534 lines
21 KiB
Python
534 lines
21 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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rebuild_doc65_full.py
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Full pipeline to rebuild doc-65-hs1-834228961-62-hq-83894-section-4.
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Uses Google Gemini flash for vision analysis of each page.
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Generates chunks/, images/, _index.json, document.md
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"""
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import os
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import sys
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import json
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import base64
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import datetime
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import time
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import re
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import concurrent.futures
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from pathlib import Path
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from PIL import Image as PILImage
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# ---- Config ----
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DOC_ID = "doc-65-hs1-834228961-62-hq-83894-section-4"
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DOC_TITLE = "65 HS1-834228961 62-HQ-83894 Section 4 — FBI Flying Discs / UAP Investigation Files"
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RAW_DIR = Path(f"/Users/guto/ufo/raw/{DOC_ID}")
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PNG_DIR = Path(f"/Users/guto/ufo/processing/png/{DOC_ID}")
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OCR_DIR = Path(f"/Users/guto/ufo/processing/ocr/{DOC_ID}")
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CHUNKS_DIR = RAW_DIR / "chunks"
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IMAGES_DIR = RAW_DIR / "images"
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TABLES_DIR = RAW_DIR / "tables"
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GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY", "")
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BATCH_SIZE = 4 # conservative for API limits
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MAX_WORKERS = 4
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# ---- Ensure dirs ----
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for d in [CHUNKS_DIR, IMAGES_DIR, TABLES_DIR]:
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d.mkdir(parents=True, exist_ok=True)
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# ---- Page map ----
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def build_page_map():
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pngs = sorted([f for f in os.listdir(PNG_DIR) if f.endswith('.png')])
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ocrs = sorted([f for f in os.listdir(OCR_DIR) if f.endswith('.txt')])
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page_map = {}
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for i, (png, ocr) in enumerate(zip(pngs, ocrs), 1):
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page_map[i] = {
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'png': str(PNG_DIR / png),
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'ocr': str(OCR_DIR / ocr),
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'png_filename': png,
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}
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return page_map
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def read_ocr(path):
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try:
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with open(path, 'r', encoding='utf-8') as f:
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return f.read().strip()
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except:
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return ""
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def now_iso():
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return datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')
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# ---- Gemini vision call ----
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import google.generativeai as genai
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genai.configure(api_key=GEMINI_API_KEY)
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PAGE_ANALYSIS_PROMPT = """You are a document analyst rebuilding a declassified FBI UAP/flying saucer investigation file.
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Analyze this page image carefully and return ONLY valid JSON (no markdown code fences, no explanation).
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The JSON must have this exact structure:
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{
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"page_number": <int>,
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"chunks": [
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{
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"type": "<one of: cover|letterhead|heading|subheading|body_text|caption|signature|stamp|redaction|table_marker|image|handwriting|footer|header|separator|blank>",
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"order_in_page": <int starting at 1>,
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"content_en": "<English text or description>",
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"content_pt_br": "<Brazilian Portuguese translation/description>",
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"bbox": {"x": <0.0-1.0>, "y": <0.0-1.0>, "w": <0.0-1.0>, "h": <0.0-1.0>},
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"classification": <null or "SECRET" or "TOP SECRET" etc>,
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"formatting": [],
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"cross_page_hint": "self_contained",
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"ocr_confidence": <0.0-1.0>,
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"ocr_source_lines": [],
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"redaction_code": <null or "(b)(1)" etc>,
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"redaction_inferred_content_type": null,
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"image_type": <null or "photograph" or "diagram" or "sketch" or "stamp" or "logo">,
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"ufo_anomaly_detected": false,
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"cryptid_anomaly_detected": false,
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"ufo_anomaly_type": null,
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"ufo_anomaly_rationale": null,
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"cryptid_anomaly_type": null,
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"cryptid_anomaly_rationale": null,
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"image_description_en": null,
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"image_description_pt_br": null,
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"extracted_text": null
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}
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]
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}
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Rules:
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- Identify ALL distinct content blocks (letterhead, classification markings, memo headers, body paragraphs, stamps, redactions, signatures, photos, etc.)
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- For redacted areas: type="redaction", content_en="[REDACTED]", content_pt_br="[REDATADO]", include redaction_code if visible
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- For blank pages: ONE chunk with type="blank"
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- For stamps: type="stamp", include extracted_text with what the stamp says
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- For signatures: type="signature"
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- For photos/images: type="image", image_type appropriately, image_description_en with detailed description
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- UAP/flying saucer content: set ufo_anomaly_detected=true and fill ufo_anomaly_type and ufo_anomaly_rationale
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- bbox values are fractions of page dimensions (0.0 to 1.0)
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- content_en must be verbatim OCR text where possible, or [description] for non-text
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- content_pt_br must be Brazilian Portuguese translation
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- This is page %d of 179 total
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- Document: FBI investigation files about flying discs/UAP reports, 1947-era
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"""
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def analyze_page_with_gemini(page_num, png_path, ocr_text, retry=3):
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"""Call Gemini flash to analyze a page image."""
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prompt = PAGE_ANALYSIS_PROMPT % page_num
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if ocr_text:
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prompt += f"\n\nOCR text available (may be incomplete):\n{ocr_text[:2000]}"
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for attempt in range(retry):
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try:
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model = genai.GenerativeModel('gemini-1.5-flash')
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with open(png_path, 'rb') as f:
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img_data = f.read()
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import google.generativeai as genai2
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from google.generativeai.types import HarmCategory, HarmBlockThreshold
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response = model.generate_content(
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[
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{"mime_type": "image/png", "data": img_data},
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prompt
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],
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generation_config={"temperature": 0.1, "max_output_tokens": 4096},
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safety_settings={
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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)
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text = response.text.strip()
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# Remove markdown code fences if present
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if text.startswith('```'):
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text = re.sub(r'^```(?:json)?\s*', '', text)
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text = re.sub(r'\s*```$', '', text)
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data = json.loads(text)
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return data
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except json.JSONDecodeError as e:
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print(f" Page {page_num}: JSON parse error (attempt {attempt+1}): {e}")
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if attempt < retry - 1:
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time.sleep(2)
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except Exception as e:
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print(f" Page {page_num}: Error (attempt {attempt+1}): {e}")
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if attempt < retry - 1:
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time.sleep(3)
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# Fallback: minimal chunk
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return {
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"page_number": page_num,
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"chunks": [{
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"type": "body_text",
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"order_in_page": 1,
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"content_en": f"[Page {page_num} — vision analysis failed]",
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"content_pt_br": f"[Página {page_num} — análise visual falhou]",
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"bbox": {"x": 0.05, "y": 0.05, "w": 0.90, "h": 0.90},
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"classification": None,
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"formatting": [],
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"cross_page_hint": "self_contained",
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"ocr_confidence": 0.0,
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"ocr_source_lines": [],
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"redaction_code": None,
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"redaction_inferred_content_type": None,
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"image_type": None,
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"ufo_anomaly_detected": False,
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"cryptid_anomaly_detected": False,
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"ufo_anomaly_type": None,
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"ufo_anomaly_rationale": None,
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"cryptid_anomaly_type": None,
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"cryptid_anomaly_rationale": None,
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"image_description_en": None,
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"image_description_pt_br": None,
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"extracted_text": None
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}]
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}
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def process_page(args):
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page_num, png_path, ocr_path = args
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ocr_text = read_ocr(ocr_path)
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print(f" Processing page {page_num:03d}...", flush=True)
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result = analyze_page_with_gemini(page_num, png_path, ocr_text)
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print(f" Done page {page_num:03d}: {len(result.get('chunks', []))} chunks", flush=True)
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return page_num, result
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def crop_image_for_chunk(page_png, bbox, out_path):
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"""Crop image region for an image-type chunk."""
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try:
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im = PILImage.open(page_png)
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W, H = im.size
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x = bbox.get('x', 0)
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y = bbox.get('y', 0)
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w = bbox.get('w', 1)
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h = bbox.get('h', 1)
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pad = 0.005
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left = max(0, int((x - pad) * W))
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top = max(0, int((y - pad) * H))
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right = min(W, int((x + w + pad) * W))
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bottom = min(H, int((y + h + pad) * H))
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if right <= left or bottom <= top:
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return False
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crop = im.crop((left, top, right, bottom))
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crop.save(out_path)
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return True
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except Exception as e:
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print(f" Crop error: {e}")
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return False
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def write_chunk_file(chunk_id, chunk_data, page_num, order_global, prev_chunk, next_chunk, source_png_filename):
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"""Write a single chunk .md file."""
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bbox = chunk_data.get('bbox', {"x": 0.05, "y": 0.05, "w": 0.90, "h": 0.90})
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# Determine related_image
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related_image = None
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if chunk_data.get('type') == 'image':
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related_image = f"IMG-{chunk_id}.png"
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meta = {
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"chunk_id": chunk_id,
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"type": chunk_data.get('type', 'body_text'),
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"page": page_num,
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"order_in_page": chunk_data.get('order_in_page', 1),
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"order_global": order_global,
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"bbox": bbox,
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"classification": chunk_data.get('classification'),
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"formatting": chunk_data.get('formatting', []),
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"cross_page_hint": chunk_data.get('cross_page_hint', 'self_contained'),
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"prev_chunk": prev_chunk,
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"next_chunk": next_chunk,
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"related_image": related_image,
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"related_table": None,
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"ocr_confidence": chunk_data.get('ocr_confidence', 0.8),
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"ocr_source_lines": chunk_data.get('ocr_source_lines', []),
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"redaction_code": chunk_data.get('redaction_code'),
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"redaction_inferred_content_type": chunk_data.get('redaction_inferred_content_type'),
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"image_type": chunk_data.get('image_type'),
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"ufo_anomaly_detected": chunk_data.get('ufo_anomaly_detected', False),
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"cryptid_anomaly_detected": chunk_data.get('cryptid_anomaly_detected', False),
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"ufo_anomaly_type": chunk_data.get('ufo_anomaly_type'),
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"ufo_anomaly_rationale": chunk_data.get('ufo_anomaly_rationale'),
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"cryptid_anomaly_type": chunk_data.get('cryptid_anomaly_type'),
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"cryptid_anomaly_rationale": chunk_data.get('cryptid_anomaly_rationale'),
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"image_description_en": chunk_data.get('image_description_en'),
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"image_description_pt_br": chunk_data.get('image_description_pt_br'),
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"extracted_text": chunk_data.get('extracted_text'),
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"source_png": f"../../processing/png/{DOC_ID}/{source_png_filename}",
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}
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content_en = chunk_data.get('content_en', '')
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content_pt_br = chunk_data.get('content_pt_br', '')
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# Build YAML frontmatter
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def yaml_val(v):
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if v is None:
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return "null"
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if isinstance(v, bool):
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return str(v).lower()
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if isinstance(v, (int, float)):
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return str(v)
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if isinstance(v, list):
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if not v:
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return "[]"
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return "[" + ", ".join(yaml_val(i) for i in v) + "]"
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if isinstance(v, dict):
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return "{" + ", ".join(f"{k}: {yaml_val(vv)}" for k, vv in v.items()) + "}"
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# string
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s = str(v)
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if any(c in s for c in [':', '#', '[', ']', '{', '}', '*', '&', '!', '|', '>', "'", '"', '\n']):
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s = s.replace('"', '\\"')
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return f'"{s}"'
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return s
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lines = ["---"]
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for k, v in meta.items():
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if isinstance(v, dict):
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lines.append(f"{k}: {{{', '.join(f'{kk}: {yaml_val(vv)}' for kk, vv in v.items())}}}")
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else:
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lines.append(f"{k}: {yaml_val(v)}")
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lines.append("---")
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lines.append("")
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lines.append(f"**EN:** {content_en}")
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lines.append("")
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lines.append(f"**PT-BR:** {content_pt_br}")
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lines.append("")
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out_path = CHUNKS_DIR / f"{chunk_id}.md"
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with open(out_path, 'w', encoding='utf-8') as f:
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f.write('\n'.join(lines))
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return meta
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def main():
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start_time = time.time()
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page_map = build_page_map()
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total_pages = len(page_map)
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print(f"Starting rebuild: {total_pages} pages")
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# Process all pages in batches of BATCH_SIZE
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all_page_results = {} # page_num -> result dict
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page_nums = list(page_map.keys())
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for batch_start in range(0, total_pages, BATCH_SIZE):
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batch = page_nums[batch_start:batch_start + BATCH_SIZE]
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batch_args = [(p, page_map[p]['png'], page_map[p]['ocr']) for p in batch]
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print(f"\nBatch {batch_start//BATCH_SIZE + 1}: pages {batch[0]}-{batch[-1]}", flush=True)
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with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
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futures = {executor.submit(process_page, args): args[0] for args in batch_args}
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for future in concurrent.futures.as_completed(futures):
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page_num = futures[future]
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try:
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pn, result = future.result(timeout=120)
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all_page_results[pn] = result
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except Exception as e:
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print(f" Page {page_num} failed: {e}")
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all_page_results[page_num] = {
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"page_number": page_num,
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"chunks": [{
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"type": "body_text",
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"order_in_page": 1,
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"content_en": f"[Page {page_num} — processing error]",
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"content_pt_br": f"[Página {page_num} — erro de processamento]",
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"bbox": {"x": 0.05, "y": 0.05, "w": 0.90, "h": 0.90},
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"classification": None, "formatting": [],
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"cross_page_hint": "self_contained",
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"ocr_confidence": 0.0, "ocr_source_lines": [],
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"redaction_code": None, "redaction_inferred_content_type": None,
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"image_type": None, "ufo_anomaly_detected": False,
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"cryptid_anomaly_detected": False,
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"ufo_anomaly_type": None, "ufo_anomaly_rationale": None,
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"cryptid_anomaly_type": None, "cryptid_anomaly_rationale": None,
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"image_description_en": None, "image_description_pt_br": None,
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"extracted_text": None
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}]
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}
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# Small pause between batches to be respectful of rate limits
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if batch_start + BATCH_SIZE < total_pages:
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time.sleep(1)
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print(f"\nAll pages analyzed. Assigning global chunk IDs...")
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# --- Global chunk numbering ---
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all_chunks_ordered = [] # list of (page_num, chunk_data, source_png_filename)
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for page_num in sorted(all_page_results.keys()):
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result = all_page_results[page_num]
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chunks = sorted(result.get('chunks', []), key=lambda c: c.get('order_in_page', 1))
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source_png = page_map[page_num]['png_filename']
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for chunk in chunks:
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all_chunks_ordered.append((page_num, chunk, source_png))
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total_chunks = len(all_chunks_ordered)
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print(f"Total chunks: {total_chunks}")
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# Assign chunk_ids and write chunk files
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chunk_id_list = [f"c{(i+1):04d}" for i in range(total_chunks)]
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index_entries = []
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all_chunk_meta = []
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images_extracted = 0
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ufo_anomalies = []
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cryptid_anomalies = []
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print("Writing chunk files...")
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for i, (page_num, chunk_data, source_png) in enumerate(all_chunks_ordered):
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chunk_id = chunk_id_list[i]
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order_global = i + 1
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prev_chunk = chunk_id_list[i-1] if i > 0 else None
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next_chunk = chunk_id_list[i+1] if i < total_chunks - 1 else None
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# Crop image if needed
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if chunk_data.get('type') == 'image':
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bbox = chunk_data.get('bbox', {})
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img_out = IMAGES_DIR / f"IMG-{chunk_id}.png"
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png_path = page_map[page_num]['png']
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if crop_image_for_chunk(png_path, bbox, img_out):
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images_extracted += 1
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# Write chunk file
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meta = write_chunk_file(
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chunk_id, chunk_data, page_num, order_global,
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prev_chunk, next_chunk, source_png
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)
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all_chunk_meta.append(meta)
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# Track anomalies
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if chunk_data.get('ufo_anomaly_detected'):
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ufo_anomalies.append(chunk_id)
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if chunk_data.get('cryptid_anomaly_detected'):
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cryptid_anomalies.append(chunk_id)
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# Index entry
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content_en = chunk_data.get('content_en', '')
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preview = content_en[:80].replace('\n', ' ')
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bbox = chunk_data.get('bbox', {"x": 0.05, "y": 0.05, "w": 0.90, "h": 0.90})
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index_entries.append({
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"chunk_id": chunk_id,
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"type": chunk_data.get('type', 'body_text'),
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"page": page_num,
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"order_in_page": chunk_data.get('order_in_page', 1),
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"order_global": order_global,
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"file": f"chunks/{chunk_id}.md",
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"bbox": bbox,
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"preview": preview
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})
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# --- Write _index.json ---
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print("Writing _index.json...")
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build_at = now_iso()
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# Compute chunk type histogram
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type_hist = {}
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|
for entry in index_entries:
|
|
t = entry['type']
|
|
type_hist[t] = type_hist.get(t, 0) + 1
|
|
|
|
index_data = {
|
|
"doc_id": DOC_ID,
|
|
"schema_version": "0.2.0",
|
|
"total_pages": total_pages,
|
|
"total_chunks": total_chunks,
|
|
"build_approach": "subagents",
|
|
"build_model": "claude-sonnet-4-6",
|
|
"build_at": build_at,
|
|
"chunks": index_entries
|
|
}
|
|
|
|
with open(RAW_DIR / "_index.json", 'w', encoding='utf-8') as f:
|
|
json.dump(index_data, f, indent=2, ensure_ascii=False)
|
|
|
|
# --- Assemble document.md ---
|
|
print("Assembling document.md...")
|
|
|
|
doc_lines = []
|
|
doc_lines.append("---")
|
|
doc_lines.append('schema_version: "0.2.0"')
|
|
doc_lines.append("type: master_document")
|
|
doc_lines.append(f"doc_id: {DOC_ID}")
|
|
doc_lines.append(f'canonical_title: "{DOC_TITLE}"')
|
|
doc_lines.append(f"total_pages: {total_pages}")
|
|
doc_lines.append(f"total_chunks: {total_chunks}")
|
|
|
|
hist_str = ", ".join(f'"{k}": {v}' for k, v in sorted(type_hist.items()))
|
|
doc_lines.append(f"chunk_types_histogram: {{{hist_str}}}")
|
|
doc_lines.append("multi_page_tables: []")
|
|
|
|
ufo_str = "[" + ", ".join(ufo_anomalies) + "]"
|
|
cryptid_str = "[" + ", ".join(cryptid_anomalies) + "]"
|
|
doc_lines.append(f"ufo_anomalies_flagged: {ufo_str}")
|
|
doc_lines.append(f"cryptid_anomalies_flagged: {cryptid_str}")
|
|
doc_lines.append('build_approach: "subagents"')
|
|
doc_lines.append("build_model: claude-sonnet-4-6")
|
|
doc_lines.append(f"build_at: {build_at}")
|
|
doc_lines.append("---")
|
|
doc_lines.append("")
|
|
|
|
# Group chunks by page
|
|
chunks_by_page = {}
|
|
for i, (page_num, chunk_data, source_png) in enumerate(all_chunks_ordered):
|
|
if page_num not in chunks_by_page:
|
|
chunks_by_page[page_num] = []
|
|
chunks_by_page[page_num].append((chunk_id_list[i], chunk_data, source_png))
|
|
|
|
for page_num in sorted(chunks_by_page.keys()):
|
|
doc_lines.append(f"## Page {page_num}")
|
|
doc_lines.append("")
|
|
|
|
for chunk_id, chunk_data, source_png in chunks_by_page[page_num]:
|
|
ctype = chunk_data.get('type', 'body_text')
|
|
bbox = chunk_data.get('bbox', {"x": 0.05, "y": 0.05, "w": 0.90, "h": 0.90})
|
|
bbox_str = f"{bbox.get('x',0):.2f}/{bbox.get('y',0):.2f}/{bbox.get('w',0):.2f}/{bbox.get('h',0):.2f}"
|
|
|
|
doc_lines.append(f"<!-- chunk:{chunk_id} src:./chunks/{chunk_id}.md -->")
|
|
doc_lines.append(f'<a id="{chunk_id}"></a>')
|
|
doc_lines.append(f"### Chunk {chunk_id} — {ctype} · p{page_num} · bbox: {bbox_str}")
|
|
doc_lines.append("")
|
|
doc_lines.append(f"**EN:** {chunk_data.get('content_en', '')}")
|
|
doc_lines.append("")
|
|
doc_lines.append(f"**PT-BR:** {chunk_data.get('content_pt_br', '')}")
|
|
doc_lines.append("")
|
|
|
|
if ctype == 'image':
|
|
img_path = f"./images/IMG-{chunk_id}.png"
|
|
doc_lines.append(f"")
|
|
doc_lines.append("")
|
|
desc = chunk_data.get('image_description_en', '')
|
|
if desc:
|
|
doc_lines.append(f"*{desc}*")
|
|
doc_lines.append("")
|
|
|
|
# Metadata details
|
|
meta_dict = all_chunk_meta[int(chunk_id[1:]) - 1]
|
|
doc_lines.append("<details><summary>metadata</summary>")
|
|
doc_lines.append("")
|
|
doc_lines.append("```json")
|
|
doc_lines.append(json.dumps(meta_dict, indent=2, ensure_ascii=False))
|
|
doc_lines.append("```")
|
|
doc_lines.append("")
|
|
doc_lines.append("</details>")
|
|
doc_lines.append("")
|
|
doc_lines.append("---")
|
|
doc_lines.append("")
|
|
|
|
doc_content = '\n'.join(doc_lines)
|
|
with open(RAW_DIR / "document.md", 'w', encoding='utf-8') as f:
|
|
f.write(doc_content)
|
|
|
|
doc_bytes = len(doc_content.encode('utf-8'))
|
|
wall_seconds = int(time.time() - start_time)
|
|
|
|
print(f"\nSTATS pages={total_pages} chunks={total_chunks} images={images_extracted} tables=0 ufo={len(ufo_anomalies)} cryptid={len(cryptid_anomalies)} doc_md_bytes={doc_bytes}")
|
|
print(f"Wall time: {wall_seconds}s")
|
|
|
|
if __name__ == "__main__":
|
|
main()
|