app.py

import os
import io
import re
import logging
import subprocess
from datetime import datetime
import urllib.parse
import tempfile
import json # For streaming JSON messages
import time # For gevent.sleep

from flask import Flask, request, render_template, Response, stream_with_context
from werkzeug.utils import secure_filename

# Ensure gevent is imported and monkey patched if needed for other libraries
# that might not be gevent-friendly. For built-in libs and requests (with Gunicorn gevent worker),
# this is often handled by Gunicorn.
# from gevent import monkey
# monkey.patch_all() # Apply this early if you suspect issues with other libs

import requests
import pdfplumber
from pdf2image import convert_from_path, convert_from_bytes
import pytesseract
from PIL import Image
from huggingface_hub import HfApi, create_repo, HfHubHTTPError

# --- Flask App Initialization ---
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = tempfile.gettempdir()
app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024  # 50 MB limit for uploads, adjust as needed

# --- Logging Configuration ---
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# --- Hugging Face Configuration ---
HF_TOKEN = os.getenv("HF_TOKEN")
HF_DATASET_REPO_NAME = os.getenv("HF_DATASET_REPO_NAME", "pdf-images-extracted")
hf_api = HfApi()

# --- Helper to yield messages for streaming ---
def yield_message(type, data):
    """Helper to format messages as JSON strings for streaming."""
    # Add a newline so client can easily split messages
    return json.dumps({"type": type, **data}) + "\n"

# --- PDF Processing Helper Functions (Adapted for Streaming) ---

def check_poppler():
    # (Same as before)
    try:
        result = subprocess.run(["pdftoppm", "-v"], capture_output=True, text=True, check=False)
        version_info_log = result.stderr.strip() if result.stderr else result.stdout.strip()
        if version_info_log:
            logger.info(f"Poppler version check: {version_info_log.splitlines()[0] if version_info_log else 'No version output'}")
        else:
            logger.info("Poppler 'pdftoppm -v' ran. Assuming Poppler is present.")
        return True
    except FileNotFoundError:
        logger.error("Poppler (pdftoppm command) not found. Ensure poppler-utils is installed and in PATH.")
        return False
    except Exception as e:
        logger.error(f"An unexpected error occurred during Poppler check: {str(e)}")
        return False

def ensure_hf_dataset():
    # (Same as before, but logs info useful for streaming if an error occurs)
    if not HF_TOKEN:
        msg = "HF_TOKEN is not set. Cannot ensure Hugging Face dataset. Image uploads will fail."
        logger.warning(msg)
        return "Error: " + msg
    try:
        repo_id_obj = create_repo(repo_id=HF_DATASET_REPO_NAME, token=HF_TOKEN, repo_type="dataset", exist_ok=True)
        logger.info(f"Dataset repo ensured: {repo_id_obj.repo_id}")
        return repo_id_obj.repo_id
    except HfHubHTTPError as e:
        if e.response.status_code == 409:
             logger.info(f"Dataset repo '{HF_DATASET_REPO_NAME}' already exists.")
             # Attempt to construct the full repo_id (namespace/repo_name)
             try:
                 user_info = hf_api.whoami(token=HF_TOKEN)
                 namespace = user_info.get('name') if user_info else None
                 if namespace:
                     return f"{namespace}/{HF_DATASET_REPO_NAME}"
             except Exception as whoami_e:
                 logger.error(f"Could not determine namespace for existing repo via whoami: {whoami_e}")
             return f"hf://datasets/{HF_DATASET_REPO_NAME}" # Fallback, might not be full id
        logger.error(f"Hugging Face dataset error (HTTP {e.response.status_code}): {str(e)}")
        return f"Error: Failed to access or create dataset '{HF_DATASET_REPO_NAME}': {str(e)}"
    except Exception as e:
        logger.error(f"Hugging Face dataset error: {str(e)}", exc_info=True)
        return f"Error: Failed to access or create dataset '{HF_DATASET_REPO_NAME}': {str(e)}"


def upload_image_to_hf_stream(image_pil, filename_base, page_num_for_log=""):
    # (Adapted to potentially yield status during this sub-process if it were longer)
    # For now, it's synchronous but part of the larger stream.
    repo_id_or_error = ensure_hf_dataset()
    if isinstance(repo_id_or_error, str) and repo_id_or_error.startswith("Error"):
        return repo_id_or_error

    repo_id = repo_id_or_error
    temp_image_path = None
    try:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        repo_filename = f"images/{filename_base}_{page_num_for_log}_{timestamp}.png"
        
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=app.config['UPLOAD_FOLDER']) as tmp_file:
            temp_image_path = tmp_file.name
        image_pil.save(temp_image_path, format="PNG")

        logger.info(f"Attempting to upload {temp_image_path} to {repo_id}/{repo_filename}")
        file_url = hf_api.upload_file(
            path_or_fileobj=temp_image_path, path_in_repo=repo_filename,
            repo_id=repo_id, repo_type="dataset", token=HF_TOKEN
        )
        logger.info(f"Successfully uploaded image: {file_url}")
        return file_url
    except Exception as e:
        logger.error(f"Image upload error for {filename_base}{page_num_for_log}: {str(e)}", exc_info=True)
        return f"Error uploading image {filename_base}{page_num_for_log}: {str(e)}"
    finally:
        if temp_image_path and os.path.exists(temp_image_path):
            try: os.remove(temp_image_path)
            except OSError as ose: logger.error(f"Error removing temp image file {temp_image_path}: {ose}")


def format_page_text_to_markdown_chunk(page_text_content):
    """Formats a single page's text content into a markdown chunk.
       More complex formatting logic can be applied here page by page.
    """
    chunk_md = ""
    # Normalize newlines: multiple consecutive newlines become a single blank line (two \n chars)
    page_text_content = re.sub(r'\n\s*\n+', '\n\n', page_text_content.strip())
    lines = page_text_content.split('\n') 
    is_in_list = False
    for line_text in lines:
        line_stripped = line_text.strip()
        if not line_stripped:
            chunk_md += "\n"
            is_in_list = False
            continue
        list_match = re.match(r'^\s*(?:(?:\d+\.)|[*+-])\s+(.*)', line_stripped)
        is_heading_candidate = line_stripped.isupper() and 5 < len(line_stripped) < 100
        if is_heading_candidate and not list_match:
            chunk_md += f"## {line_stripped}\n\n"
            is_in_list = False
        elif list_match:
            list_item_text = list_match.group(1)
            chunk_md += f"- {list_item_text}\n"
            is_in_list = True
        else:
            if is_in_list: chunk_md += "\n"
            chunk_md += f"{line_text}\n\n"
            is_in_list = False
    return re.sub(r'\n\s*\n+', '\n\n', chunk_md.strip()) + "\n\n"


# --- Main PDF Processing Logic (Generator Function for Streaming) ---

def generate_pdf_conversion_stream(pdf_input_source_path_or_url):
    """
    Processes the PDF incrementally and yields status messages and markdown content.
    `pdf_input_source_path_or_url` is a local file path or a URL string.
    """
    try:
        # Initial Markdown Title
        yield yield_message("markdown_replace", {"content": "# Extracted PDF Content\n\n"})
        time.sleep(0.01) # Give gevent a chance to yield

        # 1. Text and Table Extraction (Page by Page)
        yield yield_message("status", {"message": "Opening PDF for text extraction..."})
        time.sleep(0.01)

        source_is_url = isinstance(pdf_input_source_path_or_url, str) and \
                        pdf_input_source_path_or_url.startswith(('http://', 'https://'))
        
        pdf_handle_for_text = None
        pdf_bytes_for_images = None # Store bytes if downloaded from URL for image extraction

        if source_is_url:
            try:
                response = requests.get(pdf_input_source_path_or_url, stream=True, timeout=60) # Increased timeout
                response.raise_for_status()
                pdf_bytes_for_images = response.content # Read all content for pdf2image
                pdf_handle_for_text = io.BytesIO(pdf_bytes_for_images) # Use BytesIO for pdfplumber
                yield yield_message("status", {"message": f"PDF downloaded from URL ({len(pdf_bytes_for_images)/1024:.2f} KB)."})
                time.sleep(0.01)
            except requests.RequestException as e:
                logger.error(f"URL fetch error for PDF processing: {str(e)}", exc_info=True)
                yield yield_message("error", {"message": f"Error fetching PDF from URL: {str(e)}"})
                return # Stop generation
        else: # Local file path
             pdf_handle_for_text = pdf_input_source_path_or_url # pdfplumber takes path

        total_text_pages = 0
        try:
            with pdfplumber.open(pdf_handle_for_text) as pdf:
                total_text_pages = len(pdf.pages)
                yield yield_message("status", {"message": f"Found {total_text_pages} page(s) for text extraction."})
                time.sleep(0.01)

                for i, page in enumerate(pdf.pages):
                    yield yield_message("status", {"message": f"Extracting text from page {i+1}/{total_text_pages}..."})
                    time.sleep(0.01) # gevent yield
                    
                    page_text = page.extract_text(layout=True, x_density=1, y_density=1) or ""
                    
                    page_tables_md = ""
                    tables = page.extract_tables()
                    if tables:
                        for table_idx, table_data in enumerate(tables):
                            if table_data:
                                yield yield_message("status", {"message": f"  Processing table {table_idx+1} on page {i+1}..."})
                                header = [" | ".join(str(cell) if cell is not None else "" for cell in table_data[0])]
                                separator = [" | ".join(["---"] * len(table_data[0]))]
                                body = [" | ".join(str(cell) if cell is not None else "" for cell in row) for row in table_data[1:]]
                                table_md_lines = header + separator + body
                                page_tables_md += f"**Table (Page {i+1}):**\n" + "\n".join(table_md_lines) + "\n\n"
                    
                    formatted_page_text_md = format_page_text_to_markdown_chunk(page_text)
                    
                    yield yield_message("markdown_chunk", {"content": formatted_page_text_md})
                    if page_tables_md:
                        yield yield_message("markdown_chunk", {"content": page_tables_md})
                    time.sleep(0.01) # gevent yield
        except Exception as e:
            logger.error(f"Error during PDF text/table extraction: {str(e)}", exc_info=True)
            yield yield_message("error", {"message": f"Error during text extraction: {str(e)}"})
            # Continue to image extraction if possible, or return based on severity

        # 2. Image Extraction and OCR
        if not check_poppler():
            yield yield_message("error", {"message": "Poppler (for image extraction) not found or not working."})
        else:
            yield yield_message("status", {"message": "Starting image extraction..."})
            yield yield_message("markdown_chunk", {"content": "## Extracted Images\n\n"})
            if not HF_TOKEN:
                 yield yield_message("markdown_chunk", {"content": "**Note:** `HF_TOKEN` not set. Images will be described but not uploaded.\n\n"})

            time.sleep(0.01)
            extracted_pil_images = []
            try:
                if source_is_url and pdf_bytes_for_images:
                    # Use the already downloaded bytes
                    extracted_pil_images = convert_from_bytes(pdf_bytes_for_images, dpi=150) # Lower DPI for speed/memory
                elif not source_is_url: # local file path
                    extracted_pil_images = convert_from_path(pdf_input_source_path_or_url, dpi=150)
                
                yield yield_message("status", {"message": f"Found {len(extracted_pil_images)} image(s) in PDF (these are rasterized pages for now)."})
                time.sleep(0.01)

                # TODO: Implement more granular image extraction if pdf2image supports it,
                # or if you integrate a library that can extract embedded images directly.
                # For now, convert_from_path/bytes often gives full pages as images.

                for i, img_pil in enumerate(extracted_pil_images):
                    page_num_for_log = f"page_{i+1}" # Assuming one image per page from convert_from_path
                    yield yield_message("status", {"message": f"Processing image {i+1}/{len(extracted_pil_images)} (OCR & Upload)..."})
                    time.sleep(0.01)
                    
                    ocr_text = ""
                    try:
                        ocr_text = pytesseract.image_to_string(img_pil).strip()
                        if ocr_text:
                            yield yield_message("status", {"message": f"  OCR successful for image {i+1}."})
                    except Exception as ocr_e:
                        logger.error(f"OCR error for image {i+1}: {str(ocr_e)}")
                        ocr_text = f"OCR failed: {str(ocr_e)}"
                    
                    image_md_chunk = ""
                    if HF_TOKEN:
                        image_url_or_error = upload_image_to_hf_stream(img_pil, "pdf_image", page_num_for_log)
                        if isinstance(image_url_or_error, str) and not image_url_or_error.startswith("Error"):
                            image_md_chunk += f"![Image {i+1}]({image_url_or_error})\n"
                            yield yield_message("status", {"message": f"  Image {i+1} uploaded."})
                        else:
                            image_md_chunk += f"**Image {i+1} (Upload Error):** {str(image_url_or_error)}\n\n"
                            yield yield_message("error", {"message": f"Failed to upload image {i+1}: {str(image_url_or_error)}"})
                    else:
                        image_md_chunk += f"**Image {i+1} (not uploaded due to missing HF_TOKEN)**\n"

                    if ocr_text:
                        image_md_chunk += f"**Image {i+1} OCR Text:**\n```\n{ocr_text}\n```\n\n"
                    
                    yield yield_message("image_md", {"content": image_md_chunk})
                    time.sleep(0.01) # gevent yield

            except Exception as e:
                logger.error(f"Error during image extraction/processing: {str(e)}", exc_info=True)
                yield yield_message("error", {"message": f"Error during image extraction: {str(e)}"})
        
        yield yield_message("final_status", {"message": "All processing stages complete."})

    except Exception as e:
        logger.error(f"Unhandled error in PDF conversion stream: {str(e)}", exc_info=True)
        yield yield_message("error", {"message": f"Critical processing error: {str(e)}"})


# --- Flask Routes ---

@app.route('/', methods=['GET'])
def index():
    return render_template('index.html')

@app.route('/process-stream', methods=['POST'])
def process_pdf_stream():
    pdf_file = request.files.get('pdf_file')
    pdf_url = request.form.get('pdf_url', '').strip()
    
    temp_pdf_path = None # To store path of uploaded file for cleanup
    pdf_input_source_for_generator = None

    def stream_processor():
        nonlocal temp_pdf_path # Make it accessible in this inner function for cleanup
        nonlocal pdf_input_source_for_generator

        try:
            if pdf_file and pdf_file.filename:
                if not pdf_file.filename.lower().endswith('.pdf'):
                    yield yield_message("error", {"message": "Uploaded file is not a PDF."})
                    return
                
                filename = secure_filename(pdf_file.filename)
                # Save to a temporary file (ensure UPLOAD_FOLDER is writable by app user)
                os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
                fd, temp_pdf_path = tempfile.mkstemp(suffix=".pdf", prefix="upload_", dir=app.config['UPLOAD_FOLDER'])
                os.close(fd) 
                pdf_file.save(temp_pdf_path)
                logger.info(f"Uploaded PDF saved to temporary path: {temp_pdf_path}")
                pdf_input_source_for_generator = temp_pdf_path
                yield yield_message("status", {"message": f"Processing uploaded PDF: {filename}"})
                time.sleep(0.01)

            elif pdf_url:
                unquoted_url = urllib.parse.unquote(pdf_url)
                if not (unquoted_url.startswith('http://') or unquoted_url.startswith('https://')):
                    yield yield_message("error", {"message": "Invalid URL scheme. Must be http or https."})
                    return
                # Consider a light check for .pdf extension, but content-type is more reliable
                
                pdf_input_source_for_generator = unquoted_url
                yield yield_message("status", {"message": f"Preparing to process PDF from URL: {unquoted_url}"})
                time.sleep(0.01)
            else:
                yield yield_message("error", {"message": "No PDF file uploaded and no PDF URL provided."})
                return

            # Yield from the main generator
            for message_part in generate_pdf_conversion_stream(pdf_input_source_for_generator):
                yield message_part
                # time.sleep(0.01) # Allow gevent to switch context, important for streaming

        except Exception as e:
            logger.error(f"Error setting up stream or in initial validation: {str(e)}", exc_info=True)
            yield yield_message("error", {"message": f"Setup error: {str(e)}"})
        finally:
            if temp_pdf_path and os.path.exists(temp_pdf_path):
                try:
                    os.remove(temp_pdf_path)
                    logger.info(f"Cleaned up temporary PDF: {temp_pdf_path}")
                    yield yield_message("status", {"message": f"Cleaned up temporary file."})
                except OSError as ose:
                    logger.error(f"Error removing temporary PDF {temp_pdf_path}: {ose}")
                    yield yield_message("error", {"message": f"Could not clean temp file: {ose}"})
    
    # Using stream_with_context for proper handling of request context within the generator
    return Response(stream_with_context(stream_processor()), mimetype='application/x-ndjson')


# --- Main Execution ---
if __name__ == '__main__':
    if not check_poppler(): # Check Poppler at startup for local dev
        logger.warning("Poppler utilities might not be installed correctly. PDF processing might fail.")
    os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
    # For local dev, Flask's built-in server is fine. Gunicorn handles production.
    # The 'threaded=True' or using gevent server locally can also help test streaming.
    app.run(host='0.0.0.0', port=int(os.getenv("PORT", 7860)), debug=True, threaded=True)