Update app.py
Browse files
app.py
CHANGED
@@ -1,115 +1,939 @@
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import streamlit as st
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import os
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import
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import base64
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import
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import
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from datetime import datetime
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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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# ---
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st.set_page_config(
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page_title="Vision & Layout Titans
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page_icon="π€",
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layout="wide"
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# --- Helper Functions ---
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def
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})
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st.
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import io
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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 logging
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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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import toml
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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 pytz
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import streamlit as st
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from PIL import Image, ImageDraw
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from reportlab.pdfgen import canvas
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from reportlab.lib.utils import ImageReader
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from reportlab.lib.pagesizes import letter
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
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from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
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from reportlab.lib.enums import TA_JUSTIFY
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import fitz
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import requests
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try:
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor, AutoModelForVision2Seq, pipeline
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_transformers_available = True
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except ImportError:
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_transformers_available = False
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st.sidebar.warning("AI/ML libraries (torch, transformers) not found. Local model features disabled.")
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try:
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from diffusers import StableDiffusionPipeline
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_diffusers_available = True
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except ImportError:
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_diffusers_available = False
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if _transformers_available:
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st.sidebar.warning("Diffusers library not found. Diffusion model features disabled.")
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try:
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from openai import OpenAI
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_openai_available = True
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except ImportError:
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_openai_available = False
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st.sidebar.warning("OpenAI library not found. OpenAI model features disabled.")
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from huggingface_hub import InferenceClient, HfApi, list_models
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from huggingface_hub.utils import RepositoryNotFoundError, GatedRepoError
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# --- App Configuration ---
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56 |
st.set_page_config(
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page_title="Vision & Layout Titans ππΌοΈ",
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page_icon="π€",
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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,
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'About': "Combined App: Image/MD->PDF Layout + AI-Powered Tools π"
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}
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)
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# --- Secrets Management ---
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try:
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secrets = toml.load(".streamlit/secrets.toml") if os.path.exists(".streamlit/secrets.toml") else {}
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HF_TOKEN = secrets.get("HF_TOKEN", os.getenv("HF_TOKEN", ""))
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OPENAI_API_KEY = secrets.get("OPENAI_API_KEY", os.getenv("OPENAI_API_KEY", ""))
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except Exception as e:
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st.error(f"Error loading secrets: {e}")
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HF_TOKEN = os.getenv("HF_TOKEN", "")
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
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if not HF_TOKEN:
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st.sidebar.warning("Hugging Face token not found in secrets or environment. Some features may be limited.")
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if not OPENAI_API_KEY and _openai_available:
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st.sidebar.warning("OpenAI API key not found in secrets or environment. OpenAI features disabled.")
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# --- Logging Setup ---
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84 |
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logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
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logger = logging.getLogger(__name__)
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log_records = []
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class LogCaptureHandler(logging.Handler):
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def emit(self, record):
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log_records.append(record)
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logger.addHandler(LogCaptureHandler())
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# --- Model Initialization ---
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DEFAULT_PROVIDER = "hf-inference"
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FEATURED_MODELS_LIST = [
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"meta-llama/Meta-Llama-3.1-8B-Instruct",
|
96 |
+
"mistralai/Mistral-7B-Instruct-v0.3",
|
97 |
+
"google/gemma-2-9b-it",
|
98 |
+
"Qwen/Qwen2-7B-Instruct",
|
99 |
+
"microsoft/Phi-3-mini-4k-instruct",
|
100 |
+
"HuggingFaceH4/zephyr-7b-beta",
|
101 |
+
"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
|
102 |
+
"HuggingFaceTB/SmolLM-1.7B-Instruct"
|
103 |
+
]
|
104 |
+
VISION_MODELS_LIST = [
|
105 |
+
"Salesforce/blip-image-captioning-large",
|
106 |
+
"microsoft/trocr-large-handwritten",
|
107 |
+
"llava-hf/llava-1.5-7b-hf",
|
108 |
+
"google/vit-base-patch16-224"
|
109 |
+
]
|
110 |
+
DIFFUSION_MODELS_LIST = [
|
111 |
+
"stabilityai/stable-diffusion-xl-base-1.0",
|
112 |
+
"runwayml/stable-diffusion-v1-5",
|
113 |
+
"OFA-Sys/small-stable-diffusion-v0"
|
114 |
+
]
|
115 |
+
OPENAI_MODELS_LIST = [
|
116 |
+
"gpt-4o",
|
117 |
+
"gpt-4-turbo",
|
118 |
+
"gpt-3.5-turbo",
|
119 |
+
"text-davinci-003"
|
120 |
+
]
|
121 |
+
st.session_state.setdefault('local_models', {})
|
122 |
+
st.session_state.setdefault('hf_inference_client', None)
|
123 |
+
st.session_state.setdefault('openai_client', None)
|
124 |
+
if _openai_available and OPENAI_API_KEY:
|
125 |
+
try:
|
126 |
+
st.session_state['openai_client'] = OpenAI(api_key=OPENAI_API_KEY)
|
127 |
+
logger.info("OpenAI client initialized successfully.")
|
128 |
+
except Exception as e:
|
129 |
+
st.error(f"Failed to initialize OpenAI client: {e}")
|
130 |
+
logger.error(f"OpenAI client initialization failed: {e}")
|
131 |
+
st.session_state['openai_client'] = None
|
132 |
+
|
133 |
+
# --- Session State Initialization ---
|
134 |
+
st.session_state.setdefault('layout_snapshots', [])
|
135 |
+
st.session_state.setdefault('layout_new_uploads', [])
|
136 |
+
st.session_state.setdefault('history', [])
|
137 |
+
st.session_state.setdefault('processing', {})
|
138 |
+
st.session_state.setdefault('asset_checkboxes', {'image': {}, 'md': {}, 'pdf': {}})
|
139 |
+
st.session_state.setdefault('downloaded_pdfs', {})
|
140 |
+
st.session_state.setdefault('unique_counter', 0)
|
141 |
+
st.session_state.setdefault('cam0_file', None)
|
142 |
+
st.session_state.setdefault('cam1_file', None)
|
143 |
+
st.session_state.setdefault('characters', [])
|
144 |
+
st.session_state.setdefault('char_form_reset_key', 0)
|
145 |
+
st.session_state.setdefault('gallery_size', 10)
|
146 |
+
st.session_state.setdefault('hf_provider', DEFAULT_PROVIDER)
|
147 |
+
st.session_state.setdefault('hf_custom_key', "")
|
148 |
+
st.session_state.setdefault('hf_selected_api_model', FEATURED_MODELS_LIST[0])
|
149 |
+
st.session_state.setdefault('hf_custom_api_model', "")
|
150 |
+
st.session_state.setdefault('openai_selected_model', OPENAI_MODELS_LIST[0] if _openai_available else "")
|
151 |
+
st.session_state.setdefault('selected_local_model_path', None)
|
152 |
+
st.session_state.setdefault('gen_max_tokens', 512)
|
153 |
+
st.session_state.setdefault('gen_temperature', 0.7)
|
154 |
+
st.session_state.setdefault('gen_top_p', 0.95)
|
155 |
+
st.session_state.setdefault('gen_frequency_penalty', 0.0)
|
156 |
+
if 'asset_gallery_container' not in st.session_state:
|
157 |
+
st.session_state['asset_gallery_container'] = {'image': st.sidebar.empty(), 'md': st.sidebar.empty(), 'pdf': st.sidebar.empty()}
|
158 |
+
|
159 |
+
# --- Dataclasses ---
|
160 |
+
@dataclass
|
161 |
+
class LocalModelConfig:
|
162 |
+
name: str
|
163 |
+
hf_id: str
|
164 |
+
model_type: str
|
165 |
+
size_category: str = "unknown"
|
166 |
+
domain: Optional[str] = None
|
167 |
+
local_path: str = field(init=False)
|
168 |
+
def __post_init__(self):
|
169 |
+
type_folder = f"{self.model_type}_models"
|
170 |
+
safe_name = re.sub(r'[^\w\-]+', '_', self.name)
|
171 |
+
self.local_path = os.path.join(type_folder, safe_name)
|
172 |
+
def get_full_path(self):
|
173 |
+
return os.path.abspath(self.local_path)
|
174 |
+
|
175 |
+
@dataclass
|
176 |
+
class DiffusionConfig:
|
177 |
+
name: str
|
178 |
+
base_model: str
|
179 |
+
size: str
|
180 |
+
domain: Optional[str] = None
|
181 |
+
@property
|
182 |
+
def model_path(self):
|
183 |
+
return f"diffusion_models/{self.name}"
|
184 |
+
|
185 |
# --- Helper Functions ---
|
186 |
+
def generate_filename(sequence, ext="png"):
|
187 |
+
timestamp = time.strftime('%Y%m%d_%H%M%S')
|
188 |
+
safe_sequence = re.sub(r'[^\w\-]+', '_', str(sequence))
|
189 |
+
return f"{safe_sequence}_{timestamp}.{ext}"
|
190 |
+
|
191 |
+
def pdf_url_to_filename(url):
|
192 |
+
name = re.sub(r'^https?://', '', url)
|
193 |
+
name = re.sub(r'[<>:"/\\|?*]', '_', name)
|
194 |
+
return name[:100] + ".pdf"
|
195 |
+
|
196 |
+
def get_download_link(file_path, mime_type="application/octet-stream", label="Download"):
|
197 |
+
if not os.path.exists(file_path):
|
198 |
+
return f"{label} (File not found)"
|
199 |
+
try:
|
200 |
+
with open(file_path, "rb") as f:
|
201 |
+
file_bytes = f.read()
|
202 |
+
b64 = base64.b64encode(file_bytes).decode()
|
203 |
+
return f'<a href="data:{mime_type};base64,{b64}" download="{os.path.basename(file_path)}">{label}</a>'
|
204 |
+
except Exception as e:
|
205 |
+
logger.error(f"Error creating download link for {file_path}: {e}")
|
206 |
+
return f"{label} (Error)"
|
207 |
+
|
208 |
+
def zip_directory(directory_path, zip_path):
|
209 |
+
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
|
210 |
+
for root, _, files in os.walk(directory_path):
|
211 |
+
for file in files:
|
212 |
+
file_path = os.path.join(root, file)
|
213 |
+
zipf.write(file_path, os.path.relpath(file_path, os.path.dirname(directory_path)))
|
214 |
+
|
215 |
+
def get_local_model_paths(model_type="causal"):
|
216 |
+
pattern = f"{model_type}_models/*"
|
217 |
+
dirs = [d for d in glob.glob(pattern) if os.path.isdir(d)]
|
218 |
+
return dirs
|
219 |
+
|
220 |
+
def get_gallery_files(file_types=("png", "pdf", "jpg", "jpeg", "md", "txt")):
|
221 |
+
all_files = set()
|
222 |
+
for ext in file_types:
|
223 |
+
all_files.update(glob.glob(f"*.{ext.lower()}"))
|
224 |
+
all_files.update(glob.glob(f"*.{ext.upper()}"))
|
225 |
+
return sorted([f for f in all_files if os.path.basename(f).lower() != 'readme.md'])
|
226 |
+
|
227 |
+
def get_typed_gallery_files(file_type):
|
228 |
+
if file_type == 'image':
|
229 |
+
return get_gallery_files(('png', 'jpg', 'jpeg'))
|
230 |
+
elif file_type == 'md':
|
231 |
+
return get_gallery_files(('md',))
|
232 |
+
elif file_type == 'pdf':
|
233 |
+
return get_gallery_files(('pdf',))
|
234 |
+
return []
|
235 |
+
|
236 |
+
def download_pdf(url, output_path):
|
237 |
+
try:
|
238 |
+
headers = {'User-Agent': 'Mozilla/5.0'}
|
239 |
+
response = requests.get(url, stream=True, timeout=20, headers=headers)
|
240 |
+
response.raise_for_status()
|
241 |
+
with open(output_path, "wb") as f:
|
242 |
+
for chunk in response.iter_content(chunk_size=8192):
|
243 |
+
f.write(chunk)
|
244 |
+
logger.info(f"Successfully downloaded {url} to {output_path}")
|
245 |
+
return True
|
246 |
+
except requests.exceptions.RequestException as e:
|
247 |
+
logger.error(f"Failed to download {url}: {e}")
|
248 |
+
if os.path.exists(output_path):
|
249 |
+
try:
|
250 |
+
os.remove(output_path)
|
251 |
+
except:
|
252 |
+
pass
|
253 |
+
return False
|
254 |
+
except Exception as e:
|
255 |
+
logger.error(f"An unexpected error occurred during download of {url}: {e}")
|
256 |
+
if os.path.exists(output_path):
|
257 |
+
try:
|
258 |
+
os.remove(output_path)
|
259 |
+
except:
|
260 |
+
pass
|
261 |
+
return False
|
262 |
+
|
263 |
+
async def process_pdf_snapshot(pdf_path, mode="single", resolution_factor=2.0):
|
264 |
+
start_time = time.time()
|
265 |
+
status_placeholder = st.empty()
|
266 |
+
status_placeholder.text(f"Processing PDF Snapshot ({mode}, Res: {resolution_factor}x)... (0s)")
|
267 |
+
output_files = []
|
268 |
+
try:
|
269 |
+
doc = fitz.open(pdf_path)
|
270 |
+
matrix = fitz.Matrix(resolution_factor, resolution_factor)
|
271 |
+
num_pages_to_process = min(1, len(doc)) if mode == "single" else min(2, len(doc)) if mode == "twopage" else len(doc)
|
272 |
+
for i in range(num_pages_to_process):
|
273 |
+
page_start_time = time.time()
|
274 |
+
page = doc[i]
|
275 |
+
pix = page.get_pixmap(matrix=matrix)
|
276 |
+
base_name = os.path.splitext(os.path.basename(pdf_path))[0]
|
277 |
+
output_file = generate_filename(f"{base_name}_pg{i+1}_{mode}", "png")
|
278 |
+
await asyncio.to_thread(pix.save, output_file)
|
279 |
+
output_files.append(output_file)
|
280 |
+
elapsed_page = int(time.time() - page_start_time)
|
281 |
+
status_placeholder.text(f"Processing PDF Snapshot ({mode}, Res: {resolution_factor}x)... Page {i+1}/{num_pages_to_process} done ({elapsed_page}s)")
|
282 |
+
await asyncio.sleep(0.01)
|
283 |
+
doc.close()
|
284 |
+
elapsed = int(time.time() - start_time)
|
285 |
+
status_placeholder.success(f"PDF Snapshot ({mode}, {len(output_files)} files) completed in {elapsed}s!")
|
286 |
+
return output_files
|
287 |
+
except Exception as e:
|
288 |
+
logger.error(f"Failed to process PDF snapshot for {pdf_path}: {e}")
|
289 |
+
status_placeholder.error(f"Failed to process PDF {os.path.basename(pdf_path)}: {e}")
|
290 |
+
for f in output_files:
|
291 |
+
if os.path.exists(f):
|
292 |
+
os.remove(f)
|
293 |
+
return []
|
294 |
+
|
295 |
+
def get_hf_client() -> Optional[InferenceClient]:
|
296 |
+
provider = st.session_state.hf_provider
|
297 |
+
custom_key = st.session_state.hf_custom_key.strip()
|
298 |
+
token_to_use = custom_key if custom_key else HF_TOKEN
|
299 |
+
if not token_to_use and provider != "hf-inference":
|
300 |
+
st.error(f"Provider '{provider}' requires a Hugging Face API token.")
|
301 |
+
return None
|
302 |
+
if provider == "hf-inference" and not token_to_use:
|
303 |
+
logger.warning("Using hf-inference provider without a token. Rate limits may apply.")
|
304 |
+
token_to_use = None
|
305 |
+
current_client = st.session_state.get('hf_inference_client')
|
306 |
+
needs_reinit = True
|
307 |
+
if current_client:
|
308 |
+
client_uses_custom = hasattr(current_client, '_token') and current_client._token == custom_key
|
309 |
+
client_uses_default = hasattr(current_client, '_token') and current_client._token == HF_TOKEN
|
310 |
+
client_uses_no_token = not hasattr(current_client, '_token') or current_client._token is None
|
311 |
+
if current_client.provider == provider:
|
312 |
+
if custom_key and client_uses_custom:
|
313 |
+
needs_reinit = False
|
314 |
+
elif not custom_key and HF_TOKEN and client_uses_default:
|
315 |
+
needs_reinit = False
|
316 |
+
elif not custom_key and not HF_TOKEN and client_uses_no_token:
|
317 |
+
needs_reinit = False
|
318 |
+
if needs_reinit:
|
319 |
+
try:
|
320 |
+
logger.info(f"Initializing InferenceClient for provider: {provider}.")
|
321 |
+
st.session_state.hf_inference_client = InferenceClient(token=token_to_use, provider=provider)
|
322 |
+
logger.info("InferenceClient initialized successfully.")
|
323 |
+
except Exception as e:
|
324 |
+
st.error(f"Failed to initialize Hugging Face client: {e}")
|
325 |
+
logger.error(f"InferenceClient initialization failed: {e}")
|
326 |
+
st.session_state.hf_inference_client = None
|
327 |
+
return st.session_state.hf_inference_client
|
328 |
+
|
329 |
+
def process_text_hf(text: str, prompt: str, use_api: bool, model_id: str = None) -> str:
|
330 |
+
status_placeholder = st.empty()
|
331 |
+
start_time = time.time()
|
332 |
+
result_text = ""
|
333 |
+
params = {
|
334 |
+
"max_new_tokens": st.session_state.gen_max_tokens,
|
335 |
+
"temperature": st.session_state.gen_temperature,
|
336 |
+
"top_p": st.session_state.gen_top_p,
|
337 |
+
"repetition_penalty": st.session_state.gen_frequency_penalty + 1.0,
|
338 |
+
}
|
339 |
+
seed = st.session_state.gen_seed
|
340 |
+
if seed != -1:
|
341 |
+
params["seed"] = seed
|
342 |
+
system_prompt = "You are a helpful assistant. Process the following text based on the user's request."
|
343 |
+
full_prompt = f"{prompt}\n\n---\n\n{text}"
|
344 |
+
messages = [
|
345 |
+
{"role": "system", "content": system_prompt},
|
346 |
+
{"role": "user", "content": full_prompt}
|
347 |
+
]
|
348 |
+
if use_api:
|
349 |
+
status_placeholder.info("Processing text using Hugging Face API...")
|
350 |
+
client = get_hf_client()
|
351 |
+
if not client:
|
352 |
+
return "Error: Hugging Face client not available."
|
353 |
+
model_id = model_id or st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model
|
354 |
+
status_placeholder.info(f"Using API Model: {model_id}")
|
355 |
+
try:
|
356 |
+
response = client.chat_completion(
|
357 |
+
model=model_id,
|
358 |
+
messages=messages,
|
359 |
+
max_tokens=params['max_new_tokens'],
|
360 |
+
temperature=params['temperature'],
|
361 |
+
top_p=params['top_p'],
|
362 |
+
)
|
363 |
+
result_text = response.choices[0].message.content or ""
|
364 |
+
logger.info(f"HF API text processing successful for model {model_id}.")
|
365 |
+
except Exception as e:
|
366 |
+
logger.error(f"HF API text processing failed for model {model_id}: {e}")
|
367 |
+
result_text = f"Error during Hugging Face API inference: {str(e)}"
|
368 |
+
else:
|
369 |
+
status_placeholder.info("Processing text using local model...")
|
370 |
+
if not _transformers_available:
|
371 |
+
return "Error: Transformers library not available."
|
372 |
+
model_path = st.session_state.get('selected_local_model_path')
|
373 |
+
if not model_path or model_path not in st.session_state.get('local_models', {}):
|
374 |
+
return "Error: No suitable local model selected."
|
375 |
+
local_model_data = st.session_state['local_models'][model_path]
|
376 |
+
if local_model_data.get('type') != 'causal':
|
377 |
+
return f"Error: Loaded model '{os.path.basename(model_path)}' is not a Causal LM."
|
378 |
+
status_placeholder.info(f"Using Local Model: {os.path.basename(model_path)}")
|
379 |
+
model = local_model_data.get('model')
|
380 |
+
tokenizer = local_model_data.get('tokenizer')
|
381 |
+
if not model or not tokenizer:
|
382 |
+
return f"Error: Model or tokenizer not found for {os.path.basename(model_path)}."
|
383 |
+
try:
|
384 |
+
try:
|
385 |
+
prompt_for_model = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
386 |
+
except Exception:
|
387 |
+
logger.warning(f"Could not apply chat template for {model_path}. Using basic formatting.")
|
388 |
+
prompt_for_model = f"System: {system_prompt}\nUser: {full_prompt}\nAssistant:"
|
389 |
+
inputs = tokenizer(prompt_for_model, return_tensors="pt", padding=True, truncation=True, max_length=params['max_new_tokens'] * 2)
|
390 |
+
inputs = {k: v.to(model.device) for k, v in inputs.items()}
|
391 |
+
generate_params = {
|
392 |
+
"max_new_tokens": params['max_new_tokens'],
|
393 |
+
"temperature": params['temperature'],
|
394 |
+
"top_p": params['top_p'],
|
395 |
+
"repetition_penalty": params.get('repetition_penalty', 1.0),
|
396 |
+
"do_sample": True if params['temperature'] > 0.1 else False,
|
397 |
+
"pad_token_id": tokenizer.eos_token_id
|
398 |
+
}
|
399 |
+
with torch.no_grad():
|
400 |
+
outputs = model.generate(**inputs, **generate_params)
|
401 |
+
input_length = inputs['input_ids'].shape[1]
|
402 |
+
generated_ids = outputs[0][input_length:]
|
403 |
+
result_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
|
404 |
+
logger.info(f"Local text processing successful for model {model_path}.")
|
405 |
+
except Exception as e:
|
406 |
+
logger.error(f"Local text processing failed for model {model_path}: {e}")
|
407 |
+
result_text = f"Error during local model inference: {str(e)}"
|
408 |
+
elapsed = int(time.time() - start_time)
|
409 |
+
status_placeholder.success(f"Text processing completed in {elapsed}s.")
|
410 |
+
return result_text
|
411 |
+
|
412 |
+
def process_text_openai(text: str, prompt: str, model_id: str) -> str:
|
413 |
+
if not _openai_available or not st.session_state.get('openai_client'):
|
414 |
+
return "Error: OpenAI client not available or API key missing."
|
415 |
+
status_placeholder = st.empty()
|
416 |
+
start_time = time.time()
|
417 |
+
client = st.session_state['openai_client']
|
418 |
+
system_prompt = "You are a helpful assistant. Process the following text based on the user's request."
|
419 |
+
full_prompt = f"{prompt}\n\n---\n\n{text}"
|
420 |
+
messages = [
|
421 |
+
{"role": "system", "content": system_prompt},
|
422 |
+
{"role": "user", "content": full_prompt}
|
423 |
+
]
|
424 |
+
status_placeholder.info(f"Processing text using OpenAI model: {model_id}...")
|
425 |
+
try:
|
426 |
+
response = client.chat.completions.create(
|
427 |
+
model=model_id,
|
428 |
+
messages=messages,
|
429 |
+
max_tokens=st.session_state.gen_max_tokens,
|
430 |
+
temperature=st.session_state.gen_temperature,
|
431 |
+
top_p=st.session_state.gen_top_p,
|
432 |
+
)
|
433 |
+
result_text = response.choices[0].message.content or ""
|
434 |
+
logger.info(f"OpenAI text processing successful for model {model_id}.")
|
435 |
+
except Exception as e:
|
436 |
+
logger.error(f"OpenAI text processing failed for model {model_id}: {e}")
|
437 |
+
result_text = f"Error during OpenAI inference: {str(e)}"
|
438 |
+
elapsed = int(time.time() - start_time)
|
439 |
+
status_placeholder.success(f"Text processing completed in {elapsed}s.")
|
440 |
+
return result_text
|
441 |
+
|
442 |
+
def process_image_hf(image: Image.Image, prompt: str, use_api: bool, model_id: str = None) -> str:
|
443 |
+
status_placeholder = st.empty()
|
444 |
+
start_time = time.time()
|
445 |
+
result_text = ""
|
446 |
+
if use_api:
|
447 |
+
status_placeholder.info("Processing image using Hugging Face API...")
|
448 |
+
client = get_hf_client()
|
449 |
+
if not client:
|
450 |
+
return "Error: HF client not configured."
|
451 |
+
buffered = BytesIO()
|
452 |
+
image.save(buffered, format="PNG" if image.format != 'JPEG' else 'JPEG')
|
453 |
+
img_bytes = buffered.getvalue()
|
454 |
+
model_id = model_id or "Salesforce/blip-image-captioning-large"
|
455 |
+
status_placeholder.info(f"Using API Image-to-Text Model: {model_id}")
|
456 |
+
try:
|
457 |
+
response_list = client.image_to_text(data=img_bytes, model=model_id)
|
458 |
+
if response_list and isinstance(response_list, list) and 'generated_text' in response_list[0]:
|
459 |
+
result_text = response_list[0]['generated_text']
|
460 |
+
logger.info(f"HF API image captioning successful for model {model_id}.")
|
461 |
+
else:
|
462 |
+
result_text = "Error: Unexpected response format from image-to-text API."
|
463 |
+
logger.warning(f"Unexpected API response for image-to-text: {response_list}")
|
464 |
+
except Exception as e:
|
465 |
+
logger.error(f"HF API image processing failed: {e}")
|
466 |
+
result_text = f"Error during Hugging Face API image inference: {str(e)}"
|
467 |
+
else:
|
468 |
+
status_placeholder.info("Processing image using local model...")
|
469 |
+
if not _transformers_available:
|
470 |
+
return "Error: Transformers library needed."
|
471 |
+
model_path = st.session_state.get('selected_local_model_path')
|
472 |
+
if not model_path or model_path not in st.session_state.get('local_models', {}):
|
473 |
+
return "Error: No suitable local model selected."
|
474 |
+
local_model_data = st.session_state['local_models'][model_path]
|
475 |
+
model_type = local_model_data.get('type')
|
476 |
+
if model_type == 'vision':
|
477 |
+
processor = local_model_data.get('processor')
|
478 |
+
model = local_model_data.get('model')
|
479 |
+
if processor and model:
|
480 |
+
try:
|
481 |
+
inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
|
482 |
+
generated_ids = model.generate(**inputs, max_new_tokens=st.session_state.gen_max_tokens)
|
483 |
+
result_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
|
484 |
+
except Exception as e:
|
485 |
+
result_text = f"Error during local vision model inference: {e}"
|
486 |
+
else:
|
487 |
+
result_text = "Error: Processor or model missing for local vision task."
|
488 |
+
elif model_type == 'ocr':
|
489 |
+
processor = local_model_data.get('processor')
|
490 |
+
model = local_model_data.get('model')
|
491 |
+
if processor and model:
|
492 |
+
try:
|
493 |
+
pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(model.device)
|
494 |
+
generated_ids = model.generate(pixel_values, max_new_tokens=st.session_state.gen_max_tokens)
|
495 |
+
result_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
496 |
+
except Exception as e:
|
497 |
+
result_text = f"Error during local OCR model inference: {e}"
|
498 |
+
else:
|
499 |
+
result_text = "Error: Processor or model missing for local OCR task."
|
500 |
+
else:
|
501 |
+
result_text = f"Error: Loaded model '{os.path.basename(model_path)}' is not a recognized vision/OCR type."
|
502 |
+
elapsed = int(time.time() - start_time)
|
503 |
+
status_placeholder.success(f"Image processing completed in {elapsed}s.")
|
504 |
+
return result_text
|
505 |
+
|
506 |
+
def process_image_openai(image: Image.Image, prompt: str, model_id: str = "gpt-4o") -> str:
|
507 |
+
if not _openai_available or not st.session_state.get('openai_client'):
|
508 |
+
return "Error: OpenAI client not available or API key missing."
|
509 |
+
status_placeholder = st.empty()
|
510 |
+
start_time = time.time()
|
511 |
+
client = st.session_state['openai_client']
|
512 |
+
buffered = BytesIO()
|
513 |
+
image.save(buffered, format="PNG")
|
514 |
+
img_b64 = base64.b64encode(buffered.getvalue()).decode()
|
515 |
+
status_placeholder.info(f"Processing image using OpenAI model: {model_id}...")
|
516 |
+
try:
|
517 |
+
response = client.chat.completions.create(
|
518 |
+
model=model_id,
|
519 |
+
messages=[
|
520 |
+
{"role": "user", "content": [
|
521 |
+
{"type": "text", "text": prompt},
|
522 |
+
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_b64}"}}
|
523 |
+
]}
|
524 |
+
],
|
525 |
+
max_tokens=st.session_state.gen_max_tokens,
|
526 |
+
temperature=st.session_state.gen_temperature,
|
527 |
+
)
|
528 |
+
result_text = response.choices[0].message.content or ""
|
529 |
+
logger.info(f"OpenAI image processing successful for model {model_id}.")
|
530 |
+
except Exception as e:
|
531 |
+
logger.error(f"OpenAI image processing failed for model {model_id}: {e}")
|
532 |
+
result_text = f"Error during OpenAI image inference: {str(e)}"
|
533 |
+
elapsed = int(time.time() - start_time)
|
534 |
+
status_placeholder.success(f"Image processing completed in {elapsed}s.")
|
535 |
+
return result_text
|
536 |
+
|
537 |
+
async def process_hf_ocr(image: Image.Image, output_file: str, use_api: bool, model_id: str = None) -> str:
|
538 |
+
ocr_prompt = "Extract text content from this image."
|
539 |
+
result = process_image_hf(image, ocr_prompt, use_api, model_id=model_id or "microsoft/trocr-large-handwritten")
|
540 |
+
if result and not result.startswith("Error") and not result.startswith("["):
|
541 |
+
try:
|
542 |
+
async with aiofiles.open(output_file, "w", encoding='utf-8') as f:
|
543 |
+
await f.write(result)
|
544 |
+
logger.info(f"HF OCR result saved to {output_file}")
|
545 |
+
except IOError as e:
|
546 |
+
logger.error(f"Failed to save HF OCR output to {output_file}: {e}")
|
547 |
+
result += f"\n[Error saving file: {e}]"
|
548 |
+
elif os.path.exists(output_file):
|
549 |
+
try:
|
550 |
+
os.remove(output_file)
|
551 |
+
except OSError:
|
552 |
+
pass
|
553 |
+
return result
|
554 |
+
|
555 |
+
async def process_openai_ocr(image: Image.Image, output_file: str, model_id: str = "gpt-4o") -> str:
|
556 |
+
ocr_prompt = "Extract text content from this image."
|
557 |
+
result = process_image_openai(image, ocr_prompt, model_id)
|
558 |
+
if result and not result.startswith("Error"):
|
559 |
+
try:
|
560 |
+
async with aiofiles.open(output_file, "w", encoding='utf-8') as f:
|
561 |
+
await f.write(result)
|
562 |
+
logger.info(f"OpenAI OCR result saved to {output_file}")
|
563 |
+
except IOError as e:
|
564 |
+
logger.error(f"Failed to save OpenAI OCR output to {output_file}: {e}")
|
565 |
+
result += f"\n[Error saving file: {e}]"
|
566 |
+
elif os.path.exists(output_file):
|
567 |
+
try:
|
568 |
+
os.remove(output_file)
|
569 |
+
except OSError:
|
570 |
+
pass
|
571 |
+
return result
|
572 |
+
|
573 |
+
def randomize_character_content():
|
574 |
+
intro_templates = [
|
575 |
+
"{char} is a valiant knight...", "{char} is a mischievous thief...",
|
576 |
+
"{char} is a wise scholar...", "{char} is a fiery warrior...", "{char} is a gentle healer..."
|
577 |
+
]
|
578 |
+
greeting_templates = [
|
579 |
+
"'I am from the knight's guild...'", "'I heard you needed helpβnameβs {char}...",
|
580 |
+
"'Oh, hello! IοΏ½οΏ½m {char}, didnβt see you there...'", "'Iβm {char}, and Iβm here to fight...'",
|
581 |
+
"'Iβm {char}, here to heal...'"
|
582 |
+
]
|
583 |
+
name = f"Character_{random.randint(1000, 9999)}"
|
584 |
+
gender = random.choice(["Male", "Female"])
|
585 |
+
intro = random.choice(intro_templates).format(char=name)
|
586 |
+
greeting = random.choice(greeting_templates).format(char=name)
|
587 |
+
return name, gender, intro, greeting
|
588 |
+
|
589 |
+
def save_character(character_data):
|
590 |
+
characters = st.session_state.get('characters', [])
|
591 |
+
if any(c['name'] == character_data['name'] for c in characters):
|
592 |
+
st.error(f"Character name '{character_data['name']}' already exists.")
|
593 |
+
return False
|
594 |
+
characters.append(character_data)
|
595 |
+
st.session_state['characters'] = characters
|
596 |
+
try:
|
597 |
+
with open("characters.json", "w", encoding='utf-8') as f:
|
598 |
+
json.dump(characters, f, indent=2)
|
599 |
+
logger.info(f"Saved character: {character_data['name']}")
|
600 |
+
return True
|
601 |
+
except IOError as e:
|
602 |
+
logger.error(f"Failed to save characters.json: {e}")
|
603 |
+
st.error(f"Failed to save character file: {e}")
|
604 |
+
return False
|
605 |
+
|
606 |
+
def load_characters():
|
607 |
+
if not os.path.exists("characters.json"):
|
608 |
+
st.session_state['characters'] = []
|
609 |
+
return
|
610 |
+
try:
|
611 |
+
with open("characters.json", "r", encoding='utf-8') as f:
|
612 |
+
characters = json.load(f)
|
613 |
+
if isinstance(characters, list):
|
614 |
+
st.session_state['characters'] = characters
|
615 |
+
logger.info(f"Loaded {len(characters)} characters.")
|
616 |
+
else:
|
617 |
+
st.session_state['characters'] = []
|
618 |
+
logger.warning("characters.json is not a list, resetting.")
|
619 |
+
os.remove("characters.json")
|
620 |
+
except (json.JSONDecodeError, IOError) as e:
|
621 |
+
logger.error(f"Failed to load or decode characters.json: {e}")
|
622 |
+
st.error(f"Error loading character file: {e}. Starting fresh.")
|
623 |
+
st.session_state['characters'] = []
|
624 |
+
try:
|
625 |
+
corrupt_filename = f"characters_corrupt_{int(time.time())}.json"
|
626 |
+
shutil.copy("characters.json", corrupt_filename)
|
627 |
+
logger.info(f"Backed up corrupted character file to {corrupt_filename}")
|
628 |
+
os.remove("characters.json")
|
629 |
+
except Exception as backup_e:
|
630 |
+
logger.error(f"Could not backup corrupted character file: {backup_e}")
|
631 |
+
|
632 |
+
def clean_stem(fn: str) -> str:
|
633 |
+
name = os.path.splitext(os.path.basename(fn))[0]
|
634 |
+
name = name.replace('-', ' ').replace('_', ' ')
|
635 |
+
return name.strip().title()
|
636 |
+
|
637 |
+
def make_image_sized_pdf(sources, is_markdown_flags):
|
638 |
+
if not sources:
|
639 |
+
st.warning("No sources provided for PDF generation.")
|
640 |
+
return None
|
641 |
+
buf = BytesIO()
|
642 |
+
styles = getSampleStyleSheet()
|
643 |
+
md_style = ParagraphStyle(
|
644 |
+
name='Markdown',
|
645 |
+
fontSize=10,
|
646 |
+
leading=12,
|
647 |
+
spaceAfter=6,
|
648 |
+
alignment=TA_JUSTIFY,
|
649 |
+
fontName='Helvetica'
|
650 |
+
)
|
651 |
+
doc = SimpleDocTemplate(buf, pagesize=letter, rightMargin=36, leftMargin=36, topMargin=36, bottomMargin=36)
|
652 |
+
story = []
|
653 |
+
try:
|
654 |
+
for idx, (src, is_md) in enumerate(zip(sources, is_markdown_flags), start=1):
|
655 |
+
status_placeholder = st.empty()
|
656 |
+
filename = 'page_' + str(idx)
|
657 |
+
status_placeholder.info(f"Adding page {idx}/{len(sources)}: {os.path.basename(str(src))}...")
|
658 |
+
try:
|
659 |
+
if is_md:
|
660 |
+
with open(src, 'r', encoding='utf-8') as f:
|
661 |
+
content = f.read()
|
662 |
+
content = re.sub(r'!\[.*?\]\(.*?\)', '', content)
|
663 |
+
paragraphs = content.split('\n\n')
|
664 |
+
for para in paragraphs:
|
665 |
+
if para.strip():
|
666 |
+
story.append(Paragraph(para.strip(), md_style))
|
667 |
+
story.append(PageBreak())
|
668 |
+
status_placeholder.success(f"Added markdown page {idx}/{len(sources)}: {filename}")
|
669 |
+
else:
|
670 |
+
if isinstance(src, str):
|
671 |
+
if not os.path.exists(src):
|
672 |
+
logger.warning(f"Image file not found: {src}. Skipping.")
|
673 |
+
status_placeholder.warning(f"Skipping missing file: {os.path.basename(src)}")
|
674 |
+
continue
|
675 |
+
img_obj = Image.open(src)
|
676 |
+
filename = os.path.basename(src)
|
677 |
+
else:
|
678 |
+
src.seek(0)
|
679 |
+
img_obj = Image.open(src)
|
680 |
+
filename = getattr(src, 'name', f'uploaded_image_{idx}')
|
681 |
+
src.seek(0)
|
682 |
+
with img_obj:
|
683 |
+
iw, ih = img_obj.size
|
684 |
+
if iw <= 0 or ih <= 0:
|
685 |
+
logger.warning(f"Invalid image dimensions ({iw}x{ih}) for {filename}. Skipping.")
|
686 |
+
status_placeholder.warning(f"Skipping invalid image: {filename}")
|
687 |
+
continue
|
688 |
+
cap_h = 30
|
689 |
+
c = canvas.Canvas(BytesIO(), pagesize=(iw, ih + cap_h))
|
690 |
+
img_reader = ImageReader(img_obj)
|
691 |
+
c.drawImage(img_reader, 0, cap_h, width=iw, height=ih, preserveAspectRatio=True, anchor='c', mask='auto')
|
692 |
+
caption = clean_stem(filename)
|
693 |
+
c.setFont('Helvetica', 12)
|
694 |
+
c.setFillColorRGB(0, 0, 0)
|
695 |
+
c.drawCentredString(iw / 2, cap_h / 2 + 3, caption)
|
696 |
+
c.setFont('Helvetica', 8)
|
697 |
+
c.setFillColorRGB(0.5, 0.5, 0.5)
|
698 |
+
c.drawRightString(iw - 10, 8, f"Page {idx}")
|
699 |
+
c.save()
|
700 |
+
story.append(PageBreak())
|
701 |
+
status_placeholder.success(f"Added image page {idx}/{len(sources)}: {filename}")
|
702 |
+
except Exception as e:
|
703 |
+
logger.error(f"Error processing source {src}: {e}")
|
704 |
+
status_placeholder.error(f"Error adding page {idx}: {e}")
|
705 |
+
doc.build(story)
|
706 |
+
buf.seek(0)
|
707 |
+
if buf.getbuffer().nbytes < 100:
|
708 |
+
st.error("PDF generation resulted in an empty file.")
|
709 |
+
return None
|
710 |
+
return buf.getvalue()
|
711 |
+
except Exception as e:
|
712 |
+
logger.error(f"Fatal error during PDF generation: {e}")
|
713 |
+
st.error(f"PDF Generation Failed: {e}")
|
714 |
+
return None
|
715 |
+
|
716 |
+
def update_gallery(gallery_type='image'):
|
717 |
+
container = st.session_state['asset_gallery_container'][gallery_type]
|
718 |
+
with container:
|
719 |
+
st.markdown(f"### {gallery_type.capitalize()} Gallery πΈ")
|
720 |
+
files = get_typed_gallery_files(gallery_type)
|
721 |
+
if not files:
|
722 |
+
st.info(f"No {gallery_type} assets found yet.")
|
723 |
+
return
|
724 |
+
st.caption(f"Found {len(files)} assets:")
|
725 |
+
for idx, file in enumerate(files[:st.session_state.gallery_size]):
|
726 |
+
st.session_state['unique_counter'] += 1
|
727 |
+
unique_id = st.session_state['unique_counter']
|
728 |
+
item_key_base = f"{gallery_type}_gallery_item_{os.path.basename(file)}_{unique_id}"
|
729 |
+
basename = os.path.basename(file)
|
730 |
+
st.markdown(f"**{basename}**")
|
731 |
+
try:
|
732 |
+
file_ext = os.path.splitext(file)[1].lower()
|
733 |
+
if gallery_type == 'image' and file_ext in ['.png', '.jpg', '.jpeg']:
|
734 |
+
with st.expander("Preview", expanded=False):
|
735 |
+
st.image(Image.open(file), use_container_width=True)
|
736 |
+
elif gallery_type == 'pdf' and file_ext == '.pdf':
|
737 |
+
with st.expander("Preview (Page 1)", expanded=False):
|
738 |
+
doc = fitz.open(file)
|
739 |
+
if len(doc) > 0:
|
740 |
+
pix = doc[0].get_pixmap(matrix=fitz.Matrix(0.5, 0.5))
|
741 |
+
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
|
742 |
+
st.image(img, use_container_width=True)
|
743 |
+
else:
|
744 |
+
st.warning("Empty PDF")
|
745 |
+
doc.close()
|
746 |
+
elif gallery_type == 'md' and file_ext == '.md':
|
747 |
+
with st.expander("Preview (Start)", expanded=False):
|
748 |
+
with open(file, 'r', encoding='utf-8', errors='ignore') as f:
|
749 |
+
content_preview = f.read(200)
|
750 |
+
st.code(content_preview + "...", language='markdown')
|
751 |
+
action_cols = st.columns(3)
|
752 |
+
with action_cols[0]:
|
753 |
+
checkbox_key = f"cb_{item_key_base}"
|
754 |
+
st.session_state['asset_checkboxes'][gallery_type][file] = st.checkbox(
|
755 |
+
"Select",
|
756 |
+
value=st.session_state['asset_checkboxes'][gallery_type].get(file, False),
|
757 |
+
key=checkbox_key
|
758 |
+
)
|
759 |
+
with action_cols[1]:
|
760 |
+
mime_map = {'.png': 'image/png', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', '.pdf': 'application/pdf', '.md': 'text/markdown'}
|
761 |
+
mime_type = mime_map.get(file_ext, "application/octet-stream")
|
762 |
+
dl_key = f"dl_{item_key_base}"
|
763 |
+
try:
|
764 |
+
with open(file, "rb") as fp:
|
765 |
+
st.download_button(
|
766 |
+
label="π₯",
|
767 |
+
data=fp,
|
768 |
+
file_name=basename,
|
769 |
+
mime=mime_type,
|
770 |
+
key=dl_key,
|
771 |
+
help="Download this file"
|
772 |
+
)
|
773 |
+
except Exception as dl_e:
|
774 |
+
st.error(f"Download Error: {dl_e}")
|
775 |
+
with action_cols[2]:
|
776 |
+
delete_key = f"del_{item_key_base}"
|
777 |
+
if st.button("ποΈ", key=delete_key, help=f"Delete {basename}"):
|
778 |
+
try:
|
779 |
+
os.remove(file)
|
780 |
+
st.session_state['asset_checkboxes'][gallery_type].pop(file, None)
|
781 |
+
if file in st.session_state.get('layout_snapshots', []):
|
782 |
+
st.session_state['layout_snapshots'].remove(file)
|
783 |
+
logger.info(f"Deleted {gallery_type} asset: {file}")
|
784 |
+
st.toast(f"Deleted {basename}!", icon="β
")
|
785 |
+
st.rerun()
|
786 |
+
except OSError as e:
|
787 |
+
logger.error(f"Error deleting file {file}: {e}")
|
788 |
+
st.error(f"Could not delete {basename}")
|
789 |
+
except Exception as e:
|
790 |
+
st.error(f"Error displaying {basename}: {e}")
|
791 |
+
logger.error(f"Error displaying asset {file}: {e}")
|
792 |
+
st.markdown("---")
|
793 |
+
|
794 |
+
# --- UI Elements ---
|
795 |
+
st.sidebar.subheader("π€ AI Settings")
|
796 |
+
with st.sidebar.expander("API Inference Settings", expanded=False):
|
797 |
+
st.session_state.hf_custom_key = st.text_input(
|
798 |
+
"Custom HF Token",
|
799 |
+
value=st.session_state.get('hf_custom_key', ""),
|
800 |
+
type="password",
|
801 |
+
key="hf_custom_key_input"
|
802 |
+
)
|
803 |
+
token_status = "Custom Key Set" if st.session_state.hf_custom_key else ("Default HF_TOKEN Set" if HF_TOKEN else "No Token Set")
|
804 |
+
st.caption(f"HF Token Status: {token_status}")
|
805 |
+
providers_list = ["hf-inference", "cerebras", "together", "sambanova", "novita", "cohere", "fireworks-ai", "hyperbolic", "nebius"]
|
806 |
+
st.session_state.hf_provider = st.selectbox(
|
807 |
+
"HF Inference Provider",
|
808 |
+
options=providers_list,
|
809 |
+
index=providers_list.index(st.session_state.get('hf_provider', DEFAULT_PROVIDER)),
|
810 |
+
key="hf_provider_select"
|
811 |
+
)
|
812 |
+
st.session_state.hf_custom_api_model = st.text_input(
|
813 |
+
"Custom HF API Model ID",
|
814 |
+
value=st.session_state.get('hf_custom_api_model', ""),
|
815 |
+
key="hf_custom_model_input"
|
816 |
+
)
|
817 |
+
effective_hf_model = st.session_state.hf_custom_api_model.strip() or st.session_state.hf_selected_api_model
|
818 |
+
st.session_state.hf_selected_api_model = st.selectbox(
|
819 |
+
"Featured HF API Model",
|
820 |
+
options=FEATURED_MODELS_LIST,
|
821 |
+
index=FEATURED_MODELS_LIST.index(st.session_state.get('hf_selected_api_model', FEATURED_MODELS_LIST[0])),
|
822 |
+
key="hf_featured_model_select"
|
823 |
+
)
|
824 |
+
st.caption(f"Effective HF API Model: {effective_hf_model}")
|
825 |
+
if _openai_available:
|
826 |
+
st.session_state.openai_selected_model = st.selectbox(
|
827 |
+
"OpenAI Model",
|
828 |
+
options=OPENAI_MODELS_LIST,
|
829 |
+
index=OPENAI_MODELS_LIST.index(st.session_state.get('openai_selected_model', OPENAI_MODELS_LIST[0])),
|
830 |
+
key="openai_model_select"
|
831 |
+
)
|
832 |
+
|
833 |
+
with st.sidebar.expander("Local Model Selection", expanded=True):
|
834 |
+
if not _transformers_available:
|
835 |
+
st.warning("Transformers library not found. Cannot load local models.")
|
836 |
+
else:
|
837 |
+
local_model_options = ["None"] + list(st.session_state.get('local_models', {}).keys())
|
838 |
+
current_selection = st.session_state.get('selected_local_model_path', "None")
|
839 |
+
if current_selection not in local_model_options:
|
840 |
+
current_selection = "None"
|
841 |
+
selected_path = st.selectbox(
|
842 |
+
"Active Local Model",
|
843 |
+
options=local_model_options,
|
844 |
+
index=local_model_options.index(current_selection),
|
845 |
+
format_func=lambda x: os.path.basename(x) if x != "None" else "None",
|
846 |
+
key="local_model_selector"
|
847 |
+
)
|
848 |
+
st.session_state.selected_local_model_path = selected_path if selected_path != "None" else None
|
849 |
+
if st.session_state.selected_local_model_path:
|
850 |
+
model_info = st.session_state.local_models[st.session_state.selected_local_model_path]
|
851 |
+
st.caption(f"Type: {model_info.get('type', 'Unknown')}")
|
852 |
+
st.caption(f"Device: {model_info.get('model').device if model_info.get('model') else 'N/A'}")
|
853 |
+
else:
|
854 |
+
st.caption("No local model selected.")
|
855 |
+
|
856 |
+
with st.sidebar.expander("Generation Parameters", expanded=False):
|
857 |
+
st.session_state.gen_max_tokens = st.slider("Max New Tokens", 1, 4096, st.session_state.get('gen_max_tokens', 512), key="param_max_tokens")
|
858 |
+
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")
|
859 |
+
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")
|
860 |
+
st.session_state.gen_frequency_penalty = st.slider("Repetition Penalty", 0.0, 1.0, st.session_state.get('gen_frequency_penalty', 0.0), step=0.05, key="param_repetition")
|
861 |
+
st.session_state.gen_seed = st.slider("Seed", -1, 65535, st.session_state.get('gen_seed', -1), step=1, key="param_seed")
|
862 |
+
|
863 |
+
st.sidebar.subheader("πΌοΈ Gallery Settings")
|
864 |
+
st.slider(
|
865 |
+
"Max Items Shown",
|
866 |
+
min_value=2,
|
867 |
+
max_value=50,
|
868 |
+
value=st.session_state.get('gallery_size', 10),
|
869 |
+
key="gallery_size_slider"
|
870 |
+
)
|
871 |
+
st.session_state.gallery_size = st.session_state.gallery_size_slider
|
872 |
+
st.sidebar.markdown("---")
|
873 |
+
update_gallery('image')
|
874 |
+
update_gallery('md')
|
875 |
+
update_gallery('pdf')
|
876 |
+
|
877 |
+
# --- Main Application ---
|
878 |
+
st.title("Vision & Layout Titans ππΌοΈπ")
|
879 |
+
st.markdown("Create PDFs from images and markdown, process with AI, and manage characters.")
|
880 |
+
tabs = st.tabs([
|
881 |
+
"Image/MD->PDF Layout πΌοΈβ‘οΈπ",
|
882 |
+
"Camera Snap π·",
|
883 |
+
"Download PDFs π₯",
|
884 |
+
"Build Titan (Local Models) π±",
|
885 |
+
"PDF Process (AI) π",
|
886 |
+
"Image Process (AI) πΌοΈ",
|
887 |
+
"Text Process (AI) π",
|
888 |
+
"Test OCR (AI) π",
|
889 |
+
"Test Image Gen (Diffusers) π¨",
|
890 |
+
"Character Editor π§βπ¨",
|
891 |
+
"Character Gallery πΌοΈ"
|
892 |
+
])
|
893 |
+
|
894 |
+
with tabs[0]:
|
895 |
+
st.header("Image/Markdown to PDF Layout Generator")
|
896 |
+
st.markdown("Select images and markdown files, reorder them, and generate a PDF.")
|
897 |
+
col1, col2 = st.columns(2)
|
898 |
+
with col1:
|
899 |
+
st.subheader("A. Select Assets")
|
900 |
+
selected_images = [f for f in get_typed_gallery_files('image') if st.session_state['asset_checkboxes']['image'].get(f, False)]
|
901 |
+
selected_mds = [f for f in get_typed_gallery_files('md') if st.session_state['asset_checkboxes']['md'].get(f, False)]
|
902 |
+
st.write(f"Selected Images: {len(selected_images)}")
|
903 |
+
st.write(f"Selected Markdown Files: {len(selected_mds)}")
|
904 |
+
with col2:
|
905 |
+
st.subheader("B. Review and Reorder")
|
906 |
+
layout_records = []
|
907 |
+
for idx, path in enumerate(selected_images + selected_mds, start=1):
|
908 |
+
is_md = path in selected_mds
|
909 |
+
try:
|
910 |
+
if is_md:
|
911 |
+
with open(path, 'r', encoding='utf-8') as f:
|
912 |
+
content = f.read(50)
|
913 |
+
layout_records.append({
|
914 |
+
"filename": os.path.basename(path),
|
915 |
+
"source": path,
|
916 |
+
"type": "Markdown",
|
917 |
+
"preview": content + "...",
|
918 |
+
"order": idx
|
919 |
+
})
|
920 |
+
else:
|
921 |
+
with Image.open(path) as im:
|
922 |
+
w, h = im.size
|
923 |
+
ar = round(w / h, 2) if h > 0 else 0
|
924 |
+
orient = "Square" if 0.9 <= ar <= 1.1 else ("Landscape" if ar > 1.1 else "Portrait")
|
925 |
+
layout_records.append({
|
926 |
+
"filename": os.path.basename(path),
|
927 |
+
"source": path,
|
928 |
+
"type": "Image",
|
929 |
+
"width": w,
|
930 |
+
"height": h,
|
931 |
+
"aspect_ratio": ar,
|
932 |
+
"orientation": orient,
|
933 |
+
"order": idx
|
934 |
+
})
|
935 |
+
except Exception as e:
|
936 |
+
logger.warning(f"Could not process {path}: {e}")
|
937 |
+
st.warning(f"Skipping invalid file: {os.path.basename(path)}")
|
938 |
+
if not layout_records:
|
939 |
+
st.infoperiod
|