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| from sparrow_parse.extractor.unstructured_processor import UnstructuredProcessor | |
| from sparrow_parse.extractor.markdown_processor import MarkdownProcessor | |
| import json | |
| def execute_sparrow_processor(options, file_path, strategy, model_name, local, debug): | |
| content, table_content = None, None | |
| if "unstructured" in options: | |
| processor = UnstructuredProcessor() | |
| content, table_content = processor.extract_data(file_path, strategy, model_name, | |
| ['tables', 'unstructured'], local, debug) | |
| elif "markdown" in options: | |
| processor = MarkdownProcessor() | |
| content, table_content = processor.extract_data(file_path, ['tables', 'markdown'], local, debug) | |
| return content, table_content | |
| def merge_dicts(json_str1, json_str2): | |
| # Convert JSON strings to dictionaries | |
| dict1 = json.loads(json_str1) | |
| dict2 = json.loads(json_str2) | |
| merged_dict = dict1.copy() | |
| for key, value in dict2.items(): | |
| if key in merged_dict and isinstance(merged_dict[key], list) and isinstance(value, list): | |
| merged_dict[key].extend(value) | |
| else: | |
| merged_dict[key] = value | |
| return merged_dict | |
| def track_query_output(keys, json_data, types): | |
| # Convert JSON string to dictionary | |
| data = json.loads(json_data) | |
| # Initialize the result lists | |
| result = [] | |
| result_types = [] | |
| # Iterate through each key in the keys array | |
| for i, key in enumerate(keys): | |
| # Check if the key is present in the JSON and has a non-empty value | |
| if key not in data or not data[key].strip(): | |
| result.append(key) | |
| result_types.append(types[i]) | |
| return result, result_types | |
| def add_answer_page(answer, page_name, answer_page): | |
| if not isinstance(answer, dict): | |
| raise ValueError("The answer should be a dictionary.") | |
| # Parse answer_table if it is a JSON string | |
| if isinstance(answer_page, str): | |
| answer_page = json.loads(answer_page) | |
| answer[page_name] = answer_page | |
| return answer | |