |
-
|
1042 |
-
if st.button(f"Download & Save '{hf_model_id}' Locally", key="build_download_button", disabled=not hf_model_id or not local_model_name):
|
1043 |
-
local_name_check = re.sub(r'[^\w\-]+', '_', local_model_name) # Sanitize proposed name for path check
|
1044 |
-
potential_path_base = os.path.join(f"{build_model_type.split('/')[0].lower()}_models", local_name_check)
|
1045 |
-
|
1046 |
-
if any(os.path.basename(p) == local_name_check for p in get_local_model_paths(build_model_type.split('/')[0].lower())):
|
1047 |
-
st.error(f"A local model folder named '{local_name_check}' already exists. Choose a different local name.")
|
1048 |
-
else:
|
1049 |
-
model_type_map = {"Causal LM": "causal", "Vision/Multimodal": "vision", "OCR": "ocr", "Diffusion": "diffusion"}
|
1050 |
-
model_type_short = model_type_map.get(build_model_type, "unknown")
|
1051 |
-
config = LocalModelConfig(name=local_model_name, hf_id=hf_model_id, model_type=model_type_short)
|
1052 |
-
save_path = config.get_full_path()
|
1053 |
-
os.makedirs(os.path.dirname(save_path), exist_ok=True)
|
1054 |
-
st.info(f"Attempting to download '{hf_model_id}' to '{save_path}'..."); progress_bar_build = st.progress(0); status_text_build = st.empty()
|
1055 |
-
token_build = st.session_state.hf_custom_key or HF_TOKEN or None
|
1056 |
-
try:
|
1057 |
-
if build_model_type == "Diffusion":
|
1058 |
-
if not _diffusers_available: raise ImportError("Diffusers library required.")
|
1059 |
-
status_text_build.text("Downloading diffusion pipeline..."); pipeline_obj = StableDiffusionPipeline.from_pretrained(hf_model_id, token=token_build); status_text_build.text("Saving diffusion model pipeline..."); pipeline_obj.save_pretrained(save_path)
|
1060 |
-
st.session_state.local_models[save_path] = {'type': 'diffusion', 'hf_id': hf_model_id, 'model':None, 'processor':None} # Mark as downloaded
|
1061 |
-
st.success(f"Diffusion model '{hf_model_id}' downloaded and saved to {save_path}")
|
1062 |
-
del pipeline_obj # Free memory
|
1063 |
-
else:
|
1064 |
-
status_text_build.text("Downloading model components...")
|
1065 |
-
if model_type_short == 'causal': model_class, proc_tok_class = AutoModelForCausalLM, AutoTokenizer; proc_name="tokenizer"
|
1066 |
-
elif model_type_short == 'vision': model_class, proc_tok_class = AutoModelForVision2Seq, AutoProcessor; proc_name="processor"
|
1067 |
-
elif model_type_short == 'ocr': model_class, proc_tok_class = AutoModelForVision2Seq, AutoProcessor; proc_name="processor"
|
1068 |
-
else: raise ValueError(f"Unknown model type: {model_type_short}")
|
1069 |
-
|
1070 |
-
model_obj = model_class.from_pretrained(hf_model_id, token=token_build); model_obj.save_pretrained(save_path)
|
1071 |
-
status_text_build.text(f"Model saved. Downloading {proc_name}..."); proc_tok_obj = proc_tok_class.from_pretrained(hf_model_id, token=token_build); proc_tok_obj.save_pretrained(save_path)
|
1072 |
-
status_text_build.text(f"Components saved. Loading '{local_model_name}' into memory...")
|
1073 |
-
device = "cuda" if torch.cuda.is_available() else "cpu"
|
1074 |
-
# Use trust_remote_code cautiously if needed for specific models
|
1075 |
-
reloaded_model = model_class.from_pretrained(save_path).to(device)
|
1076 |
-
reloaded_proc_tok = proc_tok_class.from_pretrained(save_path)
|
1077 |
-
st.session_state.local_models[save_path] = {'type': model_type_short, 'hf_id': hf_model_id, 'model': reloaded_model, proc_name: reloaded_proc_tok}
|
1078 |
-
# Add tokenizer specifically if it's nested in processor
|
1079 |
-
if proc_name == "processor" and hasattr(reloaded_proc_tok, 'tokenizer'):
|
1080 |
-
st.session_state.local_models[save_path]['tokenizer'] = reloaded_proc_tok.tokenizer
|
1081 |
-
st.success(f"{build_model_type} model '{hf_model_id}' downloaded to {save_path} and loaded ({device})."); st.session_state.selected_local_model_path = save_path
|
1082 |
-
del model_obj, proc_tok_obj # Free memory from download cache if possible
|
1083 |
-
except (RepositoryNotFoundError, GatedRepoError) as e: st.error(f"Download failed: Repo not found or requires access/token. Error: {e}"); logger.error(f"Download failed for {hf_model_id}: {e}"); #if os.path.exists(save_path): shutil.rmtree(save_path)
|
1084 |
-
except ImportError as e: st.error(f"Download failed: Library missing. {e}"); logger.error(f"ImportError for {hf_model_id}: {e}")
|
1085 |
-
except Exception as e: st.error(f"Download error: {e}"); logger.error(f"Download failed for {hf_model_id}: {e}", exc_info=True); #if os.path.exists(save_path): shutil.rmtree(save_path)
|
1086 |
-
finally: progress_bar_build.progress(1.0); status_text_build.empty(); #st.rerun() # Rerun removed
|
1087 |
-
|
1088 |
-
st.subheader("Manage Local Models")
|
1089 |
-
# Refresh list for display
|
1090 |
-
loaded_model_paths = list(st.session_state.get('local_models', {}).keys())
|
1091 |
-
if not loaded_model_paths: st.info("No models downloaded yet.")
|
1092 |
-
else:
|
1093 |
-
models_df_data = []
|
1094 |
-
for path in loaded_model_paths:
|
1095 |
-
data = st.session_state.local_models.get(path, {}) # Safely get data
|
1096 |
-
models_df_data.append({
|
1097 |
-
"Local Name": os.path.basename(path), "Type": data.get('type', '?'),
|
1098 |
-
"HF ID": data.get('hf_id', '?'), "Loaded": "Yes" if data.get('model') else "No", "Path": path })
|
1099 |
-
models_df = pd.DataFrame(models_df_data); st.dataframe(models_df, use_container_width=True, hide_index=True, column_order=["Local Name", "Type", "HF ID", "Loaded"])
|
1100 |
-
model_to_delete = st.selectbox("Select model to delete", [""] + [os.path.basename(p) for p in loaded_model_paths], key="delete_model_select")
|
1101 |
-
if model_to_delete and st.button(f"Delete Local Model '{model_to_delete}'", type="primary"):
|
1102 |
-
path_to_delete = next((p for p in loaded_model_paths if os.path.basename(p) == model_to_delete), None)
|
1103 |
-
if path_to_delete:
|
1104 |
-
try:
|
1105 |
-
# Explicitly delete model objects from memory first if they exist
|
1106 |
-
if path_to_delete in st.session_state.local_models:
|
1107 |
-
model_data_to_del = st.session_state.local_models[path_to_delete]
|
1108 |
-
if model_data_to_del.get('model'): del model_data_to_del['model']
|
1109 |
-
if model_data_to_del.get('tokenizer'): del model_data_to_del['tokenizer']
|
1110 |
-
if model_data_to_del.get('processor'): del model_data_to_del['processor']
|
1111 |
-
if _transformers_available and torch.cuda.is_available(): torch.cuda.empty_cache() # Try to clear VRAM
|
1112 |
-
|
1113 |
-
# Remove from session state
|
1114 |
-
st.session_state.local_models.pop(path_to_delete, None)
|
1115 |
-
if st.session_state.selected_local_model_path == path_to_delete: st.session_state.selected_local_model_path = None
|
1116 |
-
# Delete from disk
|
1117 |
-
if os.path.exists(path_to_delete): shutil.rmtree(path_to_delete)
|
1118 |
-
st.success(f"Deleted model '{model_to_delete}'."); logger.info(f"Deleted local model: {path_to_delete}"); st.rerun()
|
1119 |
-
except Exception as e: st.error(f"Failed to delete model '{model_to_delete}': {e}"); logger.error(f"Failed to delete model {path_to_delete}: {e}")
|
1120 |
-
|
1121 |
-
|
1122 |
-
# --- Tab 5: PDF Process (HF) ---
|
1123 |
-
with tabs[4]:
|
1124 |
-
st.header("PDF Page Process with HF Models 📄")
|
1125 |
-
st.markdown("Upload PDFs, view pages, and extract text/info using selected HF models (API or Local Vision/OCR).")
|
1126 |
-
pdf_use_api = st.radio("Choose Processing Method", ["Hugging Face API", "Loaded Local Model"], key="pdf_process_source", horizontal=True, help="API uses settings from sidebar. Local uses the selected local model (if suitable for vision/OCR).")
|
1127 |
-
if pdf_use_api == "Hugging Face API": st.info(f"Using API Model: {st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model} (likely image-to-text)")
|
1128 |
-
else:
|
1129 |
-
if st.session_state.selected_local_model_path: st.info(f"Using Local Model: {os.path.basename(st.session_state.selected_local_model_path)}")
|
1130 |
-
else: st.warning("No local model selected.")
|
1131 |
-
|
1132 |
-
uploaded_pdfs_process_hf = st.file_uploader("Upload PDF files to process", type=["pdf"], accept_multiple_files=True, key="pdf_process_uploader_hf")
|
1133 |
-
if uploaded_pdfs_process_hf:
|
1134 |
-
process_all_pages_pdf = st.checkbox("Process All Pages (can be slow/expensive)", value=False, key="pdf_process_all_hf")
|
1135 |
-
pdf_prompt = st.text_area("Prompt for PDF Page Processing", "Extract the text content from this page.", key="pdf_process_prompt_hf")
|
1136 |
-
if st.button("Process Uploaded PDFs with HF", key="process_uploaded_pdfs_hf"):
|
1137 |
-
if pdf_use_api == "Loaded Local Model" and not st.session_state.selected_local_model_path: st.error("Cannot process locally: No local model selected.")
|
1138 |
-
else:
|
1139 |
-
combined_text_output_hf = f"# HF PDF Processing Results ({'API' if pdf_use_api else 'Local'})\n\n"; total_pages_processed_hf = 0; output_placeholder_hf = st.container()
|
1140 |
-
for pdf_file in uploaded_pdfs_process_hf:
|
1141 |
-
output_placeholder_hf.markdown(f"--- \n### Processing: {pdf_file.name}")
|
1142 |
-
try:
|
1143 |
-
pdf_bytes = pdf_file.read(); doc = fitz.open("pdf", pdf_bytes); num_pages = len(doc)
|
1144 |
-
pages_to_process = range(num_pages) if process_all_pages_pdf else range(min(1, num_pages))
|
1145 |
-
output_placeholder_hf.info(f"Processing {len(pages_to_process)} of {num_pages} pages..."); doc_text = f"## File: {pdf_file.name}\n\n"
|
1146 |
-
for i in pages_to_process:
|
1147 |
-
page_placeholder = output_placeholder_hf.empty(); page_placeholder.info(f"Processing Page {i + 1}/{num_pages}...")
|
1148 |
-
page = doc.load_page(i); pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0)); img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
|
1149 |
-
cols_pdf = output_placeholder_hf.columns(2); cols_pdf[0].image(img, caption=f"Page {i+1}", use_container_width=True)
|
1150 |
-
with cols_pdf[1], st.spinner("Processing page with HF model..."): hf_text = process_image_hf(img, pdf_prompt, use_api=pdf_use_api)
|
1151 |
-
st.text_area(f"Result (Page {i+1})", hf_text, height=250, key=f"pdf_hf_out_{pdf_file.name}_{i}")
|
1152 |
-
doc_text += f"### Page {i + 1}\n\n{hf_text}\n\n---\n\n"; total_pages_processed_hf += 1; page_placeholder.empty()
|
1153 |
-
combined_text_output_hf += doc_text; doc.close()
|
1154 |
-
except Exception as e: output_placeholder_hf.error(f"Error processing {pdf_file.name}: {str(e)}")
|
1155 |
-
if total_pages_processed_hf > 0:
|
1156 |
-
st.markdown("--- \n### Combined Processing Results"); st.text_area("Full Output", combined_text_output_hf, height=400, key="combined_pdf_hf_output")
|
1157 |
-
output_filename_pdf_hf = generate_filename("hf_processed_pdfs", "md")
|
1158 |
-
try:
|
1159 |
-
with open(output_filename_pdf_hf, "w", encoding="utf-8") as f: f.write(combined_text_output_hf)
|
1160 |
-
st.success(f"Combined output saved to {output_filename_pdf_hf}")
|
1161 |
-
st.markdown(get_download_link(output_filename_pdf_hf, "text/markdown", "Download Combined MD"), unsafe_allow_html=True)
|
1162 |
-
st.session_state['asset_checkboxes'][output_filename_pdf_hf] = False; update_gallery() # Refresh sidebar
|
1163 |
-
except IOError as e: st.error(f"Failed to save combined output file: {e}")
|
1164 |
-
|
1165 |
-
# --- Tab 6: Image Process (HF) ---
|
1166 |
-
with tabs[5]:
|
1167 |
-
st.header("Image Process with HF Models 🖼️")
|
1168 |
-
st.markdown("Upload images and process them using selected HF models (API or Local).")
|
1169 |
-
img_use_api = st.radio("Choose Processing Method", ["Hugging Face API", "Loaded Local Model"], key="img_process_source_hf", horizontal=True)
|
1170 |
-
if img_use_api == "Hugging Face API": st.info(f"Using API Model: {st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model} (likely image-to-text)")
|
1171 |
-
else:
|
1172 |
-
if st.session_state.selected_local_model_path: st.info(f"Using Local Model: {os.path.basename(st.session_state.selected_local_model_path)}")
|
1173 |
-
else: st.warning("No local model selected.")
|
1174 |
-
img_prompt_hf = st.text_area("Prompt for Image Processing", "Describe this image in detail.", key="img_process_prompt_hf")
|
1175 |
-
uploaded_images_process_hf = st.file_uploader("Upload image files", type=["png", "jpg", "jpeg"], accept_multiple_files=True, key="image_process_uploader_hf")
|
1176 |
-
if uploaded_images_process_hf:
|
1177 |
-
if st.button("Process Uploaded Images with HF", key="process_images_hf"):
|
1178 |
-
if img_use_api == "Loaded Local Model" and not st.session_state.selected_local_model_path: st.error("Cannot process locally: No local model selected.")
|
1179 |
-
else:
|
1180 |
-
combined_img_text_hf = f"# HF Image Processing Results ({'API' if img_use_api else 'Local'})\n\n**Prompt:** {img_prompt_hf}\n\n---\n\n"; images_processed_hf = 0; output_img_placeholder_hf = st.container()
|
1181 |
-
for img_file in uploaded_images_process_hf:
|
1182 |
-
output_img_placeholder_hf.markdown(f"### Processing: {img_file.name}")
|
1183 |
-
try:
|
1184 |
-
img = Image.open(img_file); cols_img_hf = output_img_placeholder_hf.columns(2); cols_img_hf[0].image(img, caption=f"Input: {img_file.name}", use_container_width=True)
|
1185 |
-
with cols_img_hf[1], st.spinner("Processing image with HF model..."): hf_img_text = process_image_hf(img, img_prompt_hf, use_api=img_use_api)
|
1186 |
-
st.text_area(f"Result", hf_img_text, height=300, key=f"img_hf_out_{img_file.name}")
|
1187 |
-
combined_img_text_hf += f"## Image: {img_file.name}\n\n{hf_img_text}\n\n---\n\n"; images_processed_hf += 1
|
1188 |
-
except UnidentifiedImageError: output_img_placeholder_hf.error(f"Invalid Image: {img_file.name}. Skipping.")
|
1189 |
-
except Exception as e: output_img_placeholder_hf.error(f"Error processing {img_file.name}: {str(e)}")
|
1190 |
-
if images_processed_hf > 0:
|
1191 |
-
st.markdown("--- \n### Combined Processing Results"); st.text_area("Full Output", combined_img_text_hf, height=400, key="combined_img_hf_output")
|
1192 |
-
output_filename_img_hf = generate_filename("hf_processed_images", "md")
|
1193 |
-
try:
|
1194 |
-
with open(output_filename_img_hf, "w", encoding="utf-8") as f: f.write(combined_img_text_hf)
|
1195 |
-
st.success(f"Combined output saved to {output_filename_img_hf}"); st.markdown(get_download_link(output_filename_img_hf, "text/markdown", "Download Combined MD"), unsafe_allow_html=True)
|
1196 |
-
st.session_state['asset_checkboxes'][output_filename_img_hf] = False; update_gallery() # Refresh sidebar
|
1197 |
-
except IOError as e: st.error(f"Failed to save combined output file: {e}")
|
1198 |
-
|
1199 |
-
# --- Tab 7: Text Process (HF) ---
|
1200 |
-
with tabs[6]:
|
1201 |
-
st.header("Text Process with HF Models 📝")
|
1202 |
-
st.markdown("Process Markdown (.md) or Text (.txt) files using selected HF models (API or Local).")
|
1203 |
-
text_use_api = st.radio("Choose Processing Method", ["Hugging Face API", "Loaded Local Model"], key="text_process_source_hf", horizontal=True)
|
1204 |
-
if text_use_api == "Hugging Face API": st.info(f"Using API Model: {st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model}")
|
1205 |
-
else:
|
1206 |
-
if st.session_state.selected_local_model_path: st.info(f"Using Local Model: {os.path.basename(st.session_state.selected_local_model_path)}")
|
1207 |
-
else: st.warning("No local model selected.")
|
1208 |
-
text_files_hf = get_gallery_files(['md', 'txt'])
|
1209 |
-
if not text_files_hf: st.warning("No .md or .txt files in gallery to process.")
|
1210 |
-
else:
|
1211 |
-
selected_text_file_hf = st.selectbox("Select Text/MD File to Process", options=[""] + text_files_hf, format_func=lambda x: os.path.basename(x) if x else "Select a file...", key="text_process_select_hf")
|
1212 |
-
if selected_text_file_hf:
|
1213 |
-
st.write(f"Selected: {os.path.basename(selected_text_file_hf)}")
|
1214 |
-
try:
|
1215 |
-
with open(selected_text_file_hf, "r", encoding="utf-8", errors='ignore') as f: content_text_hf = f.read()
|
1216 |
-
st.text_area("File Content Preview", content_text_hf[:1000] + ("..." if len(content_text_hf) > 1000 else ""), height=200, key="text_content_preview_hf")
|
1217 |
-
prompt_text_hf = st.text_area("Enter Prompt for this File", "Summarize the key points of this text.", key="text_individual_prompt_hf")
|
1218 |
-
if st.button(f"Process '{os.path.basename(selected_text_file_hf)}' with HF", key=f"process_text_hf_btn"):
|
1219 |
-
if text_use_api == "Loaded Local Model" and not st.session_state.selected_local_model_path: st.error("Cannot process locally: No local model selected.")
|
1220 |
-
else:
|
1221 |
-
with st.spinner("Processing text with HF model..."): result_text_processed = process_text_hf(content_text_hf, prompt_text_hf, use_api=text_use_api)
|
1222 |
-
st.markdown("### Processing Result"); st.markdown(result_text_processed)
|
1223 |
-
output_filename_text_hf = generate_filename(f"hf_processed_{os.path.splitext(os.path.basename(selected_text_file_hf))[0]}", "md")
|
1224 |
-
try:
|
1225 |
-
with open(output_filename_text_hf, "w", encoding="utf-8") as f: f.write(result_text_processed)
|
1226 |
-
st.success(f"Result saved to {output_filename_text_hf}"); st.markdown(get_download_link(output_filename_text_hf, "text/markdown", "Download Result MD"), unsafe_allow_html=True)
|
1227 |
-
st.session_state['asset_checkboxes'][output_filename_text_hf] = False; update_gallery() # Refresh sidebar
|
1228 |
-
except IOError as e: st.error(f"Failed to save result file: {e}")
|
1229 |
-
except FileNotFoundError: st.error("Selected file not found.")
|
1230 |
-
except Exception as e: st.error(f"Error reading file: {e}")
|
1231 |
-
|
1232 |
-
# --- Tab 8: Test OCR (HF) ---
|
1233 |
-
with tabs[7]:
|
1234 |
-
st.header("Test OCR with HF Models 🔍")
|
1235 |
-
st.markdown("Select an image/PDF and run OCR using HF models (API or Local - requires suitable local model).")
|
1236 |
-
ocr_use_api = st.radio("Choose OCR Method", ["Hugging Face API (Basic Captioning/OCR)", "Loaded Local OCR Model"], key="ocr_source_hf", horizontal=True, help="API uses basic image-to-text. Local requires a dedicated OCR model (e.g., TrOCR) to be loaded.")
|
1237 |
-
if ocr_use_api == "Loaded Local OCR Model":
|
1238 |
-
if st.session_state.selected_local_model_path:
|
1239 |
-
model_info = st.session_state.local_models.get(st.session_state.selected_local_model_path,{})
|
1240 |
-
model_type = model_info.get('type'); model_name = os.path.basename(st.session_state.selected_local_model_path)
|
1241 |
-
if model_type != 'ocr': st.warning(f"Selected model ({model_name}) is type '{model_type}', not 'ocr'. Results may be poor.")
|
1242 |
-
else: st.info(f"Using Local OCR Model: {model_name}")
|
1243 |
-
else: st.warning("No local model selected.")
|
1244 |
-
|
1245 |
-
gallery_files_ocr_hf = get_gallery_files(['png', 'jpg', 'jpeg', 'pdf'])
|
1246 |
-
if not gallery_files_ocr_hf: st.warning("No images or PDFs in gallery.")
|
1247 |
-
else:
|
1248 |
-
selected_file_ocr_hf = st.selectbox("Select Image or PDF from Gallery for OCR", options=[""] + gallery_files_ocr_hf, format_func=lambda x: os.path.basename(x) if x else "Select a file...", key="ocr_select_file_hf")
|
1249 |
-
if selected_file_ocr_hf:
|
1250 |
-
st.write(f"Selected: {os.path.basename(selected_file_ocr_hf)}"); file_ext_ocr_hf = os.path.splitext(selected_file_ocr_hf)[1].lower(); image_to_ocr_hf = None; page_info_hf = ""
|
1251 |
-
try:
|
1252 |
-
if file_ext_ocr_hf in ['.png', '.jpg', '.jpeg']: image_to_ocr_hf = Image.open(selected_file_ocr_hf)
|
1253 |
-
elif file_ext_ocr_hf == '.pdf':
|
1254 |
-
doc = fitz.open(selected_file_ocr_hf)
|
1255 |
-
if len(doc) > 0: pix = doc[0].get_pixmap(matrix=fitz.Matrix(2.0, 2.0)); image_to_ocr_hf = Image.frombytes("RGB", [pix.width, pix.height], pix.samples); page_info_hf = " (Page 1)"
|
1256 |
-
else: st.warning("Selected PDF is empty.")
|
1257 |
-
doc.close()
|
1258 |
-
if image_to_ocr_hf:
|
1259 |
-
st.image(image_to_ocr_hf, caption=f"Image for OCR{page_info_hf}", use_container_width=True)
|
1260 |
-
if st.button("Run HF OCR on this Image 🚀", key="ocr_run_button_hf"):
|
1261 |
-
if ocr_use_api == "Loaded Local OCR Model" and not st.session_state.selected_local_model_path: st.error("Cannot run locally: No local model selected.")
|
1262 |
-
else:
|
1263 |
-
output_ocr_file_hf = generate_filename(f"hf_ocr_{os.path.splitext(os.path.basename(selected_file_ocr_hf))[0]}", "txt"); st.session_state['processing']['ocr'] = True
|
1264 |
-
with st.spinner("Performing OCR with HF model..."): ocr_result_hf = asyncio.run(process_hf_ocr(image_to_ocr_hf, output_ocr_file_hf, use_api=ocr_use_api))
|
1265 |
-
st.session_state['processing']['ocr'] = False; st.text_area("OCR Result", ocr_result_hf, height=300, key="ocr_result_display_hf")
|
1266 |
-
if ocr_result_hf and not ocr_result_hf.startswith("Error") and not ocr_result_hf.startswith("["):
|
1267 |
-
entry = f"HF OCR: {selected_file_ocr_hf}{page_info_hf} -> {output_ocr_file_hf}"
|
1268 |
-
st.session_state['history'].append(entry)
|
1269 |
-
if len(ocr_result_hf) > 5: st.success(f"OCR output saved to {output_ocr_file_hf}"); st.markdown(get_download_link(output_ocr_file_hf, "text/plain", "Download OCR Text"), unsafe_allow_html=True); st.session_state['asset_checkboxes'][output_ocr_file_hf] = False; update_gallery() # Refresh sidebar
|
1270 |
-
else: st.warning("OCR output seems short/empty.")
|
1271 |
-
else: st.error(f"OCR failed. {ocr_result_hf}")
|
1272 |
-
except Exception as e: st.error(f"Error loading file for OCR: {e}")
|
1273 |
-
|
1274 |
-
# --- Tab 9: Test Image Gen (Diffusers) ---
|
1275 |
-
with tabs[8]:
|
1276 |
-
st.header("Test Image Generation (Diffusers) 🎨")
|
1277 |
-
st.markdown("Generate images using Stable Diffusion models loaded locally via the Diffusers library.")
|
1278 |
-
if not _diffusers_available: st.error("Diffusers library is required.")
|
1279 |
-
else:
|
1280 |
-
local_diffusion_paths = get_local_model_paths("diffusion") # Check diffusion_models folder
|
1281 |
-
if not local_diffusion_paths: st.warning("No local diffusion models found. Download one using the 'Build Titan' tab."); selected_diffusion_model_path = None
|
1282 |
-
else: selected_diffusion_model_path = st.selectbox("Select Local Diffusion Model", options=[""] + local_diffusion_paths, format_func=lambda x: os.path.basename(x) if x else "Select...", key="imggen_diffusion_model_select")
|
1283 |
-
prompt_imggen_diff = st.text_area("Image Generation Prompt", "A photorealistic cat wearing sunglasses, studio lighting", key="imggen_prompt_diff")
|
1284 |
-
neg_prompt_imggen_diff = st.text_area("Negative Prompt (Optional)", "ugly, deformed, blurry, low quality", key="imggen_neg_prompt_diff")
|
1285 |
-
steps_imggen_diff = st.slider("Inference Steps", 10, 100, 25, key="imggen_steps"); guidance_imggen_diff = st.slider("Guidance Scale", 1.0, 20.0, 7.5, step=0.5, key="imggen_guidance")
|
1286 |
-
if st.button("Generate Image 🚀", key="imggen_run_button_diff", disabled=not selected_diffusion_model_path):
|
1287 |
-
if not prompt_imggen_diff: st.warning("Please enter a prompt.")
|
1288 |
-
else:
|
1289 |
-
status_imggen = st.empty()
|
1290 |
-
try:
|
1291 |
-
status_imggen.info(f"Loading diffusion pipeline: {os.path.basename(selected_diffusion_model_path)}..."); device = "cuda" if _transformers_available and torch.cuda.is_available() else "cpu"; dtype = torch.float16 if device == "cuda" else torch.float32
|
1292 |
-
pipe = StableDiffusionPipeline.from_pretrained(selected_diffusion_model_path, torch_dtype=dtype).to(device); pipe.safety_checker = None # Optional
|
1293 |
-
status_imggen.info(f"Generating image on {device} ({dtype})..."); start_gen_time = time.time()
|
1294 |
-
gen_output = pipe(prompt=prompt_imggen_diff, negative_prompt=neg_prompt_imggen_diff or None, num_inference_steps=steps_imggen_diff, guidance_scale=guidance_imggen_diff)
|
1295 |
-
gen_image = gen_output.images[0]; elapsed_gen = int(time.time() - start_gen_time); status_imggen.success(f"Image generated in {elapsed_gen}s!")
|
1296 |
-
output_imggen_file_diff = generate_filename("diffusion_gen", "png"); gen_image.save(output_imggen_file_diff)
|
1297 |
-
st.image(gen_image, caption=f"Generated: {output_imggen_file_diff}", use_container_width=True)
|
1298 |
-
st.markdown(get_download_link(output_imggen_file_diff, "image/png", "Download Generated Image"), unsafe_allow_html=True)
|
1299 |
-
st.session_state['asset_checkboxes'][output_imggen_file_diff] = False; update_gallery() # Refresh sidebar
|
1300 |
-
st.session_state['history'].append(f"Diffusion Gen: '{prompt_imggen_diff[:30]}...' -> {output_imggen_file_diff}")
|
1301 |
-
except ImportError: st.error("Diffusers or Torch library not found.")
|
1302 |
-
except Exception as e: st.error(f"Image generation failed: {e}"); logger.error(f"Diffusion generation failed for {selected_diffusion_model_path}: {e}", exc_info=True)
|
1303 |
-
finally:
|
1304 |
-
if 'pipe' in locals():
|
1305 |
-
del pipe; torch.cuda.empty_cache() if device == "cuda" else None # Clear VRAM
|
1306 |
-
|
1307 |
-
# --- Tab 10: Character Editor ---
|
1308 |
-
with tabs[9]:
|
1309 |
-
st.header("Character Editor 🧑🎨"); st.subheader("Create Your Character")
|
1310 |
-
load_characters(); existing_char_names = [c['name'] for c in st.session_state.get('characters', [])]
|
1311 |
-
form_key = f"character_form_{st.session_state.get('char_form_reset_key', 0)}"
|
1312 |
-
with st.form(key=form_key):
|
1313 |
-
st.markdown("**Create New Character**")
|
1314 |
-
if st.form_submit_button("Randomize Content 🎲"): st.session_state['char_form_reset_key'] += 1; st.rerun()
|
1315 |
-
rand_name, rand_gender, rand_intro, rand_greeting = randomize_character_content()
|
1316 |
-
name_char = st.text_input("Name (3-25 chars...)", value=rand_name, max_chars=25, key="char_name_input")
|
1317 |
-
gender_char = st.radio("Gender", ["Male", "Female"], index=["Male", "Female"].index(rand_gender), key="char_gender_radio")
|
1318 |
-
intro_char = st.text_area("Intro (Public description)", value=rand_intro, max_chars=300, height=100, key="char_intro_area")
|
1319 |
-
greeting_char = st.text_area("Greeting (First message)", value=rand_greeting, max_chars=300, height=100, key="char_greeting_area")
|
1320 |
-
tags_char = st.text_input("Tags (comma-separated)", "OC, friendly", key="char_tags_input")
|
1321 |
-
submitted = st.form_submit_button("Create Character ✨")
|
1322 |
-
if submitted:
|
1323 |
-
error = False; # Validation checks...
|
1324 |
-
if not (3 <= len(name_char) <= 25): st.error("Name must be 3-25 characters."); error = True
|
1325 |
-
if not re.match(r'^[a-zA-Z0-9 _-]+$', name_char): st.error("Name contains invalid characters."); error = True
|
1326 |
-
if name_char in existing_char_names: st.error(f"Name '{name_char}' already exists!"); error = True
|
1327 |
-
if not intro_char or not greeting_char: st.error("Intro/Greeting cannot be empty."); error = True
|
1328 |
-
if not error:
|
1329 |
-
tag_list = [tag.strip() for tag in tags_char.split(',') if tag.strip()]
|
1330 |
-
character_data = {"name": name_char, "gender": gender_char, "intro": intro_char, "greeting": greeting_char, "created_at": datetime.now(pytz.timezone("US/Central")).strftime('%Y-%m-%d %H:%M:%S %Z'), "tags": tag_list}
|
1331 |
-
if save_character(character_data): st.success(f"Character '{name_char}' created!"); st.session_state['char_form_reset_key'] += 1; st.rerun()
|
1332 |
-
|
1333 |
-
# --- Tab 11: Character Gallery ---
|
1334 |
-
with tabs[10]:
|
1335 |
-
st.header("Character Gallery 🖼️"); load_characters(); characters_list = st.session_state.get('characters', [])
|
1336 |
-
if not characters_list: st.warning("No characters created yet.")
|
1337 |
-
else:
|
1338 |
-
st.subheader(f"Your Characters ({len(characters_list)})"); search_term = st.text_input("Search Characters by Name", key="char_gallery_search")
|
1339 |
-
if search_term: characters_list = [c for c in characters_list if search_term.lower() in c['name'].lower()]
|
1340 |
-
cols_char_gallery = st.columns(3); chars_to_delete = []
|
1341 |
-
for idx, char in enumerate(characters_list):
|
1342 |
-
with cols_char_gallery[idx % 3], st.container(border=True):
|
1343 |
-
st.markdown(f"**{char['name']}**"); st.caption(f"Gender: {char.get('gender', 'N/A')}")
|
1344 |
-
st.markdown("**Intro:**"); st.markdown(f"> {char.get('intro', '')}")
|
1345 |
-
st.markdown("**Greeting:**"); st.markdown(f"> {char.get('greeting', '')}")
|
1346 |
-
st.caption(f"Tags: {', '.join(char.get('tags', ['N/A']))}"); st.caption(f"Created: {char.get('created_at', 'N/A')}")
|
1347 |
-
delete_key_char = f"delete_char_{char['name']}_{idx}";
|
1348 |
-
if st.button(f"Delete", key=delete_key_char, type="primary", help=f"Delete {char['name']}"): chars_to_delete.append(char['name']) # Shorten button label
|
1349 |
-
if chars_to_delete:
|
1350 |
-
current_characters = st.session_state.get('characters', []); updated_characters = [c for c in current_characters if c['name'] not in chars_to_delete]
|
1351 |
-
st.session_state['characters'] = updated_characters
|
1352 |
-
try:
|
1353 |
-
with open("characters.json", "w", encoding='utf-8') as f: json.dump(updated_characters, f, indent=2)
|
1354 |
-
logger.info(f"Deleted characters: {', '.join(chars_to_delete)}"); st.success(f"Deleted: {', '.join(chars_to_delete)}"); st.rerun()
|
1355 |
-
except IOError as e: logger.error(f"Failed to save characters.json after deletion: {e}"); st.error("Failed to update character file.")
|
1356 |
-
|
1357 |
-
# --- Footer and Persistent Sidebar Elements ------------
|
1358 |
-
st.sidebar.markdown("---")
|
1359 |
-
# Update Sidebar Gallery (Call this at the end to reflect all changes)
|
1360 |
-
update_gallery()
|
1361 |
-
|
1362 |
-
# Action Logs in Sidebar
|
1363 |
-
st.sidebar.subheader("Action Logs 📜")
|
1364 |
-
log_expander = st.sidebar.expander("View Logs", expanded=False)
|
1365 |
-
with log_expander:
|
1366 |
-
# Display logs in reverse order (newest first)
|
1367 |
-
log_text = "\n".join([f"{record.levelname}: {record.message}" for record in reversed(log_records)])
|
1368 |
-
st.code(log_text, language='log')
|
1369 |
-
|
1370 |
-
# History in Sidebar
|
1371 |
-
st.sidebar.subheader("Session History 📜")
|
1372 |
-
history_expander = st.sidebar.expander("View History", expanded=False)
|
1373 |
-
with history_expander:
|
1374 |
-
for entry in reversed(st.session_state.get("history", [])):
|
1375 |
-
if entry: history_expander.write(f"- {entry}")
|
1376 |
-
|
1377 |
-
st.sidebar.markdown("---")
|
1378 |
-
st.sidebar.info("Using Hugging Face models for AI tasks.")
|
1379 |
-
st.sidebar.caption("App Modified by AI Assistant")
|
|
|
1 |
+
# app.py (streamlined and modularized for clarity and maintainability)
|
2 |
+
import streamlit as st
|
3 |
import os
|
|
|
|
|
4 |
import glob
|
5 |
+
import base64
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
import pandas as pd
|
7 |
+
import fitz
|
8 |
+
from PIL import Image
|
9 |
+
from io import BytesIO
|
10 |
+
from datetime import datetime
|
11 |
from reportlab.pdfgen import canvas
|
12 |
from reportlab.lib.utils import ImageReader
|
13 |
+
from markdown2 import markdown
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
14 |
|
15 |
+
# --- Config ---
|
16 |
st.set_page_config(
|
17 |
+
page_title="Vision & Layout Titans 🚀",
|
18 |
page_icon="🤖",
|
19 |
+
layout="wide"
|
|
|
|
|
|
|
|
|
|
|
|
|
20 |
)
|
21 |
|
22 |
+
# --- Helper Functions ---
|
23 |
+
def get_files(exts):
|
24 |
+
files = []
|
25 |
+
for ext in exts:
|
26 |
+
files.extend(glob.glob(f'*.{ext}'))
|
27 |
+
return sorted([f for f in files if f.lower() != "readme.md"])
|
28 |
+
|
29 |
+
def image_to_pdf(images, md_files):
|
30 |
+
buffer = BytesIO()
|
31 |
+
c = canvas.Canvas(buffer)
|
32 |
+
|
33 |
+
for img_path in images:
|
34 |
+
img = Image.open(img_path)
|
35 |
+
width, height = img.size
|
36 |
+
c.setPageSize((width, height))
|
37 |
+
c.drawImage(ImageReader(img), 0, 0, width, height)
|
38 |
+
c.showPage()
|
39 |
+
|
40 |
+
for md_path in md_files:
|
41 |
+
with open(md_path, 'r', encoding='utf-8') as f:
|
42 |
+
md_content = f.read()
|
43 |
+
html = markdown(md_content)
|
44 |
+
c.setPageSize((595, 842)) # A4 size
|
45 |
+
c.setFont("Helvetica", 10)
|
46 |
+
c.drawString(50, 800, md_content[:1000]) # Simplified render
|
47 |
+
c.showPage()
|
48 |
+
|
49 |
+
c.save()
|
50 |
+
buffer.seek(0)
|
51 |
+
return buffer
|
52 |
+
|
53 |
+
def render_pdf_gallery(pdf_files):
|
54 |
+
for pdf_file in pdf_files:
|
55 |
+
doc = fitz.open(pdf_file)
|
56 |
+
page = doc.load_page(0)
|
57 |
+
pix = page.get_pixmap(matrix=fitz.Matrix(0.5, 0.5))
|
58 |
+
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
|
59 |
+
st.image(img, caption=os.path.basename(pdf_file))
|
60 |
+
|
61 |
+
# --- Sidebar Management ---
|
62 |
+
st.sidebar.header("Content Management")
|
63 |
+
|
64 |
+
image_files = get_files(['png', 'jpg', 'jpeg'])
|
65 |
+
pdf_files = get_files(['pdf'])
|
66 |
+
md_files = get_files(['md'])
|
67 |
+
|
68 |
+
selected_images = st.sidebar.multiselect("Select Images", image_files)
|
69 |
+
selected_md_files = st.sidebar.multiselect("Select Markdown Files", md_files)
|
70 |
+
|
71 |
+
# PDF Gallery
|
72 |
+
if st.sidebar.checkbox("Show PDF Gallery"):
|
73 |
+
render_pdf_gallery(pdf_files)
|
74 |
+
|
75 |
+
# PDF Generation with rearrangement
|
76 |
+
st.sidebar.subheader("Reorder Content for PDF")
|
77 |
+
content_df = pd.DataFrame({
|
78 |
+
"Content": selected_images + selected_md_files,
|
79 |
+
"Type": ["Image"] * len(selected_images) + ["Markdown"] * len(selected_md_files),
|
80 |
+
"Order": range(len(selected_images) + len(selected_md_files))
|
81 |
+
})
|
82 |
+
|
83 |
+
edited_df = st.sidebar.data_editor(content_df, use_container_width=True)
|
84 |
+
sorted_contents = edited_df.sort_values('Order')['Content'].tolist()
|
85 |
+
|
86 |
+
if st.sidebar.button("Generate PDF"):
|
87 |
+
sorted_images = [item for item in sorted_contents if item in selected_images]
|
88 |
+
sorted_md_files = [item for item in sorted_contents if item in selected_md_files]
|
89 |
+
pdf_buffer = image_to_pdf(sorted_images, sorted_md_files)
|
90 |
+
st.sidebar.download_button("Download PDF", pdf_buffer, "output.pdf")
|
91 |
+
|
92 |
+
# Deletion
|
93 |
+
st.sidebar.subheader("Delete Files")
|
94 |
+
file_to_delete = st.sidebar.selectbox("Select file to delete", image_files + pdf_files + md_files)
|
95 |
+
if st.sidebar.button("Delete Selected File"):
|
96 |
+
os.remove(file_to_delete)
|
97 |
+
st.sidebar.success(f"Deleted {file_to_delete}")
|
98 |
+
st.rerun()
|
99 |
+
|
100 |
+
# --- Main Page ---
|
101 |
+
st.title("Vision & Layout Titans 🚀")
|
102 |
+
st.markdown("### Manage, View, and Export Your Files Easily!")
|
103 |
+
|
104 |
+
# Display selected images
|
105 |
+
st.subheader("Selected Images")
|
106 |
+
for img_path in selected_images:
|
107 |
+
img = Image.open(img_path)
|
108 |
+
st.image(img, caption=os.path.basename(img_path))
|
109 |
+
|
110 |
+
# Display selected markdown
|
111 |
+
st.subheader("Selected Markdown")
|
112 |
+
for md_path in selected_md_files:
|
113 |
+
with open(md_path, 'r', encoding='utf-8') as f:
|
114 |
+
md_content = f.read()
|
115 |
+
st.markdown(md_content[:500] + '...')
|
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