| import json |
| import os |
| import pandas as pd |
| import getpass |
|
|
| from PIL import Image |
| import io |
| import base64 |
| from datetime import datetime |
|
|
| import use_vlm_ft_OpenAI |
|
|
|
|
| def get_img_base64_str(image_path): |
| img = Image.open(image_path) |
| buffered = io.BytesIO() |
| img.save(buffered, format=img.format) |
| img_base64_str = base64.b64encode(buffered.getvalue()).decode("utf-8") |
| return img_base64_str |
|
|
| if __name__ == "__main__": |
| api_key = getpass.getpass("Enter your OpenAI API key: ") |
|
|
| in_path_frame_images = './.../rpp-765k_512' |
| in_file_data_entering_test = './.../test.parquet' |
| |
| in_model_name = "OpenAI_FT_gpt-4o-2024-08-06" |
| in_name_results_output = 'OpenAI_FT_gpt-4o-2024-08-06' |
| out_path_results = './.../output' |
|
|
| path_outputs = os.path.join(out_path_results, in_model_name) |
| os.makedirs(path_outputs, exist_ok=True) |
|
|
| os.environ["USED_MODEL"] = "gpt-4o-2024-08-06" |
|
|
| dict_log = {} |
| dict_log['model'] = in_model_name |
| |
| ft_model = "id-of-fine-tuned-model" |
| |
| |
| df_result = pd.DataFrame( |
| columns=['label', 'filename', \ |
| 'brand', 'product_category', 'price', 'regular_price', 'relative_discount', 'absolute_discount', 'GTINs', 'weight_number', 'weight_unit', 'different_types']) |
| df_result_cost = pd.DataFrame( |
| columns=['label', 'filename'] |
| ) |
|
|
| df_test = pd.read_parquet(in_file_data_entering_test, engine='pyarrow') |
| df_test.reset_index(drop=True, inplace=True) |
|
|
| start_index = 0 |
| output_file = f'{datetime.now().strftime("%Y%m%d_%H%M%S")}_{in_name_results_output}' |
| for index, row in df_test.iloc[start_index:].iterrows(): |
| label = str(row.label) |
| filename = row.filename |
|
|
| dict_result = {} |
| dict_result['label'] = label |
| dict_result['filename'] = filename |
| dict_result_cost = {} |
| dict_result_cost['label'] = label |
| dict_result_cost['filename'] = filename |
| |
| |
| |
| |
| |
| |
| image_path = os.path.join( in_path_frame_images, 'train', label, filename ) |
| query_image_base64 = get_img_base64_str(image_path) |
|
|
| |
| task = "Extract all targets." |
| dict_log['prompt_task'] = task |
|
|
| dict_log, dict_result, dict_result_cost = use_vlm_ft_OpenAI.do_request( |
| api_key, |
| ft_model, |
| query_image_base64, |
| task, |
| dict_log, |
| dict_result, |
| dict_result_cost, |
| ) |
| |
| df_result = pd.concat( [ df_result, pd.DataFrame.from_dict([dict_result]) ], ignore_index=True) |
| df_result_cost = pd.concat( [ df_result_cost, pd.DataFrame.from_dict([dict_result_cost]) ], ignore_index=True) |
|
|
| if index%100 == 0: |
| df_result.to_parquet( os.path.join(path_outputs, output_file + '_' + str(index) + '.parquet'), index=False, engine='pyarrow') |
| df_result_cost.to_parquet( os.path.join(path_outputs, output_file + '_costs_' + str(index) + '.parquet'), index=False, engine='pyarrow') |
| df_result = pd.DataFrame( columns=['label', 'filename', \ |
| 'brand', 'product_category', 'price', 'regular_price', 'relative_discount', 'absolute_discount', 'GTINs', 'weight_number', 'weight_unit', 'different_types']) |
| df_result_cost = pd.DataFrame(columns=['label', 'filename']) |
| |
| df_result.to_parquet( os.path.join(path_outputs, output_file + '_' + str(index) + '.parquet'), index=False, engine='pyarrow') |
| df_result_cost.to_parquet( os.path.join(path_outputs, output_file + '_costs_' + str(index) + '.parquet'), index=False, engine='pyarrow') |
|
|
| |
| |
| |
| with open(os.path.join(path_outputs, in_name_results_output + '.json'), 'w') as json_file: |
| json.dump(dict_log, json_file) |
