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| #!/usr/bin/env python3 | |
| import os | |
| import glob | |
| import time | |
| import streamlit as st | |
| import fitz # PyMuPDF | |
| import requests | |
| from PIL import Image | |
| from transformers import AutoTokenizer, AutoModel | |
| from diffusers import StableDiffusionPipeline | |
| import cv2 | |
| import numpy as np | |
| import logging | |
| import asyncio | |
| import aiofiles | |
| from io import BytesIO | |
| # Logging setup | |
| logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") | |
| logger = logging.getLogger(__name__) | |
| log_records = [] | |
| class LogCaptureHandler(logging.Handler): | |
| def emit(self, record): | |
| log_records.append(record) | |
| logger.addHandler(LogCaptureHandler()) | |
| # Page Configuration | |
| st.set_page_config( | |
| page_title="AI Vision Titans 🚀", | |
| page_icon="🤖", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| menu_items={'About': "AI Vision Titans: PDF Snapshots, OCR, Image Gen, Line Drawings on CPU! 🌌"} | |
| ) | |
| # Initialize st.session_state | |
| if 'captured_files' not in st.session_state: | |
| st.session_state['captured_files'] = [] | |
| if 'processing' not in st.session_state: | |
| st.session_state['processing'] = {} | |
| # Utility Functions | |
| def generate_filename(sequence, ext="png"): | |
| timestamp = time.strftime("%d%m%Y%H%M%S") | |
| return f"{sequence}{timestamp}.{ext}" | |
| def get_gallery_files(file_types): | |
| return sorted([f for ext in file_types for f in glob.glob(f"*.{ext}")]) | |
| def update_gallery(): | |
| media_files = get_gallery_files(["png", "txt"]) | |
| if media_files: | |
| cols = st.sidebar.columns(2) | |
| for idx, file in enumerate(media_files[:gallery_size * 2]): | |
| with cols[idx % 2]: | |
| if file.endswith(".png"): | |
| st.image(Image.open(file), caption=file, use_container_width=True) | |
| elif file.endswith(".txt"): | |
| with open(file, "r") as f: | |
| content = f.read() | |
| st.text(content[:50] + "..." if len(content) > 50 else content, help=file) | |
| def download_pdf(url, output_path): | |
| try: | |
| response = requests.get(url, stream=True, timeout=10) | |
| if response.status_code == 200: | |
| with open(output_path, "wb") as f: | |
| for chunk in response.iter_content(chunk_size=8192): | |
| f.write(chunk) | |
| return True | |
| except requests.RequestException as e: | |
| logger.error(f"Failed to download {url}: {e}") | |
| return False | |
| # Model Loaders (CPU-focused) | |
| def load_ocr_got(): | |
| model_id = "ucaslcl/GOT-OCR2_0" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| model = AutoModel.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float32).to("cpu").eval() | |
| return tokenizer, model | |
| def load_image_gen(): | |
| model_id = "OFA-Sys/small-stable-diffusion-v0" # ~300 MB | |
| pipeline = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32).to("cpu") | |
| return pipeline | |
| def load_line_drawer(): | |
| def edge_detection(image, style="fine"): | |
| img_np = np.array(image.convert("RGB")) | |
| gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) | |
| if style == "fine": | |
| edges = cv2.Canny(gray, 50, 150) # Finer lines | |
| else: # "bold" | |
| edges = cv2.Canny(gray, 100, 200) # Bolder lines | |
| return Image.fromarray(edges) | |
| return edge_detection | |
| # Async Processing Functions | |
| async def process_pdf_snapshot(pdf_path, mode="thumbnail"): | |
| start_time = time.time() | |
| status = st.empty() | |
| status.text(f"Processing PDF Snapshot ({mode})... (0s)") | |
| doc = fitz.open(pdf_path) | |
| output_files = [] | |
| if mode == "thumbnail": | |
| page = doc[0] | |
| pix = page.get_pixmap(matrix=fitz.Matrix(0.5, 0.5)) # 50% scale | |
| output_file = generate_filename("thumbnail", "png") | |
| pix.save(output_file) | |
| output_files.append(output_file) | |
| elif mode == "twopage": | |
| for i in range(min(2, len(doc))): | |
| page = doc[i] | |
| pix = page.get_pixmap(matrix=fitz.Matrix(1.0, 1.0)) # Full scale | |
| output_file = generate_filename(f"twopage_{i}", "png") | |
| pix.save(output_file) | |
| output_files.append(output_file) | |
| doc.close() | |
| elapsed = int(time.time() - start_time) | |
| status.text(f"PDF Snapshot ({mode}) completed in {elapsed}s!") | |
| for file in output_files: | |
| if file not in st.session_state['captured_files']: | |
| st.session_state['captured_files'].append(file) | |
| update_gallery() | |
| return output_files | |
| async def process_ocr(image, output_file): | |
| start_time = time.time() | |
| status = st.empty() | |
| status.text("Processing GOT-OCR2_0... (0s)") | |
| tokenizer, model = load_ocr_got() | |
| result = model.chat(tokenizer, image, ocr_type='ocr') | |
| elapsed = int(time.time() - start_time) | |
| status.text(f"GOT-OCR2_0 completed in {elapsed}s!") | |
| async with aiofiles.open(output_file, "w") as f: | |
| await f.write(result) | |
| if output_file not in st.session_state['captured_files']: | |
| st.session_state['captured_files'].append(output_file) | |
| update_gallery() | |
| return result | |
| async def process_image_gen(prompt, output_file): | |
| start_time = time.time() | |
| status = st.empty() | |
| status.text("Processing Image Gen... (0s)") | |
| pipeline = load_image_gen() | |
| gen_image = pipeline(prompt, num_inference_steps=20).images[0] | |
| elapsed = int(time.time() - start_time) | |
| status.text(f"Image Gen completed in {elapsed}s!") | |
| gen_image.save(output_file) | |
| if output_file not in st.session_state['captured_files']: | |
| st.session_state['captured_files'].append(output_file) | |
| update_gallery() | |
| return gen_image | |
| async def process_line_drawing(image, style, output_file): | |
| start_time = time.time() | |
| status = st.empty() | |
| status.text(f"Processing Line Drawing ({style})... (0s)") | |
| edge_fn = load_line_drawer() | |
| line_drawing = edge_fn(image, style=style) | |
| elapsed = int(time.time() - start_time) | |
| status.text(f"Line Drawing ({style}) completed in {elapsed}s!") | |
| line_drawing.save(output_file) | |
| if output_file not in st.session_state['captured_files']: | |
| st.session_state['captured_files'].append(output_file) | |
| update_gallery() | |
| return line_drawing | |
| # Main App | |
| st.title("AI Vision Titans 🚀") | |
| # Sidebar Gallery | |
| st.sidebar.header("Captured Files 📜") | |
| gallery_size = st.sidebar.slider("Gallery Size", 1, 10, 4) | |
| update_gallery() | |
| st.sidebar.subheader("Action Logs 📜") | |
| log_container = st.sidebar.empty() | |
| with log_container: | |
| for record in log_records: | |
| st.write(f"{record.asctime} - {record.levelname} - {record.message}") | |
| # Tabs | |
| tab1, tab2, tab3, tab4, tab5 = st.tabs(["Camera Snap 📷", "Download PDFs 📥", "Test OCR 🔍", "Test Image Gen 🎨", "Test Line Drawings ✏️"]) | |
| with tab1: | |
| st.header("Camera Snap 📷") | |
| st.subheader("Single Capture") | |
| cols = st.columns(2) | |
| with cols[0]: | |
| cam0_img = st.camera_input("Take a picture - Cam 0", key="cam0") | |
| if cam0_img: | |
| filename = generate_filename(0) | |
| if filename not in st.session_state['captured_files']: | |
| with open(filename, "wb") as f: | |
| f.write(cam0_img.getvalue()) | |
| st.image(Image.open(filename), caption=filename, use_container_width=True) | |
| logger.info(f"Saved snapshot from Camera 0: {filename}") | |
| st.session_state['captured_files'].append(filename) | |
| update_gallery() | |
| with cols[1]: | |
| cam1_img = st.camera_input("Take a picture - Cam 1", key="cam1") | |
| if cam1_img: | |
| filename = generate_filename(1) | |
| if filename not in st.session_state['captured_files']: | |
| with open(filename, "wb") as f: | |
| f.write(cam1_img.getvalue()) | |
| st.image(Image.open(filename), caption=filename, use_container_width=True) | |
| logger.info(f"Saved snapshot from Camera 1: {filename}") | |
| st.session_state['captured_files'].append(filename) | |
| update_gallery() | |
| st.subheader("Burst Capture") | |
| slice_count = st.number_input("Number of Frames", min_value=1, max_value=20, value=10, key="burst_count") | |
| if st.button("Start Burst Capture 📸"): | |
| st.session_state['burst_frames'] = [] | |
| placeholder = st.empty() | |
| for i in range(slice_count): | |
| with placeholder.container(): | |
| st.write(f"Capturing frame {i+1}/{slice_count}...") | |
| img = st.camera_input(f"Frame {i}", key=f"burst_{i}_{time.time()}") | |
| if img: | |
| filename = generate_filename(f"burst_{i}") | |
| if filename not in st.session_state['captured_files']: | |
| with open(filename, "wb") as f: | |
| f.write(img.getvalue()) | |
| st.session_state['burst_frames'].append(filename) | |
| logger.info(f"Saved burst frame {i}: {filename}") | |
| st.image(Image.open(filename), caption=filename, use_container_width=True) | |
| time.sleep(0.5) | |
| st.session_state['captured_files'].extend([f for f in st.session_state['burst_frames'] if f not in st.session_state['captured_files']]) | |
| update_gallery() | |
| placeholder.success(f"Captured {len(st.session_state['burst_frames'])} frames!") | |
| with tab2: | |
| st.header("Download PDFs 📥") | |
| url_input = st.text_area("Enter PDF URLs (one per line)", height=100) | |
| mode = st.selectbox("Snapshot Mode", ["Thumbnail", "Two-Page View"], key="download_mode") | |
| if st.button("Download & Snapshot 📸"): | |
| urls = url_input.strip().split("\n") | |
| for url in urls: | |
| if url: | |
| pdf_path = generate_filename("downloaded", "pdf") | |
| if download_pdf(url, pdf_path): | |
| logger.info(f"Downloaded PDF from {url} to {pdf_path}") | |
| snapshots = asyncio.run(process_pdf_snapshot(pdf_path, mode.lower().replace(" ", ""))) | |
| for snapshot in snapshots: | |
| st.image(Image.open(snapshot), caption=snapshot, use_container_width=True) | |
| else: | |
| st.error(f"Failed to download {url}") | |
| with tab3: | |
| st.header("Test OCR 🔍") | |
| captured_files = get_gallery_files(["png"]) | |
| if captured_files: | |
| selected_file = st.selectbox("Select Image", captured_files, key="ocr_select") | |
| image = Image.open(selected_file) | |
| st.image(image, caption="Input Image", use_container_width=True) | |
| if st.button("Run OCR 🚀", key="ocr_run"): | |
| output_file = generate_filename("ocr_output", "txt") | |
| st.session_state['processing']['ocr'] = True | |
| result = asyncio.run(process_ocr(image, output_file)) | |
| st.text_area("OCR Result", result, height=200, key="ocr_result") | |
| st.success(f"OCR output saved to {output_file}") | |
| st.session_state['processing']['ocr'] = False | |
| else: | |
| st.warning("No images captured yet. Use Camera Snap or Download PDFs first!") | |
| with tab4: | |
| st.header("Test Image Gen 🎨") | |
| captured_files = get_gallery_files(["png"]) | |
| if captured_files: | |
| selected_file = st.selectbox("Select Image", captured_files, key="gen_select") | |
| image = Image.open(selected_file) | |
| st.image(image, caption="Reference Image", use_container_width=True) | |
| prompt = st.text_area("Prompt", "Generate a similar superhero image", key="gen_prompt") | |
| if st.button("Run Image Gen 🚀", key="gen_run"): | |
| output_file = generate_filename("gen_output", "png") | |
| st.session_state['processing']['gen'] = True | |
| result = asyncio.run(process_image_gen(prompt, output_file)) | |
| st.image(result, caption="Generated Image", use_container_width=True) | |
| st.success(f"Image saved to {output_file}") | |
| st.session_state['processing']['gen'] = False | |
| else: | |
| st.warning("No images captured yet. Use Camera Snap or Download PDFs first!") | |
| with tab5: | |
| st.header("Test Line Drawings ✏️") | |
| captured_files = get_gallery_files(["png"]) | |
| if captured_files: | |
| selected_file = st.selectbox("Select Image", captured_files, key="line_select") | |
| image = Image.open(selected_file) | |
| st.image(image, caption="Input Image", use_container_width=True) | |
| style = st.selectbox("Line Style", ["Fine", "Bold"], key="line_style") | |
| if st.button("Run Line Drawing 🚀", key="line_run"): | |
| output_file = generate_filename(f"line_{style.lower()}", "png") | |
| st.session_state['processing']['line'] = True | |
| result = asyncio.run(process_line_drawing(image, style.lower(), output_file)) | |
| st.image(result, caption=f"{style} Line Drawing", use_container_width=True) | |
| st.success(f"Line drawing saved to {output_file}") | |
| st.session_state['processing']['line'] = False | |
| else: | |
| st.warning("No images captured yet. Use Camera Snap or Download PDFs first!") | |
| # Initial Gallery Update | |
| update_gallery() |