306 lines
11 KiB
Python
306 lines
11 KiB
Python
import json
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import os
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import random
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from pathlib import Path
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import glob
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import re
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def load_json(filepath):
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"""
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Loads a JSON file .
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"""
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try:
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with open(filepath, "r", encoding="utf-8") as f:
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return json.load(f)
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except FileNotFoundError:
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print(f"Error: The file was not found at {filepath}")
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return None
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except json.JSONDecodeError as e:
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print(f"Error: The file at {filepath} is not a valid JSON file. Details: {e}")
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return None
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def read_text_file(filepath):
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"""
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Loads a prompt from a text file.
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"""
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try:
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with open(filepath, "r", encoding="utf-8") as f:
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return f.read().strip()
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except FileNotFoundError:
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print(f"Error: The file was not found at {filepath}")
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return None
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def format_items_list(items, language):
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"""
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Formats a list of item dictionaries (services) into a human-readable string.
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"""
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if not items:
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return ""
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formatted_lines = []
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for item in items:
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if not isinstance(item, dict):
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continue
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parts = []
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desc = item.get("description")
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if desc is not None:
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parts.append(f"{desc}")
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qty = item.get("quantity")
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if qty is not None:
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qty_str = "Quantity" if language == "english" else "Quantité"
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parts.append(f"{qty_str}: {qty}")
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date = item.get("date_of_service")
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if date is not None:
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date_str = "Date" if language == "english" else "Date"
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parts.append(f"{date_str}: {date}")
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mandatory = item.get("mandatory_coverage")
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if mandatory is not None:
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amo_str = (
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"Mandatory Coverage"
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if language == "english"
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else "Couverture obligatoire"
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)
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parts.append(f"{amo_str}: {mandatory}")
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amount = item.get("amount")
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if amount is not None:
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amount_str = "Amount" if language == "english" else "Montant"
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parts.append(f"{amount_str}: {amount}")
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formatted_lines.append("- " + ", ".join(parts))
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return "\n".join(formatted_lines)
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def get_conversational_answer(field, label_data, answer_bank, language):
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"""
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Generates a complete conversational answer by selecting a template and filling it
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with the appropriate value from the label data.
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"""
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if not isinstance(label_data, dict):
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return ""
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value = label_data.get(field)
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field_templates = answer_bank.get(field)
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if not field_templates:
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return str(value) if value is not None else ""
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if value is None:
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return random.choice(field_templates.get("null", {}).get(language, [""]))
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if field == "items":
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template = random.choice(field_templates[language])
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formatted_list_string = format_items_list(value, language)
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return template.format(value=formatted_list_string)
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if isinstance(value, bool):
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bool_key = str(value).lower()
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if bool_key in field_templates[language]:
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return random.choice(field_templates[language][bool_key])
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return str(value)
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if isinstance(field_templates[language], list):
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template = random.choice(field_templates[language])
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return template.format(value=value)
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return str(value) if value is not None else ""
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# --- Conversations Generation for Label Data ---
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def generate_vqa_conversations(
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labels_path,
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image_root,
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system_prompt_path,
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questions_path,
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answers_path,
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output_path,
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ratio=0.4,
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):
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"""
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Generates multiple conversational VQA pairs for each field in a label file,
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and handles multi-page documents.
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"""
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all_data_entries = load_json(labels_path)
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system_prompt = read_text_file(system_prompt_path)
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question_bank = load_json(questions_path)
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answer_bank = load_json(answers_path)
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if (
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not all_data_entries
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or not system_prompt
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or not question_bank
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or not answer_bank
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):
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print("Could not load one or more necessary files. Exiting.")
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return
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final_conversations = []
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# Process each entry in the main label file
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for entry in all_data_entries:
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label_data = entry.get("label")
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image_filename_prefix = entry.get("image")
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# Skip entries that are unlabeled, as we need the label to generate Q&A pairs
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if not label_data or not image_filename_prefix:
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continue
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# Get a list of all fields in the label data
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# all_fields = [field for field in label_data if isinstance(field, str) and field in question_bank]
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all_fields = list(question_bank.keys())
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# Determine how many questions to ask based on the available fields
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num_to_sample = max(1, int(len(all_fields) * ratio))
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# Randomly select fields to ask questions about
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fields_to_ask = random.sample(all_fields, num_to_sample)
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# Find all image files in the image_root that start with the prefix.
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# This handles cases like 'doc-1.jpg', 'doc-2.jpg', 'doc_scale.jpg' etc.
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prefix_stem = Path(image_filename_prefix).stem
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search_pattern = os.path.join(image_root, f"{prefix_stem}*")
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found_image_paths = sorted(glob.glob(search_pattern))
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if not found_image_paths:
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print(
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f"Warning: No images found for prefix '{prefix_stem}' in '{image_root}'. Skipping."
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)
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continue
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# Create a list of image dictionaries for the user message
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image_content_list = [
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{"type": "image", "image": path} for path in found_image_paths
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]
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# --- Create a new conversation for EACH field in the label ---
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for field in fields_to_ask:
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if not isinstance(field, str):
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continue
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if field not in question_bank:
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continue
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language = random.choice(["english", "french"])
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# Get the question from the question bank
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question_text = random.choice(question_bank[field][language])
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# Get the conversational answer from the answer bank
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answer_text = get_conversational_answer(
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field, label_data, answer_bank, language
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)
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# --- Assemble the conversation in the desired format ---
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system_message = {"role": "system", "content": system_prompt}
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user_message = {
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"role": "user",
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# The content is the list of image dicts, followed by the text dict
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"content": image_content_list
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+ [{"type": "text", "text": "<image>" + question_text}],
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}
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assistant_message = {"role": "assistant", "content": answer_text}
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conversation = [system_message, user_message, assistant_message]
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final_conversations.append(conversation)
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# Save the final list of conversations to the output file
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(final_conversations, f, indent=4, ensure_ascii=False)
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print(f"Success! Generated {len(final_conversations)} conversational VQA entries.")
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print(f"Formatted data saved to: {output_path}")
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# --- Conversations Generation for only Images ---
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def generate_vq_question(
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image_root, system_prompt_path, questions_path, output_path, ratio=0.4
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):
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"""
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Generates conversational VQA pairs for each document based on images only (no labels).
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Each conversation contains a system and user message for each question in the question bank.
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"""
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system_prompt = read_text_file(system_prompt_path)
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question_bank = load_json(questions_path)
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if not system_prompt or not question_bank:
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print("Could not load one or more necessary files. Exiting.")
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return
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# Find all images and group by prefix
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all_image_paths = sorted(
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glob.glob(os.path.join(image_root, "*.jpg"))
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+ glob.glob(os.path.join(image_root, "*.png"))
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+ glob.glob(os.path.join(image_root, "*.jpeg"))
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)
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prefix_to_images = {}
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for path in all_image_paths:
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if not os.path.isfile(path):
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continue
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stem = Path(path).stem
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# Remove suffixes like _1_scale, _2_scale, etc.
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prefix = re.sub(r"(_\d+(_scale)?)$", "", stem)
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prefix_to_images.setdefault(prefix, []).append(path)
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# Get a list of all possible fields from the question bank.
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all_fields = list(question_bank.keys())
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# Determine how many questions to ask based on the available fields
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num_to_sample = max(1, int(len(all_fields) * ratio))
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final_conversations = []
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for prefix, image_paths in prefix_to_images.items():
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image_content_list = [
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{"type": "image", "image": path} for path in sorted(image_paths)
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]
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# Randomly select fields to ask questions about
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fields_to_ask = random.sample(all_fields, num_to_sample)
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for field in fields_to_ask:
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language = random.choice(["english", "french"])
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question_text = random.choice(question_bank[field][language])
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system_message = {"role": "system", "content": system_prompt}
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user_message = {
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"role": "user",
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"content": image_content_list
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+ [{"type": "text", "text": "<image>" + question_text}],
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}
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conversation = [system_message, user_message]
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final_conversations.append(conversation)
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(final_conversations, f, indent=4, ensure_ascii=False)
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print(
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f"Success! Generated {len(final_conversations)} image-only conversational VQA entries."
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)
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print(f"Formatted data saved to: {output_path}")
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# --- Main Execution Block ---
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if __name__ == "__main__":
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# Define file paths
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IMAGE_ROOT = "/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_1"
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LABELS_FILE = os.path.join(IMAGE_ROOT, "label_data.json")
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SYSTEM_PROMPT_FILE = os.path.join(IMAGE_ROOT, "system_prompt.txt")
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UNSTRUCTURED_PROMPT_FILE = "/home/nguyendc/phong-dev/distillation/easydistill/mmkd/dev-vqa/qa_bank/unstructured_prompt.txt"
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QUESTION_BANK_FILE = "/home/nguyendc/phong-dev/distill/prompt/question_bank.json"
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ANSWER_BANK_FILE = "/home/nguyendc/phong-dev/distill/prompt/answer_bank.json"
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OUTPUT_FILE = "/home/nguyendc/phong-dev/distill/vqa_label.json"
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QUESTION_RATIO = 0.4
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# Run the main generation function
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generate_vqa_conversations(
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LABELS_FILE,
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IMAGE_ROOT,
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UNSTRUCTURED_PROMPT_FILE,
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QUESTION_BANK_FILE,
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ANSWER_BANK_FILE,
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OUTPUT_FILE,
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QUESTION_RATIO,
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)
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# generate_vq_question(
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# IMAGE_ROOT,
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# UNSTRUCTURED_PROMPT_FILE,
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# QUESTION_BANK_FILE,
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# OUTPUT_FILE,
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# QUESTION_RATIO,
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# )
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